Reflection on Informatics and Knowledge Work: Using Data, the DIKW Framework and Nurse Leader Clinical Judgment (Example Scenario)
8 min read Question: Reflection on the concepts of informatics and knowledge work
Consider a hypothetical scenario based on your own healthcare practice or organization that would require or benefit from the access/collection and application of data. Describe the data that could be used and how the data might be collected and accessed. What knowledge might be derived from that data? How would a nurse leader use clinical reasoning and judgment in the formation of knowledge from this experience?
Introduction: nurses as knowledge workers
Peter Drucker coined the term “knowledge worker” for people whose main asset is what they know and how they apply it. Nurses are knowledge workers. Every shift, they gather data, interpret it, combine it with experience and evidence, and act on it to keep patients safe (McGonigle & Mastrian, 2022). Nursing informatics supports this work by integrating nursing science with information and computer science to manage and communicate data, information, knowledge and wisdom in practice.
A useful way to understand this process is the Data–Information–Knowledge–Wisdom (DIKW) framework (Nelson & Staggers, 2018):
Raw, unorganized facts, e.g. “weight 82 kg”
Data organized and interpreted, e.g. “weight up 2 kg in 2 days”
Patterns and relationships, e.g. “rapid weight gain predicts HF decompensation”
Using knowledge appropriately, e.g. “design a home weight-monitoring program”
Hypothetical scenario: heart failure readmissions
On a 28-bed cardiac telemetry unit in a community hospital, the nurse manager notices that many patients with heart failure (HF) return within 30 days of discharge. Staff report that patients often come back short of breath and fluid overloaded, saying they “didn’t know the weight gain mattered” or “couldn’t afford the new medication.” High readmission rates harm patients, frustrate staff, and expose the hospital to Medicare payment penalties under the Hospital Readmissions Reduction Program. The nurse manager wants to understand why patients are being readmitted, and what nursing can do about it.
This scenario is hypothetical and is used to illustrate the concepts.
What data could be used?
| Data category | Examples |
|---|---|
| Patient demographics | Age, sex, race and ethnicity, preferred language, insurance, zip code, living situation |
| Clinical data | Ejection fraction, BNP or NT-proBNP, daily weights, intake and output, vital signs, comorbidities (diabetes, CKD, COPD), length of stay |
| Medication data | Discharge medications (diuretics, guideline-directed therapy), number of medications, changes at discharge, pharmacy refill data, cost and coverage |
| Nursing documentation | Discharge education completed, teach-back results, health literacy screening, functional status, fall risk |
| Care transition data | Follow-up appointment scheduled within 7 days, post-discharge phone call completed, home health or cardiac rehab referral |
| Social determinants of health | Transportation, food insecurity, ability to afford medications, caregiver support |
| Outcome data | 30-day readmissions (including to other hospitals), ED visits, mortality, patient experience (HCAHPS discharge-information scores) |
| Remote patient data | Home weights, blood pressure and symptoms from Bluetooth scales or a patient portal |
How the data could be collected and accessed
- Electronic health record (EHR): most clinical, medication and nursing documentation data already exist in the EHR. The nurse manager could request a report from the clinical informatics or quality team, using the clinical data warehouse, for all HF discharges over the past 12 months.
- Structured documentation: adding required fields for teach-back, health literacy and SDOH screening would make the data consistent and reportable, rather than buried in free-text notes.
- Admission-discharge-transfer (ADT) and health information exchange (HIE) data: these capture readmissions to other hospitals, which are often missed.
- Pharmacy and claims data: refill records show whether patients picked up their medications.
- Remote patient monitoring platforms and the patient portal: collect daily home weights and symptoms after discharge.
- Dashboards: informatics teams can build a unit dashboard showing readmission rates and process measures in near real time.
- Privacy and security: access must follow HIPAA and organizational policy, using role-based access and the “minimum necessary” standard. Data used for quality improvement should be de-identified or aggregated where possible.
What knowledge might be derived from the data?
By organizing the data into information, such as comparing readmitted and non-readmitted patients, and looking for patterns, the team could develop knowledge such as:
- Patients who did not have a follow-up appointment within 7 days of discharge were readmitted far more often.
- Readmitted patients more often screened positive for low health literacy or had incomplete teach-back documentation.
- Many readmitted patients had a weight gain of 2–3 kg in the days before readmission that was not reported to anyone, showing a gap in self-monitoring.
- Patients discharged on new, costly medications often did not fill their prescriptions.
- Readmissions clustered among patients discharged on Fridays, when follow-up calls were less consistent, and among those living alone.
This knowledge is consistent with evidence that transitional care interventions, such as nurse home visits, structured telephone follow-up and HF clinics, reduce readmissions and mortality for HF patients (Van Spall et al., 2017). It points directly to nursing actions: structured teach-back, daily-weight education with scales provided, 7-day follow-up appointments made before discharge, medication affordability checks, post-discharge calls within 48–72 hours, and remote weight monitoring for high-risk patients.
How a nurse leader uses clinical reasoning and judgment to form knowledge
Data and dashboards do not create knowledge on their own. A nurse leader’s clinical reasoning and judgment turns information into meaningful, actionable knowledge. Tanner’s (2006) Clinical Judgment Model of noticing, interpreting, responding and reflecting provides a useful structure:
| Step | How the nurse leader applies it |
|---|---|
| Noticing | Recognizes the pattern of repeat HF admissions from bedside experience and staff concerns, and decides it is worth investigating with data. |
| Interpreting | Analyzes the data in context: checks data quality and completeness, looks for confounders (for example, sicker patients may be both less educated and more likely to be readmitted), and combines the data with clinical expertise, staff and patient input, and published evidence. |
| Responding | Leads an interprofessional team (nursing, cardiology, pharmacy, case management, informatics) to design and implement evidence-based changes, such as a nurse-led transitional care bundle tested with PDSA cycles. |
| Reflecting | Evaluates the results with ongoing data, reflects on what worked and why, refines the approach, and shares the learning across the organization. This is the step that turns knowledge into wisdom. |
Nurse leaders also apply ethical judgment, protecting privacy, avoiding biased interpretations of data, and ensuring that changes promote equity rather than widen gaps for patients with language, literacy or financial barriers. They also consider the effect on staff workload, so that new documentation or monitoring tasks are sustainable and support the Quadruple Aim. See our article on the Triple and Quadruple Aim.
Sample reflection (first person)
In my practice on a cardiac telemetry unit, I often care for patients with heart failure who return within weeks of discharge. Applying informatics would allow our team to move from anecdotes to evidence. By pulling EHR data on discharge education, follow-up appointments, daily weights and medication fills, and linking it with readmission data, we could identify which patients are most at risk and why. As a nurse leader, I would use clinical reasoning to interpret these patterns in context, combining the data with my clinical experience, staff and patient perspectives, and current evidence on transitional care. This knowledge would guide a nurse-led intervention, such as teach-back education, scheduled follow-up before discharge and post-discharge calls. I would then evaluate the results and refine the approach. This process shows how nurses, as knowledge workers, transform data into information, knowledge and, ultimately, wisdom that improves patient outcomes.
Conclusion
Informatics gives nurses the tools to access and use data, but it is the nurse’s clinical reasoning and judgment that transform that data into knowledge and wisdom. In this hypothetical HF readmission scenario, EHR, transition-of-care, pharmacy, SDOH and remote monitoring data, accessed securely through informatics teams and dashboards, could reveal why patients return to the hospital. A nurse leader who notices the problem, interprets the data thoughtfully, responds with evidence-based interventions and reflects on the results shows the value of nurses as knowledge workers in improving quality, safety and outcomes.
References
McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning.
Nelson, R., & Staggers, N. (2018). Health informatics: An interprofessional approach (2nd ed.). Elsevier.
Tanner, C. A. (2006). Thinking like a nurse: A research-based model of clinical judgment in nursing. Journal of Nursing Education, 45(6), 204–211. https://doi.org/10.3928/01484834-20060601-04
Van Spall, H. G. C., Rahman, T., Mytton, O., Ramasundarahettige, C., Ibrahim, Q., Kabali, C., Coppens, M., Haynes, R. B., & Connolly, S. (2017). Comparative effectiveness of transitional care services in patients discharged from the hospital with heart failure: A systematic review and network meta-analysis. European Journal of Heart Failure, 19(11), 1427–1443. https://doi.org/10.1002/ejhf.765
