Provide an example of the application of nursing informatics in either education, practice, or research and explain how the data transforms using the DIKW model.
Use of DIKW in NI Applications
The Clinical Decision Support System (CDSS) exemplifies the application of nursing informatics in practice, showcasing its role in improving patient care. As described in the article, CDSS utilizes data from electronic health records (EHRs) to enhance clinical decision-making by processing extensive patient information, such as lab results and medication histories (Adelphi University, 2024). This system assists healthcare providers by converting raw data into actionable insights, thereby facilitating more accurate diagnoses and treatment plans.
Utilizing the DIKW model, CDSS first converts raw data into structured information. For instance, it aggregates individual lab results and medication details to highlight critical values or abnormal patterns. This process helps in transforming isolated data points into coherent information that reflects the patient's current health status and potential risks (Adelphi University, 2024).
The system further employs this structured information to generate knowledge through advanced analysis, such as identifying possible drug interactions or warning signs of complications. This knowledge is then used to provide actionable wisdom in the form of alerts and recommendations. By offering timely, evidence-based guidance, CDSS enables healthcare professionals to make informed decisions, ultimately enhancing patient safety and care quality (Adelphi University, 2024).
Reference: Adelphi University Online. (2024, August 21). Benefits of Informatics in Nursing. https://online.adelphi.edu/articles/benefits-of-informatics-in-nursing/
The Clinical Decision Support System (CDSS) exemplifies the application of nursing informatics in practice, showcasing its role in improving patient care. As described in the article, CDSS utilizes data from electronic health records (EHRs) to enhance clinical decision-making by processing extensive patient information, such as lab results and medication histories (Adelphi University, 2024). This system assists healthcare providers by converting raw data into actionable insights, thereby facilitating more accurate diagnoses and treatment plans.
Utilizing the DIKW model, CDSS first converts raw data into structured information. For instance, it aggregates individual lab results and medication details to highlight critical values or abnormal patterns. This process helps in transforming isolated data points into coherent information that reflects the patient's current health status and potential risks (Adelphi University, 2024).
The system further employs this structured information to generate knowledge through advanced analysis, such as identifying possible drug interactions or warning signs of complications. This knowledge is then used to provide actionable wisdom in the form of alerts and recommendations. By offering timely, evidence-based guidance, CDSS enables healthcare professionals to make informed decisions, ultimately enhancing patient safety and care quality (Adelphi University, 2024).
Reference: Adelphi University Online. (2024, August 21). Benefits of Informatics in Nursing. https://online.adelphi.edu/articles/benefits-of-informatics-in-nursing/
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Nursing informatics can be applied in nursing practice through the following example: A PGH nurse on her night shift is checking a patient’s database through the RADISH to assess their medical history and current status in order to create proper nursing interventions. The data in this scenario would be the patient’s vital signs, laboratory results, medical history, medications, and doctor’s notes. Through analysis of the data, the nurse can extract relevant information from the RADISH which can be the patient’s episodes of bradypnea during the morning shift. Using the patient’s clinical knowledge to interpret this information, she is able to make an appropriate nursing intervention ideal to the patient’s condition. The nurse develops wisdom through repeated exposure to similar cases and can soon anticipate the needs of the patient and the potential consequences if they are unmet. Even in unique and complex clinical situations, the nurse is able to apply their knowledge through evidence-based judgment which allows for an effective patient care.
Reference:
Kaminski, J. (2022, February 10). Theory applied to informatics: DIKW Theory | NILC Blog. Nursing-Informatics.com. https://nursing-informatics.com/blog/theory-applied-to-informatics-dikw-theory/
Reference:
Kaminski, J. (2022, February 10). Theory applied to informatics: DIKW Theory | NILC Blog. Nursing-Informatics.com. https://nursing-informatics.com/blog/theory-applied-to-informatics-dikw-theory/
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The example you provided is excellent, Meliza! It is very relevant, especially since we also have experience using RADISH during our clinical duty at PGH. You clearly demonstrated the DIKW process in using RADISH, which is one of the ways PGH promotes the use of nursing informatics.
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Your example of a PGH nurse using RADISH to assess patient data illustrates the practical application of nursing informatics effectively. By analyzing vital signs, lab results, and clinical notes, the nurse can identify critical patterns such as episodes of bradypnea. The integration of clinical knowledge with informatics tools not only supports evidence-based practice but also fosters more proactive and effective patient care. Thank you, Meliza!
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An example of an application of nursing informatics in research incorporating the DIKW model:
A researcher conducts a study to identify hospital readmission predictors in heart failure patients. The researcher uses a machine learning algorithm to analyze a large electronic health records (EHR) dataset from a hospital's database.
Data: The EHR database contains raw data on patient demographics, medical history, medications, lab results, and other variables. For example, the dataset includes 10,000 patient records with variables such as age, gender, diagnosis, medication lists, and lab results.
Information: The machine learning algorithm analyzes the data and identifies patterns and relationships between variables. From the provided example, the algorithm determines that patients with a history of hypertension are more likely to be readmitted to the hospital within 30 days.
Knowledge: The researcher interprets the machine learning algorithm's results and identifies the predictors of hospital readmission. Based on the example, the researcher concludes that a combination of hypertension and a history of previous hospitalizations are strong predictors of readmission.
Wisdom: The researcher uses their wisdom to consider the implications of the findings and develop recommendations for clinical practice. In the given example, the researcher suggests that healthcare providers should closely monitor patients with these risk factors and develop targeted interventions to reduce the risk of readmission.
A researcher conducts a study to identify hospital readmission predictors in heart failure patients. The researcher uses a machine learning algorithm to analyze a large electronic health records (EHR) dataset from a hospital's database.
Data: The EHR database contains raw data on patient demographics, medical history, medications, lab results, and other variables. For example, the dataset includes 10,000 patient records with variables such as age, gender, diagnosis, medication lists, and lab results.
Information: The machine learning algorithm analyzes the data and identifies patterns and relationships between variables. From the provided example, the algorithm determines that patients with a history of hypertension are more likely to be readmitted to the hospital within 30 days.
Knowledge: The researcher interprets the machine learning algorithm's results and identifies the predictors of hospital readmission. Based on the example, the researcher concludes that a combination of hypertension and a history of previous hospitalizations are strong predictors of readmission.
Wisdom: The researcher uses their wisdom to consider the implications of the findings and develop recommendations for clinical practice. In the given example, the researcher suggests that healthcare providers should closely monitor patients with these risk factors and develop targeted interventions to reduce the risk of readmission.
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I really appreciate how the DIKW model was used in explaining the correlation of all data and information of a patient into making an intervention using it. Seeing this, it really does show how this model can help us in making clinical decisions to care for our patients.
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The Data-Information-Knowledge-Wisdom (DIKW) model is important in clinical decision-making. Data is an observation in order to build information that can result in knowledge that can make interpretations in order to have wisdom to make clinical judgment.
For instance, a student nurse made an initial assessment on a patient and one of the significant findings was that her BP reading reads 135/88 mmHg and she also mentioned having an unhealthy diet and physical inactivity (DATA). She was then told to keep a BP diary and came back on another day with a reading of 136/85 mmHg. Her BP diary also shows systolic blood pressure ranging from 130 to 139 mmHg and diastolic blood pressure ranging from 80 to 89 mmHg (INFORMATION). This now depicts that the patient might have stage 1 hypertension and was also confirmed by her doctor's diagnosis. This now makes the student nurse interpret the relation of risk factors to the patient's diagnosis such as unhealthy diet and physical inactivity (INFORMATION). The student nurse will now start to establish a nursing care plan and interventions to care for a patient with hypertension. The patient will be needing to do exercises and adhere to dietary regimen like DASH diet to manage her hypertension. Regular follow up check ups will also be encouraged and adherence to medication will also be ensured (WISDOM).
Reference
Ang, R. J. (2019). Use of content management systems to address nursing workflow. International Journal of Nursing Sciences, 6(4), 454-459. doi.org/10.1016/j.ijnss.2019.09.012
For instance, a student nurse made an initial assessment on a patient and one of the significant findings was that her BP reading reads 135/88 mmHg and she also mentioned having an unhealthy diet and physical inactivity (DATA). She was then told to keep a BP diary and came back on another day with a reading of 136/85 mmHg. Her BP diary also shows systolic blood pressure ranging from 130 to 139 mmHg and diastolic blood pressure ranging from 80 to 89 mmHg (INFORMATION). This now depicts that the patient might have stage 1 hypertension and was also confirmed by her doctor's diagnosis. This now makes the student nurse interpret the relation of risk factors to the patient's diagnosis such as unhealthy diet and physical inactivity (INFORMATION). The student nurse will now start to establish a nursing care plan and interventions to care for a patient with hypertension. The patient will be needing to do exercises and adhere to dietary regimen like DASH diet to manage her hypertension. Regular follow up check ups will also be encouraged and adherence to medication will also be ensured (WISDOM).
Reference
Ang, R. J. (2019). Use of content management systems to address nursing workflow. International Journal of Nursing Sciences, 6(4), 454-459. doi.org/10.1016/j.ijnss.2019.09.012
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I appreciate that you used an example that is very relatable to us. The DIKW truly ilustrates the process of how data, as simple as the vital signs of our patients, transforms into wisdom that will help us not only in caring for our current patients but also for future ones.
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Hi Monii, I really appreciated how you connected a familiar clinical experience to the DIKW model. It’s impressive how this model can guide us in making the best decisions for our patients. Utilizing it effectively will undoubtedly enhance our decision-making and improve patient care.
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The DIKW model assists nurses and other professionals in articulating how we use data, information, and knowledge in our practice, as well as how they might promote the development and application of wisdom in healthcare (Kaminski, 2022). In the context of the DIKW model, nursing informatics facilitates a more efficient transformation from data to actionable insights in healthcare workflow management. Taking the OPD Ob-Gyne as an example that I previously mentioned:
Data: Nurses in the OPD Ob-Gyne department enter patient data into the Electronic Health Record (EHR) using a Google Spreadsheet.
Information: The Google Spreadsheet organizes and structures the data, making it easy for healthcare professionals to access and review.
Knowledge: The system can highlight irregularities in patients' vital signs or assessments, prompting the healthcare team to take necessary action.
Wisdom: Nurses and doctors use their experience and system insights to make informed decisions, improving patient care and workflow efficiency.
Reference:
Kaminski, J. (2022, February 10). NILC blog. https://nursing-informatics.com/blog/theory-applied-to-informatics-dikw-theory/#:~:text=The%20DIKW%20theory%20is%20just,within%20the%20context%20of%20healthcare.
Data: Nurses in the OPD Ob-Gyne department enter patient data into the Electronic Health Record (EHR) using a Google Spreadsheet.
Information: The Google Spreadsheet organizes and structures the data, making it easy for healthcare professionals to access and review.
Knowledge: The system can highlight irregularities in patients' vital signs or assessments, prompting the healthcare team to take necessary action.
Wisdom: Nurses and doctors use their experience and system insights to make informed decisions, improving patient care and workflow efficiency.
Reference:
Kaminski, J. (2022, February 10). NILC blog. https://nursing-informatics.com/blog/theory-applied-to-informatics-dikw-theory/#:~:text=The%20DIKW%20theory%20is%20just,within%20the%20context%20of%20healthcare.
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Thank you Aira for the constructive answer! I also had similar observations when I dutied in the OPD-Ob Gyne department. The google spreadsheet was developed by the head nurse. She told us that there were initial difficulty in using it but it made their workflow easier because it allowed them to view which patients are new, or a previous patient. It also allowed them to view the patients' AOG which allowed us to select patients that satisfied the criteria given to us in choosing a client.
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As a nurse in PGH, it is our responsibility to monitor and manage the patient’s fluid intake. Here’s how we can apply the DIKW model to this task:
Data:
Nurse records raw data on the patient’s fluid intake such as the water or juice intake and IV fluids administered. For example, the patient might have consumed 237 mL of juice and received 1 liter of 0.9% PNSS.
Information:
This data is entered into the RADISH system by the nurse or doctor in charge, where it is organized and contextualized. We can then see the comparison of the current day's data with previous records. For instance, the total fluid intake for the day is recorded as 1.75 liters, which is compared against the patient’s historical fluid intake data.
Knowledge: The nurse analyzes the organized information to understand implications for the patient and knows what to watch out for. By evaluating the total fluid intake in relation to the recommended goal, the nurse can determine if the patient’s hydration needs are being met. In this case, the recommended intake is 2 liters, so the analysis reveals that the patient is currently 250 mL short of the target.
Wisdom: With this knowledge, the nurse makes informed decisions and takes appropriate actions. For this instance, we recognize that the patient is 250 ml short of the recommended fluid intake, with that we can decide to encourage the patient to drink more fluids to achieve the 2 liters goal. This approach ensures that the patient's hydration needs are adequately addressed, promoting optimal care.
Data:
Nurse records raw data on the patient’s fluid intake such as the water or juice intake and IV fluids administered. For example, the patient might have consumed 237 mL of juice and received 1 liter of 0.9% PNSS.
Information:
This data is entered into the RADISH system by the nurse or doctor in charge, where it is organized and contextualized. We can then see the comparison of the current day's data with previous records. For instance, the total fluid intake for the day is recorded as 1.75 liters, which is compared against the patient’s historical fluid intake data.
Knowledge: The nurse analyzes the organized information to understand implications for the patient and knows what to watch out for. By evaluating the total fluid intake in relation to the recommended goal, the nurse can determine if the patient’s hydration needs are being met. In this case, the recommended intake is 2 liters, so the analysis reveals that the patient is currently 250 mL short of the target.
Wisdom: With this knowledge, the nurse makes informed decisions and takes appropriate actions. For this instance, we recognize that the patient is 250 ml short of the recommended fluid intake, with that we can decide to encourage the patient to drink more fluids to achieve the 2 liters goal. This approach ensures that the patient's hydration needs are adequately addressed, promoting optimal care.
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I like how you provided an example that we can relate. This proves how a simple data that we have collected from patients can turn into a long-term application. In this case, from a simple consumption of juice with an additional IVF, we can create interventions that will help our patients. Thank you for this example!
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Nursing informatics in education and using the DIKW model:
Students are getting ready for their OBAS rotation. As such, their clinical instructors will utilize a simulation-based learning wherein they will go to Lucina and experience the process of giving birth.
Data: The clinical instructor can collect data such as the time taken by students to complete a task. They can also collect the correct and missing interventions done by students. For example, if they did the Ritgen's maneuver correctly or not.
Information: From these data, they can organize them such as getting the average time for students to complete the task. Moreover, they can also see the common mistakes done by students during the said simulation.
Knowledge: In interpreting them, clinical instructors can identify areas where students can improve on to minimize errors and improve their efficiency.
Wisdom: With the interpretation, clinical instructors have now the ability to apply the knowledge. For example, they can develop new scenarios that will target student’s weaknesses.
Students are getting ready for their OBAS rotation. As such, their clinical instructors will utilize a simulation-based learning wherein they will go to Lucina and experience the process of giving birth.
Data: The clinical instructor can collect data such as the time taken by students to complete a task. They can also collect the correct and missing interventions done by students. For example, if they did the Ritgen's maneuver correctly or not.
Information: From these data, they can organize them such as getting the average time for students to complete the task. Moreover, they can also see the common mistakes done by students during the said simulation.
Knowledge: In interpreting them, clinical instructors can identify areas where students can improve on to minimize errors and improve their efficiency.
Wisdom: With the interpretation, clinical instructors have now the ability to apply the knowledge. For example, they can develop new scenarios that will target student’s weaknesses.
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I appreciate how you applied the DIKW Model in terms of nursing education. Indeed, the model is vital in terms of analyzing students' weaknesses and developing ways on how to address and/or correct these to prepare us for what we would experience in the actual clinical setting. Thank you for this example!
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Hi, John! Your answer is very concrete because we have experienced it during our previous courses. I would like to highlight how nursing informatics utilization could translate various facts into data, information, knowledge, and wisdom. As student nurses, I believe that we would need this framework in order to have the skill of forming knowledge and wisdom based on the context that data and information provide us. Moreover, your answer showcases how student nurses are being honed in our classroom through the use of modern technology in line with the principles of nursing informatics. I hope to experience this fun learning activities in our future endeavors as student nurses because they are a worthwhile experience that improve our clinical reasoning, critical thinking, and practical skills. That's all!
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An example of nursing informatics in nursing research is the use of data analytics to study patient outcomes and healthcare trends.
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Researchers collect raw data from sources such as electronic health records (EHRs) and patient surveys. This data might include patient demographics, treatment protocols, and disease incidence rates. For example, data on reasons why patients are lost to follow-up in their HIV treatment hubs is collected.
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This raw data is then organized and analyzed to identify patterns. For instance, researchers might sort the data by themes, revealing that patients under 18 and those who are employed tend to miss their follow-up appointments due to the treatment hub’s distance from their home or operating hours that conflict with their schedules. This step converts the data into information that reveals trends in patient care.
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Researchers synthesize this information with clinical guidelines and previous studies to generate knowledge. For example, they might conclude that scheduling flexibility and geographic access are critical barriers to consistent HIV treatment adherence for these populations. This knowledge provides insights into why certain patient populations experience different outcomes.
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Finally, wisdom is applied when researchers use this knowledge to recommend changes in clinical practice or health policy. For example, they might suggest implementing extended or flexible clinic hours or expanding telehealth options to improve follow-up rates. This wisdom can lead to the development of evidence-based protocols, which can be tested in further studies or adopted by healthcare organizations.
References
Nelson, R. (2020, July 21). Informatics: Evolution of the Nelson Data, Information, Knowledge and Wisdom Model: Part 2 | OJIN: The Online Journal of Issues in Nursing. The Online Journal of Issues in Nursing. Retrieved September 15, 2024, from https://ojin.nursingworld.org/table-of-contents/volume-25-2020/number-3-september-2020/evolution-of-nelson-model-part-2/
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I applaud what you have shared Josh! From your discussion, I have understood the process of how patients avail HIV treatments and the underlying environmental factors that affect the health-seeking behavior of clients. I agree with the Wisdom part of your discussion in which the formulation of the knowledge can be tested through its application to create more meaningful care for clients. Meaningful in the context of your discussion as evidence-based protocols
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Nursing informatics has a wide scope of use in education, practice, and research. An application of it can be seen in Information management systems that hospitals use. In the local setting, the Computerized Registry of Admissions and Discharges or RADISH of the Philippine General Hospital can be an example. Applying the DIKW model, we can view the primary results that the nurses input in management systems such as lab values as data which is still unprocessed or interpreted. Next, for example, the data is about potassium lab values and the management system interprets whether it is normal, high, or low then the systems interpretation can be considered as information or data which has been interpreted but still has no connections with other information. Then, the next stage of the DIKW model is Knowledge and Wisdom. These stages of the DIKW model emphasizes that information systems do not control the nursing practice but it is the other way around because in order to form knowledge, nursing expertise is required which cannot be programmed in the computer unlike the interpretation of whether values are high or low. For knowledge, the nurse creates connections between information such as suspecting that the patient may need to be screened for renal failure due to the high serum potassium levels which may indicate that the kidney cannot secrete the potassium it usually does. Lastly, wisdom allows the nurse to decide the best action to do based on their knowledge.
REFERENCE:
Informatics: Evolution of the Nelson Data, Information, Knowledge and Wisdom Model: Part 2 | OJIN: The Online Journal of Issues in Nursing. (n.d.). https://ojin.nursingworld.org/table-of-contents/volume-25-2020/number-3-september-2020/evolution-of-nelson-model-part-2/
REFERENCE:
Informatics: Evolution of the Nelson Data, Information, Knowledge and Wisdom Model: Part 2 | OJIN: The Online Journal of Issues in Nursing. (n.d.). https://ojin.nursingworld.org/table-of-contents/volume-25-2020/number-3-september-2020/evolution-of-nelson-model-part-2/
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RADISH is the most predominantly known tool for acknowledging patient information and analyzing and applying the necessary intervention. This platform allows me to further understand the patient's condition, particularly its pathophysiology, and identify what actions (within my scope) I may contribute to enhance their well-being.
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This is true especially when we go on duties in the hospital, especially in PGH where everything is connected and every part of the healthcare team responsible for the care of the patient can access the patient's data conveniently. This allows the healthcare team to be efficient in collecting and interpreting data, all the way to providing patient care. Great insights Allen!
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Nursing informatics can be applied in practice in terms of layouting patient care, depending on the information stored in a database. An example in a study by Ang (2019) is recording patient assessment data, such as blood pressure, heart rate, and temperature. The DIKW model is used in this scenario through the documentation of the specific assessment data, whether there are deviations from normal values, and can also be combined with data about the patient’s medication and dosage. With this data, information and knowledge can be formed through conducting a drug study on the indications of the drug and its side effects, fostering analysis of the response of the patient. Eventually, wisdom is developed and specific medical and nursing management can be performed to address the individualized needs of the patient. In terms of nursing care, the nurse also applies wisdom by evaluating the patient’s human response by comparing the effect of nursing interventions from baseline data and taking into consideration what could be done to maintain stable vital signs and prevent complications from arising.
Reference:
Ang, R. J. (2019). Use of content management systems to address nursing workflow. International Journal of Nursing Sciences, 6(4). https://doi.org/10.1016/j.ijnss.2019.09.012
Reference:
Ang, R. J. (2019). Use of content management systems to address nursing workflow. International Journal of Nursing Sciences, 6(4). https://doi.org/10.1016/j.ijnss.2019.09.012
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I really like your insights, Aira! The example you provided not only showed the essence of the DIKW model precisely, but it also can be understood easily as it is relatable to our clinical duty experience, specifically regarding the patient's medications. Well done!
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This is a very good example, Aira! We have been taught that vital signs are essential and are actually a precursor for the patient's health state. A deviation from what is considered as normal is something that we, nurses, have to be alarmed of and address immediately with the proper intervention and care!
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Well said, Aira! Thank you for sharing us your evidence-based example. I am excited to encounter your example and be able to use the DIKW model!
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The DIKW model provides a framework which allows us to build upon the data that we gather and add value to it. In a scenario where the nurse gathers information from her pregnant patient that it is her fifth pregnancy. This data alerts the nurse since she knows that multigravida women have riskier pregnancies. Based on the nurse's knowledge, she should watch out for various complications such as placenta previa. With her wisdom she is able to watch out for danger signs and teach this to her patient.
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This is a great example, Jerome! You were able to incorporate the lessons from the N105 course in applying it to the DIKW model. This makes it easier for us to understand what the model is all about.
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Indeed, utilizing the DIKW model enhances patient care by guiding the nurse from data collection to critical thinking and clinical reasoning!
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One of the ideal and amazing things that UP PGH-OPD did is to integrate the nursing practice into the online world. With the use of the Online Consultation Request and Appointment (OCRA) System, people can adapt to the changes. It allows patients to schedule their appointments online, reducing in-person visits and waiting for long lines. As supported by the Change Theory System, healthcare workers tried to find a method that works best and proves to be counterproductive. The application of the DIKW Model with the OCRA system is as follows:
Data: People are asked for their demographics, medical history, and chief complaint upon entering the OCRA system.
Information: After submitting, demographics and medical histories were organized; the appointment schedule was then organized and processed depending on the emergency, availability, and slot.
Knowledge: With the patients’ data, the healthcare team would be able to find common complaints from the patients. This helps them gain insights into the trends regarding the diseases that most people seek care about.
Wisdom: The healthcare providers can now make informed decisions about their service delivery days before the appointment, as they know which complaints are to be prioritized.
Though there are encountered problems with the system, such as its unfamiliarity with using technology most especially for older people and the appointed schedule takes months of waiting before a checkup, the OCRA system continuously improves as this is an innovative way to deliver healthcare systems through digitization.
Data: People are asked for their demographics, medical history, and chief complaint upon entering the OCRA system.
Information: After submitting, demographics and medical histories were organized; the appointment schedule was then organized and processed depending on the emergency, availability, and slot.
Knowledge: With the patients’ data, the healthcare team would be able to find common complaints from the patients. This helps them gain insights into the trends regarding the diseases that most people seek care about.
Wisdom: The healthcare providers can now make informed decisions about their service delivery days before the appointment, as they know which complaints are to be prioritized.
Though there are encountered problems with the system, such as its unfamiliarity with using technology most especially for older people and the appointed schedule takes months of waiting before a checkup, the OCRA system continuously improves as this is an innovative way to deliver healthcare systems through digitization.
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The OCRA system really does streamline the process of keeping patient info accessible while maintaining patient privacy. Thank you for this Erika!
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I completely agree, Erika! The OCRA system exemplifies how integrating technology with the DIKW model enhances healthcare delivery by streamlining appointment scheduling, improving data organization, and enabling informed decision-making.
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Nursing informatics in research and the DIKW model can be explained with the example below:
A group of nursing students wanted to find out the prevalence of heart diseases in a specific Barangay as part of their community fieldwork.
Data: The students were able to gather the numbers for old and new cases of heart disease as well as the population in the barangay.
Information: The values were input in their spreadsheets to compute for the prevalence rate.
Knowledge: They interpret the rate that was computed in the spreadsheet. They conclude that for every 1000 people in the barangay, there are n people with heart disease.
Wisdom: The students then apply wisdom to identify and implement appropriate interventions.
References:
Kaminski, J. (2022, February 10). Theory applied to informatics: DIKW Theory | NILC Blog. https://nursing-informatics.com/blog/theory-applied-to-informatics-dikw-theory/
Nelson, R. (2020). Informatics: Evolution of the Nelson Data, Information, Knowledge and Wisdom Model: Part 2. OJIN: The Online Journal of Issues in Nursing, 25(3). https://doi.org/10.3912/OJIN.Vol25No03InfoCol01
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We had the same sample scenario and I would say that your analysis of it is short yet concise and deliberate. I also considered a scenario that applied to the community health settings because I know that the nursing practice, nursing informatics included, would be of great help and use in addressing the needs of the people in that certain community. I am just delighted that we had the same perspective and goal, Mika! Hopefully, the others had the same or would recognize it as a field to apply nursing informatics as well.
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Hi, Mika! I completely agree with how this example illustrates the DIKW model in nursing informatics. As a nursing student, gathering data like heart disease cases and then using that information to calculate prevalence helps us make sense of the numbers. By interpreting the data, we gain knowledge, and it's this knowledge that guides us in making informed decisions. The end goal is applying wisdom to create meaningful interventions, ensuring we provide better care and contribute to the community's well-being. This model really shows how informatics connects all the steps, from data collection to patient care.
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In our university, the most widely used LMS are Canvas and Moodle. These apps are mainly used to deliver course content and track student progress, and with the application of the DIKW model, professors and students can know how to improve their performance. For my example, I will focus on the professors.
Data: Lectures, discussion forums, announcements, quizzes, assignments, and grades are uploaded to the LMS.
Information: The data are organized, tracking students’ progress over time. Professors can see who has not viewed the materials, has late/incomplete submissions, has failing grades, etc.
Knowledge: Knowledge is obtained about students’ performance patterns. The professors can see which topic/s are the most difficult for students, who are consistently delivering poor grades, and get insights about students’ study habits (e.g. professors may see that many students view materials only a day or so before an exam).
Wisdom: After getting insights/knowledge, the professors can make adjustments to improve the class’ performance. They may provide more educational materials, communicate with those who are struggling, and teach students about how to study efficiently.
Reference:
Marson, L. (2023, August 30). 6 benefits of a learning management system. TechTarget. https://www.techtarget.com/searchHRSoftware/tip/Benefits-of-a-learning-management-system
Data: Lectures, discussion forums, announcements, quizzes, assignments, and grades are uploaded to the LMS.
Information: The data are organized, tracking students’ progress over time. Professors can see who has not viewed the materials, has late/incomplete submissions, has failing grades, etc.
Knowledge: Knowledge is obtained about students’ performance patterns. The professors can see which topic/s are the most difficult for students, who are consistently delivering poor grades, and get insights about students’ study habits (e.g. professors may see that many students view materials only a day or so before an exam).
Wisdom: After getting insights/knowledge, the professors can make adjustments to improve the class’ performance. They may provide more educational materials, communicate with those who are struggling, and teach students about how to study efficiently.
Reference:
Marson, L. (2023, August 30). 6 benefits of a learning management system. TechTarget. https://www.techtarget.com/searchHRSoftware/tip/Benefits-of-a-learning-management-system
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This is a very good example, Carla! Canvas and Moodle have really been a great tracker of performance for both professors and students in determining their standing. With the professors as your focus in your example, the information you have provided in each component of the DIKW model is appropriate. All the more, it has been very clear how the platform aids in the educational process.
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This application of the DIKW model to LMS platforms like Canvas and Moodle is spot-on. By analyzing data such as lectures and grades, professors can identify track students' performance better.
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An example of the application of nursing informatics in practice is using electronic health records (EHR) to track and monitor patient data. Nurses can input data such as vital signs, medication administration, and patient history into the EHR system. This data is then transformed using the DIKW model as follows:
Data: The raw numbers and measurements entered into the EHR by the nurse.
Information: When the data is processed and interpreted, such as when vital signs are analyzed to determine the health needs of the patient.
Data: The raw numbers and measurements entered into the EHR by the nurse.
Information: When the data is processed and interpreted, such as when vital signs are analyzed to determine the health needs of the patient.
Knowledge: Through further analysis and interpretation, the information gathered from the EHR can contribute to the knowledge base of the healthcare team, helping to identify potential health issues, nursing interventions, and treatment plans.
Wisdom: Ultimately, the application of nursing informatics and the DIKW model can lead to wisdom in clinical decision-making, as healthcare providers use the transformed data to make informed decisions about patient care.
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An example of a nursing informatics application in nursing education, using the DIKW model, can be seen when a nurse cares for a perioperative patient:
- Data: The nurse collects data through a thorough health history and physical assessment. This may include previous surgeries, vital signs, and medication intake.
- Information: The nurse records this data in appropriate forms and platforms (e.g. EHR) to establish any differences from previous health records.
- Knowledge: The nurse analyzes the gathered information to gain insights into what the patient needs.
- Wisdom: The nurse, along with other healthcare workers, makes decisions and provides the patient with the necessary care and treatment based on the data. This also contributes to their experience and prepares them for similar future situations.
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Hi Bielle! This example perfectly illustrates how the DIKW model can be applied in perioperative care. By collecting and recording data, nurses can create a comprehensive picture of the patient's health. Analyzing this information helps in understanding specific needs, while the application of this knowledge and wisdom ensures tailored, effective care. It’s a clear demonstration of how informed decision-making can significantly improve patient outcomes and enhance nursing practice.
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Nursing informatics in nursing practice and the use of the DIKW model.
A group of nursing students wants to perform a risk assessment and analysis on a certain barangay where they are assigned to conduct their community diagnosis field work.
DATA: The input for their risk assessment includes their observations, interview responses from the people they gave mingled in the area, review of documents and papers in the barangay, as well as numerical data on demographics, vital and health statistics, and economic status of all of the members of the community.
INFORMATION: The risk description and definition that is derived from the data the students have collected and collated from the on-the-ground field work. This can all be organized in data management systems and applications (i.e. Google Docs, Google Sheets, etc.)
KNOWLEDGE: The group now analyzes and understands the risk assessment and description they have collated to be able to come up with the best control activities to address their identified risk/s. For example, they have identified a high portion of the community’s population to be at risk for NCDs. They now synthesize this data and knowledge to formulate hypotheses and plans to address the present concern.
WISDOM: Now that the group has identified, analyzed, and planned their modes of action, it is then that they formally act on them by means of nursing interventions that are grounded on their nursing knowledge and theories learned in class. The use of theoretical and conceptual frameworks would enable them to carry out their interventions in the best possible way that would meet their goals and ultimately, the needs of their client/s.
Reference:
Kaminski, J. (2022, February 10). Theory applied to informatics: DIKW Theory | NILC Blog. https://nursing-informatics.com/blog/theory-applied-to-informatics-dikw-theory/
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In reply to Aldrix Gonzales
Re: Use of DIKW in NI Applications
Great example, Drix! Since we are also about to begin our community duty, your explanation of how the DIKW model is used in community diagnosis has helped me better appreciate its application. The way you stressed the significance of obtaining precise and thorough data from numerous sources—primary as well as secondary—and then turning it into actionable information (i.e., community-based interventions) is what I find most appealing.
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During our foundation years, the preparation to meet an actual patient has been arduous as we are not only backing ourselves up with theoretical knowledge, but we are also developing clinical skills that are appropriate for our level. I even remember Professor Evio telling us that we shouldn’t only be good with theoreticals or clinicals, but both. In this regard, the use of Bates’ Visual Guide has been a very effective tool in equipping us with competencies ready to meet the demands of our clients.
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Data: Demonstration of Physical Examination
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Information: Interpretation of Findings / Gold Standards
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Knowledge: Mastery of Skills
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Wisdom: Application in real-life settings
Reference:
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Kluwers, W. (n.d.). Bates’ Visual Guide to Physical Examination. https://www.wolterskluwer.com/en/solutions/lippincott-medicine/medical-education/bates-visual-guide-to-examination
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This is a prime example of how NI is being used in education. We all have experienced this since the start of our first semester, to now where we still use technological tools in learning nursing interventions. With more tools being created, education, not only in nursing, will greatly benefit from these. Good insight Jess!
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I can completely relate with your experience Jess! The balance between theoretical and clinical skills is challenging but with the help of these resources it helps us student nurses in becoming efficient in doing Physical Examination. By progressing from data to information, knowledge, and wisdom, we are preparing ourselves in providing the best possible care to our patients.
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The findings presented by Cato et al. (2020) can be viewed through the lens of the DIKW model. Here, the main element is the CONCERN Clinical Decision Support (CDS) system, which exemplifies nursing informatics by leveraging AI to predict patient deterioration. At its core, the system uses data from electronic health records (EHRs), such as nursing notes, medication records, and flow sheet entries. The information generated from this data involves AI algorithms identifying patterns that signal a patient's risk level for deterioration.
The system then converts this information into knowledge by predicting risk levels (green, yellow, red), where yellow indicates heightened risk but no visible deterioration yet. This assists clinicians in interpreting patient conditions earlier. However, the wisdom level is reached when healthcare teams utilize this knowledge in real-time decision-making, preventing adverse outcomes through early intervention.
Using the DIKW model, the data level is seen through the vital signs, nursing notes, medication administration, and flow sheet inputs automatically captured by the EHR. For information, AI processes this data, analyzing patterns of concern, and generating risk predictions. In knowledge, the system translates predictions into actionable risk levels (green, yellow, red), representing the likelihood of patient deterioration. Lastly, in wisdom, clinicians apply these insights to make proactive decisions, supporting interdisciplinary communication and timely interventions. For instance, CONCERN can alert clinicians up to 24 hours before clear signs of deterioration, facilitating earlier care and improving patient outcomes.
Thus, nursing informatics, through tools like the CONCERN CDS, transforms raw data into actionable insights, enhancing patient safety and care.
Reference:
The system then converts this information into knowledge by predicting risk levels (green, yellow, red), where yellow indicates heightened risk but no visible deterioration yet. This assists clinicians in interpreting patient conditions earlier. However, the wisdom level is reached when healthcare teams utilize this knowledge in real-time decision-making, preventing adverse outcomes through early intervention.
Using the DIKW model, the data level is seen through the vital signs, nursing notes, medication administration, and flow sheet inputs automatically captured by the EHR. For information, AI processes this data, analyzing patterns of concern, and generating risk predictions. In knowledge, the system translates predictions into actionable risk levels (green, yellow, red), representing the likelihood of patient deterioration. Lastly, in wisdom, clinicians apply these insights to make proactive decisions, supporting interdisciplinary communication and timely interventions. For instance, CONCERN can alert clinicians up to 24 hours before clear signs of deterioration, facilitating earlier care and improving patient outcomes.
Thus, nursing informatics, through tools like the CONCERN CDS, transforms raw data into actionable insights, enhancing patient safety and care.
Reference:
- Cato, K. D., McGrow, K., & Rossetti, S. C. (2020). Transforming clinical data into wisdom: Artificial intelligence implications for nurse leaders. Nursing management, 51(11), 24-30.
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An example of application of nursing informatics in practice is telehealth systems. Telehealth systems illustrate the DIKW model in nursing informatics effectively. As detailed by Siddiqui and Perry (2024), these systems collect raw data from remote monitoring devices such as glucose meters and blood pressure cuffs. This raw data is then processed and organized to generate meaningful information, like tracking trends in blood glucose or blood pressure readings. The analysis of this information yields valuable knowledge about the patient’s health status, helping to identify patterns or issues. Healthcare providers use this knowledge to make informed decisions, such as adjusting treatment plans or recommending lifestyle changes, thereby improving patient outcomes and care.
Reference:
Siddiqui, M. R., & Perry, D. J. (2024). Telehealth: Transforming healthcare delivery. Heart & Lung: The Journal of Acute and Critical Care, 74, 19-26. https://doi.org/10.1016/j.hrtlng.2024.01.003
Reference:
Siddiqui, M. R., & Perry, D. J. (2024). Telehealth: Transforming healthcare delivery. Heart & Lung: The Journal of Acute and Critical Care, 74, 19-26. https://doi.org/10.1016/j.hrtlng.2024.01.003
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Very well said, ate Joyce! I appreciate how you applied the DIKW model in terms of telehealth systems.
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DIKW is widely evident in the nursing practice. For example, in Philippine General Hospital (PGH), nurses, doctors, and other health care providers, communicate patient information and necessary intervention through RADISH (a electronic medical record), particularly:
- Data: Nurses often provide the necessary patient data, such as their vital signs (e.g., T, RR, HR, BP, and pain), lab results (CBC, OGTT, and ECG), medication history and past medical history.
- Information: Nurses would organize and present these information in a structured manner, so that other healthcare providers assigned to the client may easily understand the data. They would often display these through a chronological format, highlighting the significant data and urgent intervention that must or was performed.
- Knowledge: Nurses subsequently utilize these information to evaluate the patient's status, detect any complications, and devise an appropriate treatment strategy. For example, if a patient's blood pressure is regularly high, the nurse may reference guidelines or clinical decision support systems to determine the most appropriate actions.
- Wisdom: Based on the nurse's knowledge and experience, s/he apply these information to make informed decisions concerning the patient's care. For instance, a nurse to adjust IV fluid rate or recommend a referral to a specialist based on their understanding of the patient's condition and treatment options.
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Very nice example, Clinen! RADISH has been proven to be invaluable to all medical workers in PGH. Through this application, giving patient care has been more streamlined and efficient since patient information can be easily accessed through the website.
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I am glad that we have the same understanding and mental model of the data transformation process. Using systems similar to RADISH certainly helps in communication and collaboration with others, allowing us to be efficient and effective in identifying appropriate interventions—from data to practice. Great example and analysis of the application of nursing informatics, Nen!
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An example of nursing informatics in the nursing practice is utilized in the triage system. With this, it allows for the prioritization of patients in receiving their health and medical needs.
Data: Patient information (health history and physical examination), especially their demographics, chief complaint, and current physical condition
Information: Gathered health information and assessment are recorded and organized in Electronic Health Records (EHRs), and interpreted according to their urgency and slot availability
Knowledge: Analysis of health data are done by the nurse and other health professionals to gain insights on patient needs, especially highlighting abnormalities in patients’ vital signs and assessments
Wisdom: Healthcare teams make informed decisions based on evidence-based practices, considering the prioritization of complaints and application of healthcare delivery.
121 words
In reply to Patricia Joy Antonette Ortiz
Re: Use of DIKW in NI Applications
That's an interesting application of nursing informatics! I can clearly visualize how it would help properly prioritize cases in the triage.
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One application of nursing informatics in nursing education is a website called Bates’ Visual Guide to Physical Examination. On this website, there are interactive elements and videos that students can use to enhance their learning about basic health assessment.
This application also utilizes the Data, Information, Knowledge, and Wisdom Model. First is data. These are the raw data which can include physical examination techniques, such as palpation, inspection, percussion, and auscultation, normal and abnormal findings, such as heart and lung sounds, and related anatomical concepts.
These vast data can be turned into information by organizing such data into a more structured format. In Bates’, they classified these into different organ systems, like cardiovascular, respiratory, gastrointestinal to make it easier for the students where to look or what chapter they have to go back to in case they have clarifications. These chapters aid in producing meaning from the given data.
From information, can be transformed into knowledge by recognizing what is normal and abnormal to see in a patient. For example, since we know the different lung sounds, we can now recognize it when we hear them from a patient. Additionally, we can now apply how to correctly percuss a patient’s abdomen when they are having gastrointestinal discomfort.
From this knowledge, we can transform it into wisdom. By this transformation to wisdom, we can now make informed decisions about patient care. For example, if we recognize the different abnormal lung sounds, we can now identify patients who are at risk for certain conditions, such as pneumonia. We use this information to guide us on what kind of care does the patient needs.
Reference:
Informatics: Evolution of the Nelson Data, Information, Knowledge and Wisdom Model: Part 2 | OJIN: The Online Journal of Issues in Nursing. (n.d.). https://ojin.nursingworld.org/table-of-contents/volume-25-2020/number-3-september-2020/evolution-of-nelson-model-part-2/
Wolters Kluwer Health, Inc. (2024). Bates’ Visual Guide to Physical Examination: About. Bates’ Visual Guide to Physical Examination. Retrieved September 15, 2024, from https://batesvisualguide.com/public/About.aspx
This application also utilizes the Data, Information, Knowledge, and Wisdom Model. First is data. These are the raw data which can include physical examination techniques, such as palpation, inspection, percussion, and auscultation, normal and abnormal findings, such as heart and lung sounds, and related anatomical concepts.
These vast data can be turned into information by organizing such data into a more structured format. In Bates’, they classified these into different organ systems, like cardiovascular, respiratory, gastrointestinal to make it easier for the students where to look or what chapter they have to go back to in case they have clarifications. These chapters aid in producing meaning from the given data.
From information, can be transformed into knowledge by recognizing what is normal and abnormal to see in a patient. For example, since we know the different lung sounds, we can now recognize it when we hear them from a patient. Additionally, we can now apply how to correctly percuss a patient’s abdomen when they are having gastrointestinal discomfort.
From this knowledge, we can transform it into wisdom. By this transformation to wisdom, we can now make informed decisions about patient care. For example, if we recognize the different abnormal lung sounds, we can now identify patients who are at risk for certain conditions, such as pneumonia. We use this information to guide us on what kind of care does the patient needs.
Reference:
Informatics: Evolution of the Nelson Data, Information, Knowledge and Wisdom Model: Part 2 | OJIN: The Online Journal of Issues in Nursing. (n.d.). https://ojin.nursingworld.org/table-of-contents/volume-25-2020/number-3-september-2020/evolution-of-nelson-model-part-2/
Wolters Kluwer Health, Inc. (2024). Bates’ Visual Guide to Physical Examination: About. Bates’ Visual Guide to Physical Examination. Retrieved September 15, 2024, from https://batesvisualguide.com/public/About.aspx
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The application of DIKW model can be seen in the treatment process of each patient with their healthcare providers. For example,
Data: The nurses collect raw data such as the patient's nursing health history and PE during the assessment phase of the nursing process.
Information: Nurses then organize and structure the collected data. This process might include recording the reported findings in the electronic health records (EHR) and classifying the data into specific body systems (cardiovascular for increased blood pressure, renal for increased BUN and creatinine, etc.). This gives an overall picture of what may be happening with the patient and may help with the next step, knowledge.
Knowledge: The nurse analyzes and interprets this information which may reveal significant patterns. For example, increased albumin in the urine may indicate kidney disease which explains the elevated blood pressure.
Wisdom: The nurse applies clinical judgment and experience to decide on the best course of action, such as implementing interventions, monitoring the patient more closely, or recommending lifestyle changes. Wisdom involves taking all the knowledge and making the best decision to improve patient outcomes, such as prescribing antihypertensive medications or advising dietary changes.
Data: The nurses collect raw data such as the patient's nursing health history and PE during the assessment phase of the nursing process.
Information: Nurses then organize and structure the collected data. This process might include recording the reported findings in the electronic health records (EHR) and classifying the data into specific body systems (cardiovascular for increased blood pressure, renal for increased BUN and creatinine, etc.). This gives an overall picture of what may be happening with the patient and may help with the next step, knowledge.
Knowledge: The nurse analyzes and interprets this information which may reveal significant patterns. For example, increased albumin in the urine may indicate kidney disease which explains the elevated blood pressure.
Wisdom: The nurse applies clinical judgment and experience to decide on the best course of action, such as implementing interventions, monitoring the patient more closely, or recommending lifestyle changes. Wisdom involves taking all the knowledge and making the best decision to improve patient outcomes, such as prescribing antihypertensive medications or advising dietary changes.
191 words
In reply to Corrine Ayesha Guillen
Re: Use of DIKW in NI Applications
What a nice and specific example, Coco! I loved how you have applied recent concepts in N105 in your given scenario. Especially with the prevalence of cardiovascular and kidney diseases in the Philippines, it is important for nurses and healthcare providers to apply clinical judgement and improve health outcomes. Also, the way you laid it out using the DIKW model visualizes the process for us and the readers.
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Hi Ayesha, I particularly like your example under the information category. I think it's important that the data gathered from different sources be collated in an effective manner, and the example you provided maximizes data organization for easier inference. These kinds of considerations are things that immensely help clinicians during the process of healthcare provision.
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The Data-Information-Knowledge-Wisdom (DIKW) model is an essential framework that shows how data evolves into actionable insights in the healthcare system. This model helps guide decision-making of the nurses, improving patient care by transforming raw data to informed, experience-based actions.
A concrete example for this is how the nurses at PGH utilizes the Computerized Registry of Admission and Discharge, commonly know as RADISH, in recording and tracking the records of the patients. Applying the DIKW model,
Data is first collected when the nurse inputs raw patient information in the RADISH system. This would include vitals signs, lab results, medication administration, and signs and symptoms. This data would then be processed into information which would provide the trend of the patient’s previous assessments. For instance, a patient was checked for vital signs, temperature was 38.8C. With this step, an insight on the trend of the patient status over time can be m such as the rise in temperature. Knowledge is then applied by using her clinical mind to interpret the information presented by the patient. The nurse may recognize that the rise in temperature may indicate infection. Lastly, wisdom. This would emerge when the nurse uses her collated experience to adjust the care plan specific toward a single patient, providing holistic care.
In this scenario, the DIKW model supports the nurse by transforming raw data from assessments into actionable knowledge, improving patient care through informed decision-making aided with critical thinking and clinical reasoning.
Reference:
Garst, C., Carroll, C., Carroll, W., Adams, S., & Cox, D. (2024). Data from novice to expert: Aligning Benner with Dikw | HIMSS. https://www.himss.org/resources/data-novice-expert-aligning-benner-dikw
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the way you contextualize your answer with RADISH is indeed a great insight as we always encounter it during clinical duty. Great job for that, Candice!
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Example of applying nursing informatics in DIKW Model:
The DIKW model enables the seamless transformation of raw data into wisdom that can be applied in clinical decision-making.
Scenario: A patient arrives in the emergency room with symptoms of severe respiratory distress. The nursing team uses the hospital's Electronic Health Record (EHR) system to track the patient's vital signs and ensure timely interventions.
Data: A nurse inputs a patient's heart rate, blood pressure, and oxygen levels into the Electronic Health Record (EHR). These are isolated numbers without context.
Information: The EHR organizes these data points into graphs, showing trends like a rising heart rate or dropping blood pressure. Transforming raw data into meaningful, organized information for clinical use.
Knowledge: The nurse recognizes that the patient's vital trends suggest early signs of sepsis. Decision-support systems (DSS) provide clinical insights based on data patterns, guiding nurses in interpreting information.
Wisdom: The nurse administers fluids and antibiotics, acting on the knowledge that the patient may be developing sepsis. In some cases where an expert system is available, they suggest treatment options, helping nurses apply knowledge in critical situations to improve patient outcomes.
References: Nelson, R., (July 21, 2020) "Informatics: Evolution of the Nelson Data, Information, Knowledge and Wisdom Model: Part 2" OJIN: The Online Journal of Issues in Nursing Vol. 25, No. 3.
The DIKW model enables the seamless transformation of raw data into wisdom that can be applied in clinical decision-making.
Scenario: A patient arrives in the emergency room with symptoms of severe respiratory distress. The nursing team uses the hospital's Electronic Health Record (EHR) system to track the patient's vital signs and ensure timely interventions.
Data: A nurse inputs a patient's heart rate, blood pressure, and oxygen levels into the Electronic Health Record (EHR). These are isolated numbers without context.
Information: The EHR organizes these data points into graphs, showing trends like a rising heart rate or dropping blood pressure. Transforming raw data into meaningful, organized information for clinical use.
Knowledge: The nurse recognizes that the patient's vital trends suggest early signs of sepsis. Decision-support systems (DSS) provide clinical insights based on data patterns, guiding nurses in interpreting information.
Wisdom: The nurse administers fluids and antibiotics, acting on the knowledge that the patient may be developing sepsis. In some cases where an expert system is available, they suggest treatment options, helping nurses apply knowledge in critical situations to improve patient outcomes.
References: Nelson, R., (July 21, 2020) "Informatics: Evolution of the Nelson Data, Information, Knowledge and Wisdom Model: Part 2" OJIN: The Online Journal of Issues in Nursing Vol. 25, No. 3.
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I appreciate this example, Draven! I think you got the point of the DIKW model across very succinctly. Incorporating our topics from other nursing courses made your application of the concept more relevant and more understandable, as it shows a sort of "thought process" or thought evolution that we as students nurses also go through during our rotation!
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The COVID-19 pandemic has shown the importance of flexible learning modes for students. In the context of student nurses, various e-learning platforms, an application of nursing informatics, were introduced to educate and enhance the skills of student nurses even without a physical classroom. During online classes, E-learning platforms, such as the Bates’ Visual Guide, allow students to learn the whole physical examination process through interactive videos and other resources that can facilitate their learning.
Using the DIKW model, the data from these e-learning platforms are transformed through a series of steps. For instance, a student is listening to a discussion about BMI, and there is an activity in which the class will try to determine a patient’s BMI. Various numbers will be displayed in front of them, such as 21.9 and 22.1, which we refer to as the data. The data will then be organized (current weight: 21.9; previous weight: 22.1) to be understood. Now that they have a better understanding of the data, they will then use their knowledge of the different BMI categories and determine the categories into which the two values for weight fall. Recognizing the BMI classification will prompt the student to think of specific nursing interventions to solve the patient’s problem or maintain their current condition if the BMI is normal. Practical application of knowledge is considered the last step of the DIKW model, which we refer to as wisdom.
E-learning is only one of the many practical applications of nursing informatics, which I can say has been effective in improving my knowledge and skills as a nursing student. Not only does it allow you to learn outside the physical classroom, but it is also highly interactive, which makes learning more engaging.
Reference:
American Nurses Association. (2022). Nursing informatics: scope and standards of practice, 3rd Edition. Nursing World. https://www.nursingworld.org/~49c602/globalassets/catalog/book-toc/nursing-informatics-3e-sample-chapter.pdf
Using the DIKW model, the data from these e-learning platforms are transformed through a series of steps. For instance, a student is listening to a discussion about BMI, and there is an activity in which the class will try to determine a patient’s BMI. Various numbers will be displayed in front of them, such as 21.9 and 22.1, which we refer to as the data. The data will then be organized (current weight: 21.9; previous weight: 22.1) to be understood. Now that they have a better understanding of the data, they will then use their knowledge of the different BMI categories and determine the categories into which the two values for weight fall. Recognizing the BMI classification will prompt the student to think of specific nursing interventions to solve the patient’s problem or maintain their current condition if the BMI is normal. Practical application of knowledge is considered the last step of the DIKW model, which we refer to as wisdom.
E-learning is only one of the many practical applications of nursing informatics, which I can say has been effective in improving my knowledge and skills as a nursing student. Not only does it allow you to learn outside the physical classroom, but it is also highly interactive, which makes learning more engaging.
Reference:
American Nurses Association. (2022). Nursing informatics: scope and standards of practice, 3rd Edition. Nursing World. https://www.nursingworld.org/~49c602/globalassets/catalog/book-toc/nursing-informatics-3e-sample-chapter.pdf
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Wow, Vi! It's nice to know that the DIKW model for nursing is also seen not only in the nursing practice but also in nursing education. Your example helped me understand it from a student's perspective as we are currently immersed in the classroom setting. To add to your answer, I think that the wisdom part may be seen when the students integrate both their clinical reasoning and the presented knowledge to make sound judgments about their health status. With your answer, I may be able to also apply this in determining a sound judgment for my BMI and know the steps I should take to improve my overall health.
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In nursing informatics research, the DIKW model can be instrumental in transforming data into actionable wisdom. For instance, during my rotation in the OB-Gyne department at the outpatient clinic, they used an electronic health record system in the form of a Google Sheet. This spreadsheet captured all patient data and check-up statuses, serving as raw data.
From this spreadsheet it included the raw patient reports and building blocks of data. The healthcare team could extract important information, such as trends and patterns. For example, they could identify high-risk patients and categorize them into various gynecological conditions based on the trends observed in their check-ups. From this, they can develop knowledge and insights into patient conditions and risks, using these patterns to guide clinical decisions and prioritize patient care. Ultimately, this enabled the team to make well-informed decisions, such as customizing care plans for high-risk patients and addressing specific gynecological issues more efficiently, which represents their application of wisdom.
In this way, the electronic health record system, through its structured data, transforms raw reports into valuable knowledge and actionable wisdom, enhancing patient care and research in the OB-Gyne department.
Reference:
June. (2022, February 10). NILC blog. https://nursing-informatics.com/blog/theory-applied-to-informatics-dikw-theory/
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An example of the application of nursing informatics in nursing practice using the DIKW model can be demonstrated through the use of electronic health records (EHR) or systems like RADISH.
Data: Raw data is collected, such as a blood pressure of 160/110 mmHg and (+) proteinuria from a pregnant client’s recent VS/laboratory records.
Data: Raw data is collected, such as a blood pressure of 160/110 mmHg and (+) proteinuria from a pregnant client’s recent VS/laboratory records.
Information: The raw data is then analyzed and flagged by the system or nurse as abnormal.
Knowledge: The nurse integrates this information with clinical knowledge. The nurse may recognize that the client is at high risk and that the clinical manifestations align with severe pre-eclampsia.
Wisdom: The nurse applies critical thinking and clinical reasoning in adjusting the care plan, ensuring timely interventions to appropriately address the emerging needs of the client.
Knowledge: The nurse integrates this information with clinical knowledge. The nurse may recognize that the client is at high risk and that the clinical manifestations align with severe pre-eclampsia.
Wisdom: The nurse applies critical thinking and clinical reasoning in adjusting the care plan, ensuring timely interventions to appropriately address the emerging needs of the client.
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Well done Ms. Arago! Your concise example of the DIKW model has equipped me with new knowledge. I was able to accurately understand the purpose of using the model. Moreover, I realized that the DIKW model is a structural framework that organizes data which would be helpful in making a clinical decision.
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In reply to Alessandra Rein Isabel Arago
Re: Use of DIKW in NI Applications
by Lynze Kyle Fernandez -
Hi, Rein! I found your concrete examples easily understandable if we want to comprehend the basics and concepts of the DIKW model. In this example, you have provided us with a range of succeeding instances where the DIKW model can be applied. Apart from blood pressure readings, we have the patient's laboratory findings, their physical examination results, and other information turned into knowledge on the patient's health history. Thank you for your insights!
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This is a very good example, Rein! You provided a solid explanation of the DIKW model applied to nursing practice, specifically with the use of EHRs like RADISH. I also liked how you used severe pre-eclampsia as an example! You were able to incorporate our latest topic in N105, which really made our understanding of the topics feel interconnected.
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In reply to Alessandra Rein Isabel Arago
Re: Use of DIKW in NI Applications
This is a great example of the DIKW model, Rein! I believe that utilizing this model can guide us future nurses in developing a care plan and in performing our interventions on our patients.
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In reply to Alessandra Rein Isabel Arago
Re: Use of DIKW in NI Applications
by John Vincent Suniega -
Hello Rein! This example allowed me to understand better how DIKW works and put this into our perspective as student nurses currently having clinical duty. I agree that utilizing RADISH makes integration easier for us because it allows us to be efficient in determining the relationships between the information provided and seeing how each healthcare professional can contribute to resolving the different cases in the area.
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Nursing informatics is a crucial tool for transforming raw data into actionable knowledge. This process is essential for improving patient care, especially in the context of electronic health records (EHRs).
The DIKW model (Data, Information, Knowledge, Wisdom) provides a framework for understanding this transformation. Data in an EHR is raw, unprocessed information like vital signs and laboratory results. When organized and contextualized, this data becomes information. For example, a nurse might notice a pattern of consistently high blood pressure readings. This information is more meaningful than individual data points.
Knowledge is derived when healthcare professionals apply their expertise to this information. A nurse might use evidence-based guidelines to interpret the patient's high blood pressure and recommend a treatment plan. Wisdom is the application of this knowledge with clinical judgment, considering the patient's individual needs and preferences.
By facilitating the flow of data into wisdom, nursing informatics empowers nurses to make informed decisions that improve patient care. It supports data-driven decision-making in all healthcare settings, enhancing both the efficiency and quality of nursing practice.
References:
Cummins, M. R. (2014). Nursing Informatics and Learning Health System. CIN Computers Informatics Nursing, 32(10), 471–474. https://doi.org/10.1097/cin.0000000000000109
Cato, K. D., McGrow, K., & Sarah Collins Rossetti. (2020). Transforming clinical data into wisdom. Nursing Management, 51(11), 24–30. https://doi.org/10.1097/01.numa.0000719396.83518.d6
The DIKW model (Data, Information, Knowledge, Wisdom) provides a framework for understanding this transformation. Data in an EHR is raw, unprocessed information like vital signs and laboratory results. When organized and contextualized, this data becomes information. For example, a nurse might notice a pattern of consistently high blood pressure readings. This information is more meaningful than individual data points.
Knowledge is derived when healthcare professionals apply their expertise to this information. A nurse might use evidence-based guidelines to interpret the patient's high blood pressure and recommend a treatment plan. Wisdom is the application of this knowledge with clinical judgment, considering the patient's individual needs and preferences.
By facilitating the flow of data into wisdom, nursing informatics empowers nurses to make informed decisions that improve patient care. It supports data-driven decision-making in all healthcare settings, enhancing both the efficiency and quality of nursing practice.
References:
Cummins, M. R. (2014). Nursing Informatics and Learning Health System. CIN Computers Informatics Nursing, 32(10), 471–474. https://doi.org/10.1097/cin.0000000000000109
Cato, K. D., McGrow, K., & Sarah Collins Rossetti. (2020). Transforming clinical data into wisdom. Nursing Management, 51(11), 24–30. https://doi.org/10.1097/01.numa.0000719396.83518.d6
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During clinicals, utilizing the DIKW has been done subconsciously by all student nurses including me. The usage of this model means obtaining data and analyzing it so as an intervention can be made. One example would be:
Data: This may be done through physical assessment or simple monitoring of vital signs. An example of this would be obtaining the blood pressure of a patient hourly during the five hour duty.
Information: This is done when the student nurse gives meaning to the data provided. An example would be that per hour of the result of the blood pressure, the trend goes up.
Knowledge: In this category, information derived from data is further analyzed. What does it mean if the trend of the data is going up? With background knowledge of the nursing student, they are able to understand what is happening to the patient and/or may be alert that this information may indicate a worsening condition, such as hypertension or fluid overload. From there on
Wisdom: the nursing student can make a clinical decision about the patient's care through the knowledge that was provided.
Source: June. (2022, February 10). Theory applied to informatics: DIKW Theory | NILC Blog. https://nursing-informatics.com/blog/theory-applied-to-informatics-dikw-theory/#:~:text=DIKW%20explicitly%20outlines%20each%20step,and%20decision%20making%E2%80%9D%20(p.
Data: This may be done through physical assessment or simple monitoring of vital signs. An example of this would be obtaining the blood pressure of a patient hourly during the five hour duty.
Information: This is done when the student nurse gives meaning to the data provided. An example would be that per hour of the result of the blood pressure, the trend goes up.
Knowledge: In this category, information derived from data is further analyzed. What does it mean if the trend of the data is going up? With background knowledge of the nursing student, they are able to understand what is happening to the patient and/or may be alert that this information may indicate a worsening condition, such as hypertension or fluid overload. From there on
Wisdom: the nursing student can make a clinical decision about the patient's care through the knowledge that was provided.
Source: June. (2022, February 10). Theory applied to informatics: DIKW Theory | NILC Blog. https://nursing-informatics.com/blog/theory-applied-to-informatics-dikw-theory/#:~:text=DIKW%20explicitly%20outlines%20each%20step,and%20decision%20making%E2%80%9D%20(p.
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I completely agree with your observation. The DIKW model is often used instinctively during clinicals, and it truly helps in transforming raw data into actionable insights.
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I agree with you, Aniela! I think many of us nursing students follow the DIKW model subconsciously during clinicals too, especially when we assess patients and interpret data to make informed care decisions.
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The DIKW (Data, Information, Knowledge, Wisdom) framework provides a structured approach to transforming raw data into actionable insights, ultimately leading to improved patient care. A practical example is its application in the patient assessment process to nursing interventions.
Starting with data collection, assessments like heart rate, blood pressure, temperature, respiratory rate, and input/output are recorded (data). This raw information, through technology like EHR, is then organized and interpreted to identify patterns and trends (information). For instance, a consistently decreased urine output might indicate a potential underlying condition. By comparing the collected data to established nursing standards and guidelines, nurses can identify potential diagnoses and appropriate interventions (knowledge). For example, if the patient with decreased urine output has a history of diarrhea and vomiting, as revealed on records, they may require medications or IV fluids. Considering factors such as the patient's cultural background, personal preferences, and overall health status, the nurse can craft an individualized plan of intervention (wisdom).
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The DIKW paradigm comprises of Data, Information, Knowledge, and Wisdom are the core concepts in nursing informatics practice (Ronquillo, Curie, & Rodney, 2016) that transforms raw data into actionable insights to improve patient care outcomes. For example, let’s say we are nurses assigned to an OPD.
Data: The raw data includes the patient’s demographics, Nursing Health History, Physical Examination, and Laboratory and Diagnostic results (e.g., CBC, ABG, etc.), among others.
Information: As nurses, we organize the data into the EHS and interpret it by noting patterns and trends, such as elevated PaCO2 and decreased pH levels or elevated WBC and segmenters over time.
Knowledge: By analyzing and synthesizing the information, we identify interrelationships. We apply clinical knowledge to rationalize that elevated PaCO2 and decreased pH levels may indicate respiratory acidosis or other health issues, depending on the signs and symptoms exhibited. Similarly, elevated WBC and segmenters may suggest an infection, leading us to recommend a check-up with a physician or lifestyle changes.
Wisdom: Nurses, especially those with long-term experience, use their wisdom—built from critical thinking skills and previous encounters—to make informed decisions about the patient’s care. This might involve providing health education, suggesting further lab tests or diagnostics, or consulting with a physician for a more definitive diagnosis.
Reference:Ronquillo, C., Currie, L. M., & Rodney, P. (2016). The evolution of data-information-knowledge-wisdom in nursing informatics. Advances in Nursing Science, 39(1), E1–E18. https://doi.org/10.1097/ANS.0000000000000107
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Thank you for this, Mel! I like how you used an example that is relevant to our current scope of knowledge. Additionally, I can see how we can apply this model when we enter our clinical rotations.
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In public health nursing, addressing malnutrition involves applying a systematic approach that integrates the DIKW model to improve community health outcomes. Here's how the DIKW framework can be applied to tackling malnutrition:
Data: Nurses collect raw data on malnutrition rates through various means, such as community surveys, school health screenings, and health records. This data includes metrics like the percentage of children with low weight-for-age ratios, the prevalence of vitamin deficiencies, and other nutritional indicators.
Information: The collected data is then processed and analyzed to identify patterns and trends in malnutrition across different demographics and regions. For example, data might reveal that certain areas have higher rates of malnutrition due to factors such as poverty or lack of access to nutritious food.
Knowledge: From this processed information, public health nurses gain a deeper understanding of the causes and impacts of malnutrition. They can identify key risk factors, such as socio-economic conditions, dietary habits, and local food availability. This knowledge helps in developing targeted interventions, such as nutrition education programs, food distribution efforts, or policy recommendations to address identified gaps.
Wisdom: Using this knowledge, nurses design and implement effective public health strategies. For instance, they might launch community-based nutrition workshops, advocate for school meal programs, or collaborate with local organizations to improve food security. They continuously evaluate and refine these strategies based on ongoing data and feedback to ensure they effectively combat malnutrition and promote long-term health improvements in the community.
By applying the DIKW model, public health nurses can transform raw data into actionable strategies that address the root causes of malnutrition and improve overall community health.
Data: Nurses collect raw data on malnutrition rates through various means, such as community surveys, school health screenings, and health records. This data includes metrics like the percentage of children with low weight-for-age ratios, the prevalence of vitamin deficiencies, and other nutritional indicators.
Information: The collected data is then processed and analyzed to identify patterns and trends in malnutrition across different demographics and regions. For example, data might reveal that certain areas have higher rates of malnutrition due to factors such as poverty or lack of access to nutritious food.
Knowledge: From this processed information, public health nurses gain a deeper understanding of the causes and impacts of malnutrition. They can identify key risk factors, such as socio-economic conditions, dietary habits, and local food availability. This knowledge helps in developing targeted interventions, such as nutrition education programs, food distribution efforts, or policy recommendations to address identified gaps.
Wisdom: Using this knowledge, nurses design and implement effective public health strategies. For instance, they might launch community-based nutrition workshops, advocate for school meal programs, or collaborate with local organizations to improve food security. They continuously evaluate and refine these strategies based on ongoing data and feedback to ensure they effectively combat malnutrition and promote long-term health improvements in the community.
By applying the DIKW model, public health nurses can transform raw data into actionable strategies that address the root causes of malnutrition and improve overall community health.
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In reply to Nicole Angelie Bañada
Re: Use of DIKW in NI Applications
Great insights, Angelie! Reading this process makes it seem easy but I know how taxing it is in reality. This is why I look up to public health nurses who create strategies and policies to address the root causes of public health issues.
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We can take a look at how we can teach our patients about communicable diseases through the DIKW. We can use a nurse learning more about COVID-19 during the pandemic as an example.
Data: During the pandemic, there was a very significant increase in the number of COVID-19 patients in the Philippines, including both symptomatic and asymptomatic individuals.
Information: The analytics are processed and stored in a database that can summarize the number of cases, their locations, and other statistics that were collected from the affected communities.
Knowledge: The nurse can use this data and recognize the patterns and its consequences. What does the information mean and how should they process it? What are the outcomes of the information that they have been given? With the knowledge of the nurse, can they draw conclusions that will be able to benefit more patients in the long run?
Wisdom: The nurse can conclude that proper safety measures are important in order to mitigate the number of cases. As such, she will be able to reach out to her patients and instruct them on relevant topics such as hygiene, mask wearing, social distancing, and other ways that patients can use to avoid COVID-19. As nurses, it is important for us to be able to foresee outcomes that will most benefit our patients.
Nelson, R. (2020, July 21). Ojin homepage. OJIN. https://ojin.nursingworld.org/table-of-contents/volume-25-2020/number-3-september-2020/evolution-of-nelson-model-part-2/
Data: During the pandemic, there was a very significant increase in the number of COVID-19 patients in the Philippines, including both symptomatic and asymptomatic individuals.
Information: The analytics are processed and stored in a database that can summarize the number of cases, their locations, and other statistics that were collected from the affected communities.
Knowledge: The nurse can use this data and recognize the patterns and its consequences. What does the information mean and how should they process it? What are the outcomes of the information that they have been given? With the knowledge of the nurse, can they draw conclusions that will be able to benefit more patients in the long run?
Wisdom: The nurse can conclude that proper safety measures are important in order to mitigate the number of cases. As such, she will be able to reach out to her patients and instruct them on relevant topics such as hygiene, mask wearing, social distancing, and other ways that patients can use to avoid COVID-19. As nurses, it is important for us to be able to foresee outcomes that will most benefit our patients.
Nelson, R. (2020, July 21). Ojin homepage. OJIN. https://ojin.nursingworld.org/table-of-contents/volume-25-2020/number-3-september-2020/evolution-of-nelson-model-part-2/
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I'd like to share my experience from last semester during our community duty at Barangay 844, where we were assigned to provide nursing care for a healthy family. This nuclear family consists of three members: the father, the pregnant mother, and their daughter. I realized afterward that I had inadvertently used nursing informatics techniques to organize their data.
Data: I gathered nursing health histories and performed physical exams on each family member. Additionally, I monitored their vital signs during each visit, which took place on Mondays, Wednesdays, and Thursdays.
Information: I organized the collected data using Google Docs and created a separate Google Sheets document for tracking their vital signs. I utilized conditional formatting in Google Sheets to automatically classify blood pressure readings into categories such as normal, pre-hypertensive, stage 1 hypertension, or stage 2 hypertension, using color codes for easy interpretation.
Knowledge: I analyzed the blood pressure trends, particularly noting that the father consistently had elevated readings. His systolic blood pressure was consistently around 140 mmHg, and his diastolic ranged from 75-85 mmHg. The father indicated that he had not visited a doctor due to his work schedule, leading me to identify undiagnosed hypertension as a significant health concern.
Wisdom: I developed a nursing care plan for the father and collaborated with him to set achievable goals. I provided health education on the causes, symptoms, and potential complications of untreated hypertension. I also introduced him to the DASH Diet to guide his dietary choices and suggested relaxation techniques to help manage his blood pressure. Finally, I referred him to the Bagong Barangay Health Center and encouraged him to make an appointment for further evaluation.
Reference:
Cato, K. D., McGrow, K., & Rossetti, S. C. (2020, November). Transforming Clinical Data Into Wisdom: Artificial Intelligence Implications for nurse leaders. Transforming clinical data into wisdom. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8018525/
Data: I gathered nursing health histories and performed physical exams on each family member. Additionally, I monitored their vital signs during each visit, which took place on Mondays, Wednesdays, and Thursdays.
Information: I organized the collected data using Google Docs and created a separate Google Sheets document for tracking their vital signs. I utilized conditional formatting in Google Sheets to automatically classify blood pressure readings into categories such as normal, pre-hypertensive, stage 1 hypertension, or stage 2 hypertension, using color codes for easy interpretation.
Knowledge: I analyzed the blood pressure trends, particularly noting that the father consistently had elevated readings. His systolic blood pressure was consistently around 140 mmHg, and his diastolic ranged from 75-85 mmHg. The father indicated that he had not visited a doctor due to his work schedule, leading me to identify undiagnosed hypertension as a significant health concern.
Wisdom: I developed a nursing care plan for the father and collaborated with him to set achievable goals. I provided health education on the causes, symptoms, and potential complications of untreated hypertension. I also introduced him to the DASH Diet to guide his dietary choices and suggested relaxation techniques to help manage his blood pressure. Finally, I referred him to the Bagong Barangay Health Center and encouraged him to make an appointment for further evaluation.
Reference:
Cato, K. D., McGrow, K., & Rossetti, S. C. (2020, November). Transforming Clinical Data Into Wisdom: Artificial Intelligence Implications for nurse leaders. Transforming clinical data into wisdom. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8018525/
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The DIKW model is a framework showing how data is processed into information, then into knowledge.
One example of the use of nursing informatics in practice is the interpretation of vital signs.
One example of the use of nursing informatics in practice is the interpretation of vital signs.
In this scenario, you are a nurse being given the following:
Data: You are given the numbers 30, 50, 80, and 110. As presented, the numbers are simply data and are essentially meaningless.
Information: However, if the numbers are reorganized and given labels in this way: PR 110 bpm, RR 30 breaths per minute, and BP 80/50 mmHg, then a nurse would be able to look at this information and interpret the vital signs that reflect her patient’s condition.
Knowledge: Knowledge, then, would be a nurse not only understanding what the numbers represent, but also knowing what their implication is in the context of her patient’s condition. If, for example, she recorded these vital signs after giving a transfusion to her patient, she would then be able to connect these vital signs with other signs and symptoms such as chills and realize that her patient may be going into shock.
References
American Nurses Association. (2022). Nursing informatics: Scope and standards of Practice.
American Nurses Association. (2022). Nursing informatics: Scope and standards of Practice.
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To describe the DIKW model succinctly, it describes the relationship between data, information, knowledge, and wisdom, and how data is processed and transformed in the field of nursing practice. One example of the application of the DIKW model in the field of nursing practice is through the use of the RADISH system of PGH, which a lot of educators and students are familiar with. Given a situation where a patient is admitted to a ward due to a certain condition and with a possibility of exacerbation of fluid and electrolyte imbalance, we would be able to account for values and or written results from laboratory tests and examinations as data and information as both are raw, unprocessed, and have not been associated with one another and with the patient’s human response. For the next stage of the DIKW model, which is knowledge and wisdom, said data and information that were inputted in the patient’s RADISH chart goes beyond the computers and through the nursing expertise of the healthcare workers (such as nurses) for possible association and interpretation of information to ascertain needed interventions and care. Adequate knowledge may enable the nurse to see that the risk of an electrolyte imbalance, such as hypermagnesemia, for example, is high and likely and wisdom gives the nurse the confidence to decide which intervention should be done first.
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To provide an example of how the DIKW paradigm works in the nursing practice, specifically in the education specialty, I would like to emphasize how a student nurse is able to utilize this model in our duty papers from assessment down to evaluation of the care. In each duty rotation, may it be in the community or hospital setting, students are assigned to a minimum of 2 index patients that they shall be assessing, monitoring, and rendering care for. In the first encounter with the patient, we introduce ourselves and establish our rapport with them, and shortly after, we proceed with acquiring the patient’s data for our nursing health history and physical examination. These acquired data is then transformed into information by the student nurse who categorizes her documentation into several segments, such as the use of Gordon’s Functional Health Patterns and the cephalocaudal components in the physical examination form, which is done more conveniently via word processing platforms (i.e., Google Docs).
Knowledge is now derived from the information arranged and analyzed by the student nurse, which allows them to formulate and implement their nursing care plans throughout the course of their contract with the patient. These interventions are now documented through the same word processing platforms, and is accompanied by the creation of pathophysiologies (via Canva, Lucidchart, etc.) and teaching materials (via Canva, Photoshop, Powerpoint, etc.) that allow the student nurse to perform her health teaching sessions for the client. Wisdom is applied by the student nurse as she integrates her knowledge and translates it into practical application in the healthcare setting.
REFERENCES:
- Brunel University London. (2024, August 19). Health sciences: Images for posters and presentations. https://libguides.brunel.ac.uk/c.php?g=202056&p=4909983
Knowledge is now derived from the information arranged and analyzed by the student nurse, which allows them to formulate and implement their nursing care plans throughout the course of their contract with the patient. These interventions are now documented through the same word processing platforms, and is accompanied by the creation of pathophysiologies (via Canva, Lucidchart, etc.) and teaching materials (via Canva, Photoshop, Powerpoint, etc.) that allow the student nurse to perform her health teaching sessions for the client. Wisdom is applied by the student nurse as she integrates her knowledge and translates it into practical application in the healthcare setting.
REFERENCES:
- Brunel University London. (2024, August 19). Health sciences: Images for posters and presentations. https://libguides.brunel.ac.uk/c.php?g=202056&p=4909983
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Hi, Lynze! I appreciate how you explained the DIKW paradigm in nursing education as it really shows how theory translates into practice. I also like how you mentioned tools like Canva and Google Docs for organizing duty papers and creating teaching materials because it is very timely now that we have our clinical duties.
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One example of the application of nursing informatics in nursing practice is the use of the RADISH in PGH. The electronic health records or data of patients from different departments make it easier for collaborative healthcare providers to access timely patient data, enabling them to provide appropriate interventions and address patient needs. (Data) The RADISH system collects and stores data from patients, including demographics, timely vital signs, medical history, laboratory tests, and intervention plans. (Information) This data is analyzed to identify patient needs, status, and outcomes, supporting clinical decision-making. (Knowledge) Nurses and other healthcare providers can access and interpret this information to develop a plan of care for the patient and make informed decisions about patient management. (Wisdom) Through ongoing monitoring and evaluation, nurses and other healthcare providers can improve patient outcome.
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Sample of DIKW model in nursing practice
Data:
The student nurse took Patient A's vital signs (e.g., blood pressure, respiratory rate).
Information:
The student nurse periodically took Patient A's vital signs and noticed some irregular patterns, such as consistently elevated blood pressure and an increasing respiratory rate over time.
Knowledge:
With the knowledge gained from clinical training, the student nurse understands that elevated blood pressure combined with an increased respiratory rate may indicate potential complications.
Wisdom:
Based on the student's knowledge, they decide to inform the supervising nurse or doctor immediately and suggest conducting additional tests or monitoring for Patient A to prevent possible deterioration. By doing so, they demonstrate sound clinical judgment and ensure appropriate interventions can be made to maintain the patient's health.
Data:
The student nurse took Patient A's vital signs (e.g., blood pressure, respiratory rate).
Information:
The student nurse periodically took Patient A's vital signs and noticed some irregular patterns, such as consistently elevated blood pressure and an increasing respiratory rate over time.
Knowledge:
With the knowledge gained from clinical training, the student nurse understands that elevated blood pressure combined with an increased respiratory rate may indicate potential complications.
Wisdom:
Based on the student's knowledge, they decide to inform the supervising nurse or doctor immediately and suggest conducting additional tests or monitoring for Patient A to prevent possible deterioration. By doing so, they demonstrate sound clinical judgment and ensure appropriate interventions can be made to maintain the patient's health.
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An example that can be seen with the application of nursing informatics in the nursing practice is the aspect of how patients can now consult with a healthcare professional online– or simply so, telecommunication. Starting with the data aspect of the DIKW model, once you consult with the healthcare professional, they initially gather data on why the patient has booked a consultation, what/how/when the symptoms started, what triggered it, and other health-history-related questions to pinpoint and help with the assessment of the patient. Following this comes the information and knowledge aspect of the model. The pieces of data gathered from the initial evaluation of the patient are then connected all together to form and come up with an initial diagnosis of the patient. Now that the healthcare professional is well aware of the diagnosis of the patient, healthcare professionals will now be able to come up with a care plan implementing the appropriate type of interventions needed to be done to the patient to effectively address their current diagnosis– tackling the wisdom aspect of the DIKW model.
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Hi, Aly! Your example of telehealth aptly demonstrates the DIKW model in action. It starts with collecting data during a virtual consultation, which is then analyzed to form a diagnosis. This knowledge informs a tailored care plan, illustrating how nursing informatics transforms data into effective, wisdom-driven patient care. This process not only showcases the seamless integration of data into actionable healthcare strategies but also highlights how nursing informatics enhances care delivery through advanced technological tools.
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Teleconsultation has been made possible by nursing informatics where virtual consultations may be done by the patient with a healthcare professional regarding their health concerns. In this application of health informatics, nurses play a role in assessing, monitoring, and educating the clients about their health condition and progress (Telehealth Nurse, n.d.).
The data in this situation are the primary data that the client would report such as vital signs, health history, and symptoms experienced. For instance, a client is having a headache with 140/100 mmHg blood pressure and a heart rate of 130 beats per minute after eating large amounts of lechon.
After receiving the abovementioned data, the nurse creates information about the patient and contextualizes the symptoms of the patient based on their medical history and present report. In this part, the nurse organizes the data from past history such as episodes of hypertension and triggering foods for it to happen.
The information generated is analyzed and the mechanism of different factors were unraveled to identify why the patient is experiencing those symptoms and how these factors affect patient health status. Through analysis of the patterns of food intake and the past medical history, the nurse has now an overview of the physiologic process of having hypertension centered on the case of the client.
After acquiring the knowledge of the processes and reasons for the patient's health condition, the nurse could make a clinical decision through clinical reasoning and critical thinking for the patient to alleviate the symptoms they are experiencing. At this juncture, the nurse may provide dietary modification and education to the client in order to manage their hypertension
Word Count: 274
Telehealth Nurse. (n.d.). https://www.multiplymii.com/job-description/telehealth-nurse#:~:text=Telehealth%20Nurses%20perform%20various%20nursing,strategies%2C%20and%20monitor%20their%20progress
The data in this situation are the primary data that the client would report such as vital signs, health history, and symptoms experienced. For instance, a client is having a headache with 140/100 mmHg blood pressure and a heart rate of 130 beats per minute after eating large amounts of lechon.
After receiving the abovementioned data, the nurse creates information about the patient and contextualizes the symptoms of the patient based on their medical history and present report. In this part, the nurse organizes the data from past history such as episodes of hypertension and triggering foods for it to happen.
The information generated is analyzed and the mechanism of different factors were unraveled to identify why the patient is experiencing those symptoms and how these factors affect patient health status. Through analysis of the patterns of food intake and the past medical history, the nurse has now an overview of the physiologic process of having hypertension centered on the case of the client.
After acquiring the knowledge of the processes and reasons for the patient's health condition, the nurse could make a clinical decision through clinical reasoning and critical thinking for the patient to alleviate the symptoms they are experiencing. At this juncture, the nurse may provide dietary modification and education to the client in order to manage their hypertension
Word Count: 274
Telehealth Nurse. (n.d.). https://www.multiplymii.com/job-description/telehealth-nurse#:~:text=Telehealth%20Nurses%20perform%20various%20nursing,strategies%2C%20and%20monitor%20their%20progress
279 words
Excellent example, Austine! In your case, I was able to identify how data transforms using the DIKW model and how it can be used in nursing informatics to assess, monitor, and educate clients, resulting in an exceptional personalized nursing care plan.
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According to the American Nurses Association (2022), the DIKW model shows how data is turned into information, information into knowledge, and knowledge into wisdom through application. In this model, each level increases in complexity and interactions, and requires greater application of human intellect. To give an example of how data transforms using the DIKW model, I will draw from my personal experience in community nursing practice.
First, I collected all the necessary patient data for the assessment database. This data was then organized into a more understandable format and transformed into information using technological tools. To interpret the meaning of this information, I placed it within the context of the family’s situation and condition. Using my existing knowledge of public health nursing, I determined whether the information represented normal or abnormal findings. After this understanding of knowledge, wisdom is applied by integrating service with compassion, as I provided appropriate nursing interventions for the patient and their family, such as health education and promotion. As this process goes on, there is a greater need for the application of human intellect as the complexity increases and as I gain more interactions and interrelationships.
References:
- American Nurses Association. (2022). Nursing Informatics: Scope and Standards of Practice, 3rd Edition. https://www.nursingworld.org/~49c602/globalassets/catalog/book-toc/nursing-informatics-3e-sample-chapter.pdf
- Nelson, R. (2020). Informatics: Evolution of the Nelson Data, Information, Knowledge and Wisdom Model: Part 2. OJIN the Online Journal of Issues in Nursing, 25(3). https://doi.org/10.3912/ojin.vol25no03infocol01
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The DIKW model may be used to understand how the clinical data support systems (CDSS) help healthcare providers in nursing practice, particularly in clinical decision-making and alert them on possible errors for better patient outcomes. For example, the DIKW model may be used to understand how the CDSS helps in chemotherapy (Cifra et al., 2022) and avoid errors from lack of precision.
Data: First, data regarding the patient’s demographics, history, imaging studies, laboratory results, and previous regimens are collected from their EHR with the help of CDSS. Previously, these were done manually by nurses.
Information: Next, the CDSS may process the gathered raw data and identify relevant information to identify patients potential risks regarding a chemotherapy regimen, such as analyzing their previous responses to treatments, as well as their abnormalities in their laboratory results. Before, these were checked manually by nurses and doctors, such as their toxicity assessments, which may be compromised by numerous factors like error-prone paper orders.
Knowledge: then, the CDSS can now further organize and synthesize the information with the help of various clinical guidelines to generate patterns and guide the healthcare provider on generating more insights and informed decisions.
Wisdom: Finally, nurses and other healthcare providers use their clinical judgment to apply their insights and take note of the alerts by the CDSS such as from potential adverse reactions. With this, all healthcare providers are able to make a sound judgment, understanding various possibilities guided by their knowledge and the CDSS.
With this, it can be seen how technology-aided clinical decision making, such as the use of CDSS, may help in providing more appropriate care in patients needing chemotherapy (Cifra et al., 2022), and may even be applied in other aspects of nursing.
Word count: 287
References:
Cifra, C. L., Custer, J. W., & Fackler, J. C. (2022). A research agenda for diagnostic excellence in critical care medicine. Critical Care Clinics, 38(1), 141–157. https://doi.org/10.1016/j.ccc.2021.07.003
Dance, J. (2021, September 20). The DIKW Consulting model - Fresh Consulting. Fresh Consulting. https://www.freshconsulting.com/insights/blog/the-dikw-consulting-model/
Data: First, data regarding the patient’s demographics, history, imaging studies, laboratory results, and previous regimens are collected from their EHR with the help of CDSS. Previously, these were done manually by nurses.
Information: Next, the CDSS may process the gathered raw data and identify relevant information to identify patients potential risks regarding a chemotherapy regimen, such as analyzing their previous responses to treatments, as well as their abnormalities in their laboratory results. Before, these were checked manually by nurses and doctors, such as their toxicity assessments, which may be compromised by numerous factors like error-prone paper orders.
Knowledge: then, the CDSS can now further organize and synthesize the information with the help of various clinical guidelines to generate patterns and guide the healthcare provider on generating more insights and informed decisions.
Wisdom: Finally, nurses and other healthcare providers use their clinical judgment to apply their insights and take note of the alerts by the CDSS such as from potential adverse reactions. With this, all healthcare providers are able to make a sound judgment, understanding various possibilities guided by their knowledge and the CDSS.
With this, it can be seen how technology-aided clinical decision making, such as the use of CDSS, may help in providing more appropriate care in patients needing chemotherapy (Cifra et al., 2022), and may even be applied in other aspects of nursing.
Word count: 287
References:
Cifra, C. L., Custer, J. W., & Fackler, J. C. (2022). A research agenda for diagnostic excellence in critical care medicine. Critical Care Clinics, 38(1), 141–157. https://doi.org/10.1016/j.ccc.2021.07.003
Dance, J. (2021, September 20). The DIKW Consulting model - Fresh Consulting. Fresh Consulting. https://www.freshconsulting.com/insights/blog/the-dikw-consulting-model/
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I appreciate the comprehensive example, Kynier! You also added how it was previously done before the prevalence of nursing informatics. I can now see its relevance to our clinical practice and how it will help guide us to have better clinical reasoning and decision-making.
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A specific example could be applied in the OPD-Maternal and OB-Gyne Ward. (Data) From the initial assessment where vital signs, past medical history, and complications were identified. NI plays an important role in seeing the needs of the patients from a wider perspective. (Information) With that data gathered, they may now categorize their patients who are high-risk and per healthcare need. (Knowledge) From there, those who are high risk are prompted immediately with the nursing care they need, while those who are not severe may opt to schedule at a later time. (Wisdom) Those who do not need immediate care will be referred to the OCRA system to schedule their visit, and those who are high-risk may be referred to a doctor in the same department or other wards. NI also plays an important role in terms of the communication between different departments to ensure that the referral system is met.
In conclusion, NI has a crucial role not just in terms of digital platforms, but also in the improvement of care and efficiency of workflow within nurses, and also other healthcare professionals. This is crucial in meeting the patient’s needs promptly and preventing miscommunications among departments.
In conclusion, NI has a crucial role not just in terms of digital platforms, but also in the improvement of care and efficiency of workflow within nurses, and also other healthcare professionals. This is crucial in meeting the patient’s needs promptly and preventing miscommunications among departments.
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According to Kamnski (2021), “it (DIKW theory) does help nurses and other professionals to articulate how we use data, information, and knowledge within our practice and how they can support the development and application of wisdom within the context of healthcare. In the nursing practice, what we usually take advantage of in nursing informatics is the use of electronic health records in hospitals or in providing patient care in general. This is where data like the patient's vital signs, condition, lab results, etc., are recorded and shared amongst the healthcare team. Next is information, and the information that the nurse has collected is being organized for relationships to be established between the data and data sets. This would allow the team to make decisions based on what information was given from the data. Which then leads us to knowledge, where the healthcare team applies their expertise on the matter, identifying what problems there might be based on the information extracted from the data, and what interventions are needed to be done to address the problems that the patient has. Then comes wisdom, as the interventions progress there might be changes in the patient’s data, or statistics that might require further interventions, actions, and/or changes that need to be done. This would depend on the wisdom or experience of the nurse handling the patient, and deciding what the best action to take is depending on the needs of the patient. But all of these processes are interconnected and each can cycle back to another depending on the case of the patient. This integrates everything from technology to the information, to the skills of the nurse to provide the best care for the patient.
Kaminski, J. (2021). Theory applied to informatics: DIKW Theory Editorial. Canadian Journal of Nursing Informatics, 16(3-4). https://cjni.net/journal/?
p=9374
Kaminski, J. (2021). Theory applied to informatics: DIKW Theory Editorial. Canadian Journal of Nursing Informatics, 16(3-4). https://cjni.net/journal/?
p=9374
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In nursing education and practice, applying the DIKW model to address undernutrition in community settings can be demonstrated through the following:
- Data: The nurse begins by collecting raw data, such as health history and physical assessments, including but not limited to food recalls and anthropometric measurements.
- Information: The collected data is then organized into structured records. The nurse creates a nutrient analysis and compares it against established age-appropriate nutritional guidelines. The nurse also identifies the classification of the current anthropometric measurements based on the age-appropriate growth indicators.
- Knowledge: The nurse analyzes the structured data to interpret trends and make sense of information. For instance, the nurse might discover a high prevalence of micronutrient deficiencies within the community and correlate this with inadequate dietary practices.
- Wisdom: With the acquired knowledge, the nurse develops and implements targeted interventions and educational programs. This could involve designing personalized diet plans, conducting community health education sessions on proper nutrition, and advocating for policy changes to improve nutritional resources.
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The DIKW, or Data, Information, Knowledge, and Wisdom Model, is one of the most well-known nursing informatics theories, providing a theoretical framework for defining nursing's scope of practice. These are required for using the tools to support patients, families, and communities. This covers each phase of the theoretical framework and provides clear guidance on how data can be processed and changed into complex knowledge. It also describes how arriving at wisdom drives quality clinical practice and decision-making. (Kaminski, 2022).
An example of application of nursing informatics in education could be:
Data: The university gathers raw data on student achievement, primarily through grades, tests, and performance in both theoretical and clinical tasks.
Information: The information is then collated and evaluated to see where students are struggling. Clinical educators identified patterns and trends in student learning. For example, students are good at remembering theoretical information, especially with normal values. Still, they struggle to use it in a clinical setting with patients in various settings that are not constant or stable.
Knowledge: Based on the studied data, the university or institution creates an intervention in the form of a revamped simulation-based curriculum that focuses on complicated scenarios and allows students to practice and adapt to real-world situations.
Wisdom: The new curriculum allows nurses at the institution to improve student learning outcomes, boost critical thinking abilities, and prepare future nurses for the complexities of nursing practice.
References:
Kaminski, J. (2022, February 10). Theory applied to informatics: DIKW Theory. NILC blog. https://nursing-informatics.com/blog/theory-applied-to-informatics-dikw-theory/
An example of application of nursing informatics in education could be:
Data: The university gathers raw data on student achievement, primarily through grades, tests, and performance in both theoretical and clinical tasks.
Information: The information is then collated and evaluated to see where students are struggling. Clinical educators identified patterns and trends in student learning. For example, students are good at remembering theoretical information, especially with normal values. Still, they struggle to use it in a clinical setting with patients in various settings that are not constant or stable.
Knowledge: Based on the studied data, the university or institution creates an intervention in the form of a revamped simulation-based curriculum that focuses on complicated scenarios and allows students to practice and adapt to real-world situations.
Wisdom: The new curriculum allows nurses at the institution to improve student learning outcomes, boost critical thinking abilities, and prepare future nurses for the complexities of nursing practice.
References:
Kaminski, J. (2022, February 10). Theory applied to informatics: DIKW Theory. NILC blog. https://nursing-informatics.com/blog/theory-applied-to-informatics-dikw-theory/
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It's great that you were able to build upon something we can relate to. With this, I have a better understanding of the DIKW concept, and I hope we can apply this framework in our future endeavors.
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In nursing informatics, data is collected from a variety of sources, such as electronic health records, patient interviews, and medical devices. This data is then transformed into information that can be used to improve patient care. For example, a nurse could use data from an EHR to identify patients who are at risk for developing a complication. This information could then be used to develop a plan to prevent the complication. Once information has been used to improve patient care, it can be transformed into knowledge. Knowledge is information that has been understood and applied. For example, a nurse could learn from experience that a certain type of medication is effective in preventing a certain complication. This knowledge could then be shared with other nurses. Finally, knowledge can be used to make wise decisions about patient care. Wisdom is knowledge that has been used to make good decisions. For example, a nurse could use their knowledge of a patient's medical history to develop a care plan that is tailored to the patient's individual needs.
The DIKW model is a useful tool for understanding how data is transformed into information, knowledge, and wisdom in nursing informatics. By using nursing informatics, nurses can improve patient care by using data to make better decisions.
Reference:
Garcia-Dia, M. J. (2019). Project Management in Nursing Informatics. https://connect.springerpub.com/highwire_display/entity_view/node/102001/content_details
The DIKW model is a useful tool for understanding how data is transformed into information, knowledge, and wisdom in nursing informatics. By using nursing informatics, nurses can improve patient care by using data to make better decisions.
Reference:
Garcia-Dia, M. J. (2019). Project Management in Nursing Informatics. https://connect.springerpub.com/highwire_display/entity_view/node/102001/content_details
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During the nurse health history taking, the patient’s height and weight is obtained. His height and weight is the data or the “raw fact” (Nelson & Joos, 1989). When this data is interpreted like the patient is classified as overweight according to the Body Mass Index (BMI), this is called information or “raw fact processed to produce meaning” (Nelson & Joos, 1989). Given that the client is overweight, the nurse interprets the BMI and understands its implications for the patient's health such as the client being at a higher risk for conditions such as cardiovascular diseases or type 2 diabetes. This is when knowledge is applied since it is the “group of interrelated pieces of information” (Nelson & Joos, 1989). With this, the nurse then decides to make a plan of care to reduce the client’s weight and provide a health education on lifestyle changes such as having a balanced diet and increasing physical activity. This would be wisdom or the nurse’s “ability to apply knowledge to human problems.” (Nelson & Joos, 1989). Based on the knowledge, the nurse makes clinical judgements and decisions, which is wisdom or the last concept of the paradigm.
REFERENCES
REFERENCES
- Nelson, R., (July 21, 2020) "Informatics: Evolution of the Nelson Data, Information, Knowledge and Wisdom Model: Part 2" OJIN: The Online Journal of Issues in Nursing Vol. 25, No. 3.
- Nelson, R, and Joos, I (1989). On language in nursing: From data to wisdom. Pennsylvania League for Nursing PLN Visions, 1(5), 6.
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The emergence of the Electronic Health Records (EHRs) is one example wherein the nursing informatics is applied in the nursing practice. In application of the DIKW model, the figures and statistics that are collected from the patient is inputted in the system as a form of Data. At this point, the nurse inputs all details that are collected both from the health history taking and physical assessment and describes but no further interpretation is generated. From this, a health database is generated which displays organized data and is therefore interpreted by nurses and/or doctors in the form Information.
Once the health database has been established, the plan of care and intervention is conceptualized by both the doctor and the nurse in the form of Knowledge where the information is correlated. Lastly, the plan of care is implemented to the patient in the form of Wisdom where the knowledge gained from the data and information is applied.
Reference:
Mielke, J. (2024, August 22). 4 Nursing Informatics Examples & Applications. nurse.org. https://nurse.org/education/nursing-informatics-examples/
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Nursing informatics may be applied in nursing education, for example, through providing training on electronic medical records for nursing students. A study done by Ting et al in 2021 reviewed existing literature on this integration, and found that “electronic health record education” and training for nurses should be “multipronged” and targeted to clinical workflows. Hence, it is possible to integrate such training into the nursing curricula, because it enhances students’ skills and readiness for clinical practice.
The DIKW model can be incorporated for a structured approach. Raw data is transformed into actionable insights, hence, educators can enhance training programs, address specific learning needs, and prepare students for real-world use of EMRs. Hands-on practice is likely to improve student proficiency, and the training program can be adjusted for specific simulations. This results in a more effective and responsive training program that aligns with current standards and best practices, thereby enhancing student proficiency and ultimately lead to better patient care outcomes.
Nelson, R. (2020). Informatics: Evolution of the nelson data, information, knowledge and wisdom model: Part 2. OJIN: The Online Journal of Issues in Nursing, 25(3). https://doi.org/10.3912/ojin.vol25no03infocol01
Ting, J., Garnett, A., & Donelle, L. (2021). Nursing education and training on electronic health record systems: An integrative review. Nurse education in practice, 55, 103168. https://doi.org/10.1016/j.nepr.2021.103168
The DIKW model can be incorporated for a structured approach. Raw data is transformed into actionable insights, hence, educators can enhance training programs, address specific learning needs, and prepare students for real-world use of EMRs. Hands-on practice is likely to improve student proficiency, and the training program can be adjusted for specific simulations. This results in a more effective and responsive training program that aligns with current standards and best practices, thereby enhancing student proficiency and ultimately lead to better patient care outcomes.
Nelson, R. (2020). Informatics: Evolution of the nelson data, information, knowledge and wisdom model: Part 2. OJIN: The Online Journal of Issues in Nursing, 25(3). https://doi.org/10.3912/ojin.vol25no03infocol01
Ting, J., Garnett, A., & Donelle, L. (2021). Nursing education and training on electronic health record systems: An integrative review. Nurse education in practice, 55, 103168. https://doi.org/10.1016/j.nepr.2021.103168
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The DIKW model serves as a framework in nursing informatics to define its scope of practice.
Data: The nurse gathers the patient’s blood pressure and enters this unorganized data in the electronic health records.
Information: This is where the data gathered will be organized. The electronic health records system will display continuous high blood pressure of 140/90.
Knowledge: This is where the nurse analyzes the information and applies critical thinking and clinical reasoning in decision-making. Based on this information, the nurse identifies that the patient may have hypertension.
Wisdom: Since the nurse now decides and knows about the patient's condition, she/he will decide what next step to take. The nurse will provide health education about the symptoms, causes, and possible complications of hypertension. Lifestyle changes can also be taught to the patient. Moreover, the nurse should refer the patient to a physician for further assessment and medication prescription.
Reference:
Nelson, R. (2020, July 21). Informatics: Evolution of the Nelson Data, Information, Knowledge and Wisdom Model: Part 2. Retrieved from https://ojin.nursingworld.org/table-of-contents/volume-25-2020/number-3-september-2020/evolution-of-nelson-model-part-2/
Data: The nurse gathers the patient’s blood pressure and enters this unorganized data in the electronic health records.
Information: This is where the data gathered will be organized. The electronic health records system will display continuous high blood pressure of 140/90.
Knowledge: This is where the nurse analyzes the information and applies critical thinking and clinical reasoning in decision-making. Based on this information, the nurse identifies that the patient may have hypertension.
Wisdom: Since the nurse now decides and knows about the patient's condition, she/he will decide what next step to take. The nurse will provide health education about the symptoms, causes, and possible complications of hypertension. Lifestyle changes can also be taught to the patient. Moreover, the nurse should refer the patient to a physician for further assessment and medication prescription.
Reference:
Nelson, R. (2020, July 21). Informatics: Evolution of the Nelson Data, Information, Knowledge and Wisdom Model: Part 2. Retrieved from https://ojin.nursingworld.org/table-of-contents/volume-25-2020/number-3-september-2020/evolution-of-nelson-model-part-2/
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Hi, Kirsten! I like how you differentiated the data and information as unorganized and organized data. It helped me understand that data is just merely the raw data obtained from the patient, while information is giving meaning to the data.
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One example of an application of nursing informatics to the DIKW model is the utilization of Electronic Health Records, specifically the computerized Registry of Admissions and Discharges (RADISH). Utilizing the DIKW model, the raw data in the utilization of RADISH takes place where individual patient data is retrieved with the use of direct nursing interventions like blood pressure monitoring, vital signs, and the likes. This data would now be analyzed and organized by medical health providers where this raw data is structured on the greater understanding of these professionals. Next, the information would then be processed or organized to provide context and meaning by these electronic health records. There are tools embedded into these programs that are able to detect abnormalities where raw datas are able to show range interpretation, coded by these programs. Lastly, wisdom is achieved through the use of the generated information in the EHRs for the use of nurses, especially in guiding them to create better care plans.
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References:
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Okrepilov, E (n.d.). What is the Data, Information, Knowledge, Wisdom (DIKW) Model?. https://weje.io/blog/data-information-knowledge-wisdom
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DIKW in wearable technology and remote patient monitoring (Mielke, 2024)
In data transformation, let us analyze how vital sign monitoring data is transformed through the DIKW model in the scope of nursing practice. Imagine wearable devices like BP Monitors, pulse oximeters, temperature readers and the alike.
Data - The raw data that come from these wearable devices may include Respiratory rate, Heart rate, BP, temperature, oxygen saturation level, blood glucose levels, sleep hours, and more. All of which do not have conclusive interpretations on a client’s health on their own.
Information - Through organization of data, the daily activities may be plotted with the various vital signs and determine trends and patterns in the client’s health. Morever, the different data can be collected and combined with each other to conclude more information. For example the combination of heart rate, BP, and ECG is more focused towards the status of the heart.
Knowledge - Multiple information can be compared and interrelated to create more conclusive diagnosis on the status of clients. For example, given the client’s heart-related vital signs and the respiratory vital signs, cardiorespiratory conditions can be interrelated and will help clinical practitioners narrow down the pathophysiological problems that affect the client.
Wisdom - Through the synthesized pathophysiological problems from the knowledge process, nurses can apply interventions catered to the clinical signs and symptoms of the patient. Wisdom is the stage where the nurse selects and prioritizes patient-centered interventions that prioritizes compassionate care for the client.
Reference:
Mielke, J. (2024, August). 4 Nursing Informatics Examples & Applications. Nurse.org. Retrieved September 15, 2024, from https://nurse.org/education/nursing-informatics-examples/
In data transformation, let us analyze how vital sign monitoring data is transformed through the DIKW model in the scope of nursing practice. Imagine wearable devices like BP Monitors, pulse oximeters, temperature readers and the alike.
Data - The raw data that come from these wearable devices may include Respiratory rate, Heart rate, BP, temperature, oxygen saturation level, blood glucose levels, sleep hours, and more. All of which do not have conclusive interpretations on a client’s health on their own.
Information - Through organization of data, the daily activities may be plotted with the various vital signs and determine trends and patterns in the client’s health. Morever, the different data can be collected and combined with each other to conclude more information. For example the combination of heart rate, BP, and ECG is more focused towards the status of the heart.
Knowledge - Multiple information can be compared and interrelated to create more conclusive diagnosis on the status of clients. For example, given the client’s heart-related vital signs and the respiratory vital signs, cardiorespiratory conditions can be interrelated and will help clinical practitioners narrow down the pathophysiological problems that affect the client.
Wisdom - Through the synthesized pathophysiological problems from the knowledge process, nurses can apply interventions catered to the clinical signs and symptoms of the patient. Wisdom is the stage where the nurse selects and prioritizes patient-centered interventions that prioritizes compassionate care for the client.
Reference:
Mielke, J. (2024, August). 4 Nursing Informatics Examples & Applications. Nurse.org. Retrieved September 15, 2024, from https://nurse.org/education/nursing-informatics-examples/
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For example, a nurse on the night shift records the results of a postpartum patient's assessment into the hospital’s electronic medical records (EMR). This step represents the "data" level of the DIKW Model.
The next day, a new nurse takes over the care of the patient. While reviewing the patient's vaccination status and comparing it with the standards of care, the new nurse notices that the patient is missing a necessary vaccination. At this point, the nurse uses the recorded data and established care standards to gather information on the patient’s quality of care.
The nurse then examines the patient's chart to check for any contraindications in receiving the vaccine. This review of available information and assessment data provides the nurse with the necessary knowledge to make an informed decision.
Following this, the nurse recommends to the healthcare team for the patient to receive the vaccine, after ensuring it is safe for the patient. The nurse also draws on previous knowledge in postpartum women’s vaccination status and past experience with postpartum care to further research on the topic, in a macro-scale, in hopes of addressing the phenomenon. This process illustrates the application of wisdom, supported by nursing informatics, in making informed healthcare decisions.
The next day, a new nurse takes over the care of the patient. While reviewing the patient's vaccination status and comparing it with the standards of care, the new nurse notices that the patient is missing a necessary vaccination. At this point, the nurse uses the recorded data and established care standards to gather information on the patient’s quality of care.
The nurse then examines the patient's chart to check for any contraindications in receiving the vaccine. This review of available information and assessment data provides the nurse with the necessary knowledge to make an informed decision.
Following this, the nurse recommends to the healthcare team for the patient to receive the vaccine, after ensuring it is safe for the patient. The nurse also draws on previous knowledge in postpartum women’s vaccination status and past experience with postpartum care to further research on the topic, in a macro-scale, in hopes of addressing the phenomenon. This process illustrates the application of wisdom, supported by nursing informatics, in making informed healthcare decisions.
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Hi, Audrelyn! Great points on the demonstration of DIKW model to improve patient care. The transition from data (patient assessment) to wisdom (making informed decisions about vaccinations) is clearly laid out. I also agree with how you highlighted the role of reviewing standards of care and previous experiences to ensure the best outcomes for patients.
Just to add, since you mentionef the capacity tp ehance research on the field. I think we can agree that this not only improves clinical practice, but also supports broader research efforts, as data can be analyzed to identify trends alongside improving care on a larger scale.
Just to add, since you mentionef the capacity tp ehance research on the field. I think we can agree that this not only improves clinical practice, but also supports broader research efforts, as data can be analyzed to identify trends alongside improving care on a larger scale.
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Well done!! Thank you for sharing this example. I like that you used a clinical setting context for your example, it makes it more relevant to our nursing practice and that it's easier to grasp as well.
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The DIKW model or pyramid describes stages which progress into a higher level starting from the lowest “data”, “information”, “knowledge”, until the highest “wisdom” (Ontotext, n.d.). In the aspect of nursing education, any input could be regarded as data. This data then is transformed into information such as when an instructor deliberately shares data towards their students. This information is then turned into knowledge as the student acquires and understands the information. Finally, this knowledge progresses into wisdom as the student fully grasps the information or data at hand especially during specific contexts.
References:
Ontotext. (n.d.). What Is the Data, Information, Knowledge, Wisdom (DIKW) Pyramid? https://www.ontotext.com/knowledgehub/fundamentals/dikw-pyramid/#:~:text=The%20DIKW%20Pyramid%20represents%20the,and%20adds%20value%20to%20it.
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What a short yet insightful summary Ashanti! It truly is wonderful that the DIKW model is able to simplify and guide us in understanding our nursing lessons, especially in case studies where we can utilize this model into analyzing raw data into its highest form- "wisdom"!
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A student nurse is assigned to a patient with a case of Chest Tube Thoracostomy
Data: The student nurse identifies and collects data through Nursing Health History and Physical Examination.
Information: The raw data collected is organized and interpreted by comparing the patient’s vital signs and drainage output to normal ranges or expected outcomes postoperatively.
Knowledge: Use the information to understand the patient’s current condition and response to the chest tube thoracostomy. Apply knowledge of chest tube management, potential complications, and standard care protocols.
Wisdom: Make informed clinical decisions based on a comprehensive understanding of the patient’s condition and apply appropriate nursing practices to ensure optimal care for the patient.
References:
Nelson, R. (2020) Informatics: Evolution of the Nelson Data, Information, Knowledge and Wisdom Model: Part 2. OJIN: The Online Journal of Issues in Nursing, 25(3). https://doi.org/10.3912/OJIN.Vol25No03InfoCol01
Data: The student nurse identifies and collects data through Nursing Health History and Physical Examination.
Information: The raw data collected is organized and interpreted by comparing the patient’s vital signs and drainage output to normal ranges or expected outcomes postoperatively.
Knowledge: Use the information to understand the patient’s current condition and response to the chest tube thoracostomy. Apply knowledge of chest tube management, potential complications, and standard care protocols.
Wisdom: Make informed clinical decisions based on a comprehensive understanding of the patient’s condition and apply appropriate nursing practices to ensure optimal care for the patient.
References:
Nelson, R. (2020) Informatics: Evolution of the Nelson Data, Information, Knowledge and Wisdom Model: Part 2. OJIN: The Online Journal of Issues in Nursing, 25(3). https://doi.org/10.3912/OJIN.Vol25No03InfoCol01
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In reply to Nathalie Bianca Gutierrez
Re: Use of DIKW in NI Applications
What a stellar example, Nathalie! I admire how you contextualized and integrated our learnings from N105 to nursing informatics. Truly, most of the procedures and nursing actions utilize the DIKW model, and we have all probably used it inadvertently. Now, we are able to use this model with more awareness to guide our future nursing actions, especially those involving certain technology, like encoding laboratory values and the like to our spreadsheets in order to optimize our work. Thank you for this creative answer!
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One example of the application of nursing informatics I can think of is the use of electronic health records such as the RADISH in PGH for clinical decision-making. The data transforms through the DIKW model as follows:
Data: Unprocessed information about the patient is recorded. These include patient demography, vital signs, medical history, and laboratory results.
Information: The data gathered is now organized and interpreted. For example, a patient has a chief complaint of dyspnea. Currently, she still experiences dyspnea and has an RR of 30 cpm, and SpO2 of 85%, even at rest, which are interpreted together to identify a trend.
Knowledge: From the obtained information, the nurse can then analyze the patient's information that even without exertion, the patient experiences difficulty breathing, which may indicate an underlying respiratory condition.
Wisdom: With the nurse’s knowledge, she can then collaborate with other healthcare professionals in decision-making to improve the patient’s condition. She then can perform interventions such as administering medications as ordered by the physician to address the underlying cause, providing supplemental oxygen, aiding in proper positioning, and assisting with deep breathing exercises, among others, which are also reflected in the electronic health record.
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The DIKW framework—data, information, knowledge, wisdom—established by Graves and Corcoran in Cato (2020), has been utilized to illustrate the influence of nursing informatics on nursing practice and care delivery, complementing the American Nurses Association's Nursing Informatics: Scope and Standards of Practice, which describes nursing informaticists as playing a role in cultivating wisdom for the entire nursing profession.
For instance, in nursing research about diabetes, the DIKW framework can be effectively applied to leverage predictive analytics to form relevant discussions and recommendations.
1. Data: Raw data might include patient demographics (such as age and gender), blood glucose levels, medication regimens, lifestyle factors (like diet and exercise), and documented complications.
2. Information: Using this data, researchers can create predictive models that estimate a patient’s risk of developing diabetes based on their health metrics and lifestyle choices. For instance, it might lead them to infer that individuals with high body mass index (BMI) and a family history of diabetes have a significantly elevated risk of developing diabetes themselves.
3. Knowledge: The model created can then produce deeper insights, such as the association between specific genetic markers and an increased risk of diabetes. Researchers might also discover that the risk increases notably with the accumulation of multiple risk factors.
4. Wisdom: The knowledge gained can then help public health officials design targeted diabetes prevention programs tailored for high-risk populations identified by the model, or advocate for policy reforms that encourage healthier eating habits and more physical activity to lower the overall incidence of diabetes.
References:
- Cato, K. D., McGrow, K., & Rossetti, S. C. (2020). Transforming clinical data into wisdom. Nursing Management, 51(11), 24–30. https://doi.org/10.1097/01.numa.0000719396.83518.d6
- Risk factors for diabetes. (2024, February 3). National Institute of Diabetes and Digestive and Kidney Diseases. https://www.niddk.nih.gov/health-information/professionals/clinical-tools-patient-management/diabetes/game-plan-preventing-type-2-diabetes/prediabetes-screening-how-why/risk-factors-diabetes
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Great examples, Gie! Nursing informatics provides a way to work efficiently from start to finish of our care. It also helps us see the population in a wider perspective so that we can better address their healthcare needs.
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USE OF DIKW IN NI APPLICATIONS
An example of the application of nursing informatics in clinical practice is the use of Electronic Health Records (EHRs), which enable healthcare providers to access and manage patient information digitally. Using the DIKW model, we can understand how data progresses to wisdom, becoming actionable insights in clinical settings.
At the base level, data refers to raw, unprocessed facts collected through EHRs, such as individual vital signs, laboratory results, or medication histories. For example, a patient's numerical lab values are initially just isolated figures without context.
When data is processed and organized, it transforms into information. For instance, a series of lab values over time can be analyzed to determine if they fall within normal ranges or indicate potential issues. This interpretation helps clinicians recognize patterns and trends, such as whether a patient's condition is improving or worsening based on the lab values.
This information is then used to develop knowledge. For example, if a patient's lab values consistently fall outside normal ranges, healthcare providers might interpret this as a sign of a particular condition or a response to treatment. This can guide clinicians to make decisions such as recommending further diagnostic tests or adjusting treatment plans.
Finally, this knowledge turns into wisdom when clinicians apply their understanding to make informed decisions about patient care, such as initiating a new treatment plan or recommending additional diagnostic tests. This wisdom improves patient outcomes based on a comprehensive analysis of the data.
Reference:
American Nurse. (n.d.). Nursing informatics: EHR and beyond. American Nurse. Retrieved September 15, 2024, from https://www.myamericannurse.com/nursing-informatics-ehr-beyond/
An example of the application of nursing informatics in clinical practice is the use of Electronic Health Records (EHRs), which enable healthcare providers to access and manage patient information digitally. Using the DIKW model, we can understand how data progresses to wisdom, becoming actionable insights in clinical settings.
At the base level, data refers to raw, unprocessed facts collected through EHRs, such as individual vital signs, laboratory results, or medication histories. For example, a patient's numerical lab values are initially just isolated figures without context.
When data is processed and organized, it transforms into information. For instance, a series of lab values over time can be analyzed to determine if they fall within normal ranges or indicate potential issues. This interpretation helps clinicians recognize patterns and trends, such as whether a patient's condition is improving or worsening based on the lab values.
This information is then used to develop knowledge. For example, if a patient's lab values consistently fall outside normal ranges, healthcare providers might interpret this as a sign of a particular condition or a response to treatment. This can guide clinicians to make decisions such as recommending further diagnostic tests or adjusting treatment plans.
Finally, this knowledge turns into wisdom when clinicians apply their understanding to make informed decisions about patient care, such as initiating a new treatment plan or recommending additional diagnostic tests. This wisdom improves patient outcomes based on a comprehensive analysis of the data.
Reference:
American Nurse. (n.d.). Nursing informatics: EHR and beyond. American Nurse. Retrieved September 15, 2024, from https://www.myamericannurse.com/nursing-informatics-ehr-beyond/
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In reply to APRILLE BANAYAT
Re: Use of DIKW in NI Applications
Use of DIKW in NI Applications
Provide an example of the application of nursing informatics in either education, practice, or research and explain how the data transforms using the DIKW model.
Data: In assessing a pregnant patient, her blood pressure, maternal weight, age, and the number of prenatal visits are collected.
Information: To make sense of this data, it is organized and processed. For instance, acquiring a blood pressure reading of 140/90 would indicate that the patient is at risk of preeclampsia. Meanwhile, a total of 2 prenatal visits during the patient’s third trimester would indicate that she did not have enough prenatal visits.
Knowledge: Upon analysis of the previously stored data, such as a blood pressure measurement of 140/90, the medical professional would be aware that a patient with raised blood pressure during pregnancy is considered to be at high risk and should receive close monitoring and early intervention.
Wisdom: By gaining more information about the patient's condition, the healthcare professional can decide on the best course of action for the pregnant patient, including educating her on dietary and lifestyle changes that can help prevent complications.
Provide an example of the application of nursing informatics in either education, practice, or research and explain how the data transforms using the DIKW model.
Data: In assessing a pregnant patient, her blood pressure, maternal weight, age, and the number of prenatal visits are collected.
Information: To make sense of this data, it is organized and processed. For instance, acquiring a blood pressure reading of 140/90 would indicate that the patient is at risk of preeclampsia. Meanwhile, a total of 2 prenatal visits during the patient’s third trimester would indicate that she did not have enough prenatal visits.
Knowledge: Upon analysis of the previously stored data, such as a blood pressure measurement of 140/90, the medical professional would be aware that a patient with raised blood pressure during pregnancy is considered to be at high risk and should receive close monitoring and early intervention.
Wisdom: By gaining more information about the patient's condition, the healthcare professional can decide on the best course of action for the pregnant patient, including educating her on dietary and lifestyle changes that can help prevent complications.
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Hi Hazeleen! You provided a great explanation on how data transforms using the DIKQ model
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As a student nurse, an example of the application of nursing informatics using the DIKW model (Data, Information, Knowledge, and Wisdom) in nursing education is the learning management system platform (LMS) that we use in our courses. Currently, we are using Canvas and VLE for accessing our course materials, assignments, checking announcements, and even to communicate with our professors. I gave this as an example because I can imagine how the DIKW model applies to the platforms that we use to facilitate our learning as future nursing practitioners. The ‘D’ or Data refers to the materials provided by our professors, from lecture slides to readings or some lecture recordings to watch. This data is then organized into topics and subtopics with mini activities like discussion forums, turning it into information that reinforces our learning. As we engage more with this information through studying, we then build ‘K’ or knowledge. Finally, the ‘W’ or wisdom in DIKW is what we gain as we apply this knowledge in our nursing practice.
Reference
Nelson, R. (2020, July 21). Informatics: Evolution of the Nelson Data, Information, Knowledge and Wisdom Model: Part 2. OJIN: The Online Journal of Issues in Nursing, 25(3). https://doi.org/10.3912/OJIN.Vol25No03InfoCol01
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The example you shared is insightful, Abegail! I appreciate how you mentioned the platforms we use in this program, such as VLE and Canvas. This really helps me clarify the DIKW model in the context of nursing education.
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The DIKW system stands for Data, Information, Knowledge, and Wisdom. It is a hierarchical model used to describe how raw data is transformed into meaningful wisdom for decision-making. In nursing informatics, this framework is applied to manage and analyze patient data effectively. Data represents raw facts, such as a patient’s vital signs. When organized and contextualized, it becomes information, such as identifying a pattern of high blood pressure. Knowledge emerges when information is combined with clinical expertise to determine potential health risks or interventions. Finally, wisdom is achieved by applying this knowledge to make sound decisions, like implementing a personalized care plan.
In practice, nurses use the DIKW system to improve patient outcomes and streamline care processes. For example, electronic health records (EHRs) collect data, which can be processed into actionable insights like alerts for medication interactions (information). Nurses interpret these insights using clinical judgment (knowledge) and apply them to make patient-centered decisions (wisdom).
In practice, nurses use the DIKW system to improve patient outcomes and streamline care processes. For example, electronic health records (EHRs) collect data, which can be processed into actionable insights like alerts for medication interactions (information). Nurses interpret these insights using clinical judgment (knowledge) and apply them to make patient-centered decisions (wisdom).
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Hi, Raf! This is a great explanation of the DIKW model and its relevance to nursing informatics! It clearly demonstrates how raw data transforms into actionable wisdom within the healthcare setting. By effectively utilizing this framework, nurses can make more informed decisions and provide better, more personalized care to their patients.
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You made an easy and concise explanation of the use of nursing informatics, AD. This model is indeed helpful in all areas of nursing practice, research, and education. Hopefully this practice will thrive presently and in the future.
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