The Application of Data to Problem-Solving in Nursing: Informatics Scenario on Inpatient Falls

Data, informatics and the nurse as a knowledge worker

Like the stockbroker and the meteorologist, nurses make better decisions when they have the right data at the right time. Nursing informatics integrates nursing science with information and analytical sciences to identify, define, manage and communicate data, information, knowledge and wisdom in nursing practice (American Nurses Association [ANA], 2022). Nurses are knowledge workers: their value lies not in collecting numbers, but in interpreting them and turning them into action that improves care (McGonigle & Mastrian, 2022).

The Data–Information–Knowledge–Wisdom (DIKW) framework explains how this happens. Raw data are organized into information; patterns in information become knowledge; and applying knowledge wisely to make the right decision becomes wisdom (Nelson & Staggers, 2018). The scenario below shows each step in action. For another worked example using heart failure readmissions, see our reflection on informatics and knowledge work.

Choosing a scenario: patient, staff or management problems

The assignment allows a patient, staff or management problem. Strong scenarios are specific, measurable and important to your unit. Here are ideas to help you choose:

This article uses a patient safety scenario, inpatient falls, because falls are common, costly, nurse-sensitive and rich in data.

1. The focus of the scenario

This scenario is hypothetical and used for illustration.

Why it matters: falls are among the most common adverse events in hospitals, and falls with injury lead to pain, loss of independence, longer stays and higher costs (Agency for Healthcare Research and Quality [AHRQ], 2013). The Centers for Medicare & Medicaid Services does not pay for the extra costs of certain hospital-acquired conditions, including falls with injury, so the problem matters to patients, staff and leaders alike.

2. The data that could be used

Example calculation (hypothetical): 9 falls ÷ 2,700 patient days × 1,000 = 3.3 falls per 1,000 patient days. Comparing this rate with national benchmarks for similar units shows whether the unit is an outlier.

3. How the data might be collected and accessed

• Electronic health record (EHR): fall risk scores, assessments, medication administration records, care plans and rounding documentation. The nurse manager would request a report through the clinical informatics or quality department.
• Incident reporting system: details of each fall, including time, activity, injury and contributing factors, plus post-fall huddle notes.
• Nurse call and bed alarm systems: time-stamped logs of call light activations, response times and alarm events.
• Staffing and scheduling systems: staffing levels, skill mix and overtime for each shift on which a fall occurred.
• Benchmarking databases: national nursing quality databases allow comparison with similar units.
• Direct observation and audits: spot checks of bed alarms, call light placement, non-slip footwear and rounding; staff and patient interviews to capture what the numbers miss.
• Dashboards: informatics teams can build a unit fall dashboard that updates monthly or in near real time.
• Privacy and governance: access must follow HIPAA, organizational data governance and role-based access, using the minimum necessary data. Results shared with staff should be aggregated and de-identified, and incident reports should be used for learning, not blame, to support a just culture.

4. Knowledge that might be derived from the data

When the data are organized and analyzed, patterns emerge that turn information into knowledge. In this hypothetical scenario, the analysis might show that:

• Seven of nine falls occurred between 11 p.m. and 6 a.m., most while patients were trying to use the toilet unassisted.
• Most patients who fell had received a diuretic or a sedative-hypnotic in the evening, increasing night-time urgency and unsteadiness.
• Hourly rounding was documented, but call light data showed average night response times over 6 minutes, suggesting that documentation did not always reflect practice.
• Bed alarms were off in four falls, often because alarms had been silenced and not reset.
• Falls clustered on nights with float or agency staff and high admission volume.
• Fall risk scores were completed, but care plans were generic, not tailored to each patient’s specific risks.

This knowledge reframes the problem. The unit doesn’t need “more fall precautions”; it needs targeted, individualized prevention: scheduled toileting rounds at night, reviewing the timing of diuretics and sedatives with pharmacy and providers, alarm checks at shift handoff, orientation for float staff, and patient-specific care plans shared with patients and families. Evidence supports this approach: a fall prevention toolkit that linked each patient’s specific risk factors to tailored interventions reduced falls in acute care hospitals, especially among patients aged 65 and older (Dykes et al., 2010).

The experience also adds to nursing knowledge more broadly. The unit learns which risk factors matter most for its patients, and when shared through quality reports, conferences or publication, these lessons help other units and the profession.

5. How a nurse leader uses clinical reasoning and judgment

Data alone don’t solve problems. The nurse leader’s clinical reasoning (the thinking process used to analyze a situation) and clinical judgment (the conclusion and decision reached) turn data into trustworthy knowledge. Levett-Jones and colleagues’ clinical reasoning cycle offers a useful structure (Levett-Jones et al., 2010):

The nurse leader’s judgment also shapes how knowledge is formed in several important ways:

• Context: clinical experience helps the leader interpret numbers. For example, a high fall risk score in a patient with delirium means something different than in an alert patient after surgery.
• Skepticism about data quality: documented rounding that doesn’t match call light data prompts questions rather than acceptance.
• Balancing safety and patient rights: avoiding restrictive measures, such as restraints or immobility, that cause harm while preventing falls.
• Equity and ethics: checking whether some groups, such as patients with limited English proficiency or cognitive impairment, are at higher risk, and protecting privacy.
• Staff engagement: using findings to support, not blame, staff, and considering workload so changes are sustainable.

See our guides to the PDSA practice improvement plan and diabetic data analysis and visualization to take the next steps.

Sample discussion post

In my organization, a 32-bed medical-surgical unit experienced nine patient falls in one quarter, three with injury, despite a fall prevention policy. The focus of my scenario is understanding why patients continue to fall and which interventions would prevent future falls. Useful data would include fall incident reports (time, activity, injury), patient data from the EHR (Morse Fall Scale scores, cognition, toileting needs), medications given before each fall, documentation of hourly rounding and bed alarm use, nurse call system response times, and staffing levels on each shift. These data could be accessed through the EHR, the incident reporting system, nurse call logs and staffing systems, with support from our clinical informatics team and in line with HIPAA and data governance policies. Analyzing the data might reveal that most falls occur at night during unassisted toileting, among patients who received evening diuretics or sedatives, on shifts with delayed call light response. This knowledge would guide targeted interventions such as scheduled night-time toileting rounds, medication timing review and individualized care plans. As a nurse leader, I would use clinical reasoning to interpret these patterns in context, question the accuracy of documentation, combine the data with staff and patient input and current evidence, and lead an interprofessional team to test changes using PDSA cycles. Evaluating and reflecting on the results would turn this experience into lasting knowledge for our unit and our profession.

Conclusion

Just as other professions depend on data, nurses use data to solve problems and expand knowledge. In this hypothetical falls scenario, data from the EHR, incident reports, nurse call systems and staffing records, accessed securely through informatics teams, could reveal the true causes of patient falls. A nurse leader who applies clinical reasoning and judgment, by questioning data quality, interpreting patterns in context, involving the team and evaluating results, transforms raw data into knowledge and wisdom that make care safer.

References

Agency for Healthcare Research and Quality. (2013). Preventing falls in hospitals: A toolkit for improving quality of care. https://www.ahrq.gov/patient-safety/settings/hospital/fall-prevention/toolkit/index.html

American Nurses Association. (2022). Nursing informatics: Scope and standards of practice (3rd ed.). American Nurses Association.

Dykes, P. C., Carroll, D. L., Hurley, A., Lipsitz, S., Benoit, A., Chang, F., Meltzer, S., Tsurikova, R., Zuyov, L., & Middleton, B. (2010). Fall prevention in acute care hospitals: A randomized trial. JAMA, 304(17), 1912–1918. https://doi.org/10.1001/jama.2010.1567

Levett-Jones, T., Hoffman, K., Dempsey, J., Jeong, S. Y.-S., Noble, D., Norton, C. A., Roche, J., & Hickey, N. (2010). The “five rights” of clinical reasoning: An educational model to enhance nursing students’ ability to identify and manage clinically “at risk” patients. Nurse Education Today, 30(6), 515–520. https://doi.org/10.1016/j.nedt.2009.10.020

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.