NUR-514 Topic 5: sample paper, in real form

Embedding a Deterioration Risk Score in the Electronic Health Record: A Leadership, Workflow and Data Governance Plan for Six Adult Acute Care Areas

[Author Name]

College of Nursing and Health Care Professions, Grand Canyon University

NUR-514: Organizational Leadership and Informatics

Topic 5 Assignment

[Instructor Name]

August 11, 2026

Composite scenario written as a model document. No real hospital, vendor, clinician or patient is described.

The Case for Change

Mercy Ridge Regional Medical Center is a composite 268-bed community hospital with 214 adult acute care beds arranged in six areas, four medical-surgical and two progressive care, staffed by 612 registered nurses. In the 12 months ending June 30 the hospital recorded 143 unplanned transfers from acute care to the intensive care area, a rate of 2.4 per 1,000 acute care patient days. A structured review of 60 randomly drawn transfer records found that 41 of them, or 68.3 percent, carried at least two abnormal vital sign sets documented six or more hours before the transfer with no rapid response activation in between. The warning was present in the record and nobody was positioned to read it.

The signal is fragmented rather than absent. Vital signs live in a flowsheet, laboratory values in a results tab, mental status in a narrative note, and no one view assembles them. Escalation criteria are single-threshold and depend on one nurse holding five or six patients across a 12-hour shift and noticing a pattern that develops over two shifts. Rapid response activation runs at 8.9 calls per 1,000 patient days, at the low end of what mature response systems report, and low activation is itself a warning sign rather than a mark of a calm hospital (Agency for Healthcare Research and Quality, 2019). Nothing about that pattern is corrected by asking nurses to be more vigilant.

The proposed change assembles the signal and routes it. A risk score native to the electronic health record recomputes every 15 minutes from vital signs, selected laboratory values and documented mental status, appears as a column on the patient list, and routes an alert to the assigned nurse and the charge nurse when it crosses a set threshold. Automated identification of this kind has been implemented at scale with a defined nursing response and better outcomes than usual care (Escobar et al., 2020). The score itself writes no orders and replaces no judgment. The deliverable of this change is not a number on a screen but a documented response inside thirty minutes.

Workflow Before and After

The current state runs as follows. A patient care technician records vital signs at 0600, 1400 and 2200 and enters them into the flowsheet in a batch at the end of rounds, often 40 to 60 minutes after the readings were taken. The assigned nurse sees the values at the next charting stop. An abnormal value prompts a judgment call with no defined next action, the charge nurse rounds twice a shift, and escalation is a telephone call placed at the nurse's discretion. In the 60 records reviewed, median elapsed time from the first abnormal set to a rapid response call was 7 hours and 20 minutes.

The future state replaces discretion with a short defined sequence. The score recomputes every 15 minutes. A score at or above threshold routes at the same moment to the assigned nurse's handheld device and to the charge nurse's queue, so the second reader exists by design rather than by luck. The nurse completes a six-item bedside evaluation covering mental status, respiratory rate and effort, perfusion, pain, intake and output, and time of last provider contact, then documents one of three dispositions: continue with a recheck in two hours, contact the provider, or activate rapid response. The charge nurse clears any alert without a documented disposition every four hours.

Two upstream changes make the sequence work and are easy to miss when a plan describes only the alert. Vital sign entry moves to the bedside at the time of measurement, because a score computed on values entered an hour late is a score that describes the past. Shift handoff moves to follow a vital sign set rather than precede it. Threshold calibration is the third design decision: the local threshold is set so that no area receives more than six alerts in 24 hours, at which point roughly one alert in five ends in transfer. An alert that costs a nurse ten minutes to dismiss will be dismissed without being read, so the evaluation template is built to be completed in two minutes at the bedside.

Data Governance, Access and Oversight

Governance is a named body with a named chair, not a promise of oversight. A data stewardship committee chaired by the chief nursing informatics officer, with the chief medical information officer, two nursing directors, an informatics nurse specialist, a compliance officer, a quality analyst and two direct care nurses, meets monthly and owns four artifacts: the score definition, the threshold, the access list and a dated change log. Every threshold adjustment is versioned, justified in writing and announced to the areas before it takes effect, so no clinician discovers a changed alert pattern by living through it. Informatics roles of this kind are now standard in hospitals of this size (Healthcare Information and Management Systems Society, 2020).

Access follows the minimum necessary rule rather than convenience. The score is visible to clinicians attached to the patient in the record, to the charge nurse for the area, and to the rapid response nurse, and it is not visible on any organization-wide dashboard that names patients. The analytics feed shared with the vendor carries no direct identifiers, and no retraining of a commercial model on this hospital's data occurs without written committee approval. Access logs are reviewed monthly against a sample of 25 records. Downtime procedure, ownership of the alert configuration and a written statement that the score generates no orders are documented against the electronic health record safety guidance published by the Office of the National Coordinator for Health Information Technology (2024).

Validation and drift monitoring keep the tool honest after go-live. The score is validated locally on 27,400 adult acute care admissions across 24 months before it is switched on, and performance is reported quarterly, overall and stratified by race, age, sex and primary language, because a model can perform well in aggregate while performing badly for a group inside it (Obermeyer et al., 2019). Input review matters as much as output review, since bias reaches a model through the variables it treats as proxies. The score inherits the documentation habits of the areas that feed it, so a floor that charts a respiratory rate of 18 by default will always look calm.

Leadership Approach, Adoption Measures and Risks

Sequencing follows a stage-based change model in which urgency, a guiding coalition and visible early wins precede any organization-wide rollout (Kotter, 2012). Urgency is built on the hospital's own figure of 41 in 60 records rather than a national statistic, because staff argue with published averages and rarely argue with their own charts. The guiding coalition is 12 direct care nurses, two from each area, chosen for credibility rather than title. Sponsorship is named in the plan: the chief nursing officer owns the outcome, the chief nursing informatics officer owns the build, and area directors own the staffing and schedule changes the new sequence requires.

The score is switched on in one progressive care area for 60 days before any other area receives it, and the results of that period, including the parts that go badly, are published to all six areas. Adoption is measured, not assumed. Four measures are reported monthly to the committee and to each area by name: documented evaluation within 30 minutes of an alert, target 80 percent by day 90; median alert-to-documentation time, target under 20 minutes; alert burden per area per 24 hours, ceiling six; and unplanned transfers to intensive care per 1,000 acute care patient days against the baseline of 2.4. Rapid response activations are tracked as a balancing measure and are expected to rise.

Three risks carry named responses. Alert fatigue is addressed by the burden ceiling and by a standing agenda item at which the committee reviews dismissal rates by area. An early rise in transfers is expected and is interpreted as improved detection for the first two reporting cycles rather than as harm, a decision recorded in advance so it cannot be reinterpreted later. Dependence on a single vendor is limited by keeping the score definition, threshold and change log as hospital-owned documents. A stop rule closes the plan: if documented evaluations sit below 50 percent at day 90 in an area, the score is switched off there and the workflow is rebuilt with that area's nurses rather than pushed harder.

References

Agency for Healthcare Research and Quality. (2019). Rapid response systems. PSNet patient safety primer. U.S. Department of Health and Human Services. https://psnet.ahrq.gov

Escobar, G. J., Liu, V. X., Schuler, A., Lawson, B., Greene, J. D., & Kipnis, P. (2020). Automated identification of adults at risk for in-hospital clinical deterioration. New England Journal of Medicine, 383(20), 1951-1960.

Healthcare Information and Management Systems Society. (2020). 2020 HIMSS nursing informatics workforce survey. https://www.himss.org

Kotter, J. P. (2012). Leading change. Harvard Business Review Press.

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342

Office of the National Coordinator for Health Information Technology. (2024). SAFER guides: Safety assurance factors for EHR resilience. U.S. Department of Health and Human Services. https://www.healthit.gov/topic/safety/safer-guides