NR 558 Week 8 Technology Impact Paper Example

Technology Impact Paper: How the Judgment "Is This Patient Likely to Fall This Shift?" Moved From the Nurse to a Pre-Filled Score and a Threshold Rule, What the Transfer Gained, What It Cost and What Should Move Back

Student Name

College of Nursing, Chamberlain University

NR 558: Informatics for Professional Nursing Practice

Instructor Name

Month Day, Year

Technology Impact Paper: How the Judgment "Is This Patient Likely to Fall This Shift?" Moved From the Nurse to a Pre-Filled Score and a Threshold Rule, What the Transfer Gained, What It Cost and What Should Move Back

One Judgment

This paper examines a single clinical judgment: whether a hospitalized adult is likely to fall during this shift, and therefore what the nurse should do to prevent it. Over the past decade on our medical-surgical unit, that judgment has moved from the nurse to a combination of settings in the electronic health record: a Morse Fall Scale section that opens already holding the last shift's answers, a calculated total and a decision support rule that fires a banner and a suggested order set when the total reaches 45. The paper asks what that transfer gained, what it cost and whether any part of the judgment should move back.

The Earlier State, Unimproved

Before the electronic record, nurses on the unit completed a paper Morse form on admission and, in principle, each shift. In practice, as senior nurses describe it, the form was completed on admission and often not again unless something happened. Fall prevention depended on the nurse's own impression: a patient who seemed unsteady got a closer eye, a bed near the station or a request for a sitter. That approach had strengths. It responded to what the nurse saw, including things the scale does not capture, such as urgency to toilet at night or a patient's refusal to use the call light. It also had weaknesses. It varied from nurse to nurse, left no reliable record and gave newer nurses little structure. Describing the earlier state honestly matters, because a flattering picture of the old practice would make any comparison meaningless. The old practice was not a golden age; it was inconsistent and largely undocumented.

What the Transfer Gained

The electronic system brought three real gains. First, a score is now recorded every shift for almost every patient, where before it was often recorded only on admission. Second, the calculation is automatic and free of arithmetic error. Third, the decision support rule makes high risk visible to everyone who opens the chart through a banner, and it offers a consistent set of interventions to every patient over the threshold. For a newer nurse, the rule provides structure that the old practice lacked. These gains are credited, not dismissed.

What the Transfer Cost

The costs are less visible. The first is currency. Because the Morse items arrive pre-filled, the score recorded each shift is often the previous shift's score. A review on our unit found that in 17 of 40 patients who gained an IV line or became confused, the following shift filed the section with those items unchanged, and in 11 the correct score would have crossed the threshold. The system records a reassessment that did not happen, a known risk of copy-forward documentation (Tsou et al., 2017).

The second cost is information. The rule reads only the total, so patients who reach 45 for different reasons receive the same response, although fall prevention evidence favors individualized interventions (Oliver et al., 2010). The nurse's knowledge of why a patient might fall, which the old practice relied on, has no place to go.

The third cost is attention. The banner appears on about 40% of charts, and the order set is accepted by habit. Repeated alerts are associated with declining response (Ancker et al., 2017).

The fourth cost is accountability. The dashboard reports 98.6% compliance by counting filed sections. The organization believes fall risk is being reassessed every shift, and the belief rests on a number that the default itself produces.

Where the Judgment Now Sits

The transfer did not remove the nurse's judgment; it moved it to places where it is harder to see. The judgment now sits in the decision to accept or change a pre-filled answer, a decision made quickly and often not made at all. It sits in the informatics team's choice of threshold and order set, made years ago and not recalibrated. And it sits in the quality department's choice of what to count. Sociotechnical models of health information technology describe how technology, people, workflow and measurement interact in ways that can shift responsibility without anyone deciding to shift it (Sittig & Singh, 2010).

What Should Move Back

Some of the judgment should move back to the nurse, and some should stay with the system. The calculation and the banner should stay; they are genuine gains. The items most likely to change, recent falls, intravenous access, gait and mental status, should move back to the nurse by arriving blank each shift, so that the score reflects a current assessment. The reasons for high risk should be shown in the banner, and the response should be chosen from interventions matched to those reasons, returning the nurse's knowledge of why a patient might fall to the decision. And the compliance measure should count active reassessments, so that the organization's picture of its own practice is accurate.

The Nursing Informatics Council has approved the first two changes for a 90-day trial on the medical-surgical units. Its results will reveal whether scores now vary from shift to shift, whether the banner rate falls or rises and how much documentation time the change adds.

A Note on Evidence

The unit review behind this paper is small, 40 patients over two weeks, and was designed to test whether the problem existed rather than to measure its size precisely. The trial approved by the council will provide better data. The paper's claims about gains and costs carry that caveat.

Conclusion

Technology did not make the fall risk judgment worse or better in a simple sense. It made the judgment more consistent and more visible, and less current and less informative. The lesson for informatics practice is to examine each judgment a system takes over, to credit what it gains, to count what it costs and to decide deliberately which parts belong with the nurse. Scoring tools have always had modest predictive value on their own (Oliver et al., 2004); their value depends on the assessment behind the number.

Whatever the trial shows, the method of this paper, isolating one judgment and tracing where it now sits, can be applied to other settings the system has taken over, such as pressure injury scoring and early warning alerts.

References

Ancker, J. S., Edwards, A., Nosal, S., Hauser, D., Mauer, E., & Kaushal, R. (2017). Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC Medical Informatics and Decision Making, 17, Article 36. https://doi.org/10.1186/s12911-017-0430-8

Oliver, D., Daly, F., Martin, F. C., & McMurdo, M. E. T. (2004). Risk factors and risk assessment tools for falls in hospital in-patients: A systematic review. Age and Ageing, 33(2), 122-130. https://doi.org/10.1093/ageing/afh017

Oliver, D., Healey, F., & Haines, T. P. (2010). Preventing falls and fall-related injuries in hospitals. Clinics in Geriatric Medicine, 26(4), 645-692. https://doi.org/10.1016/j.cger.2010.06.005

Sittig, D. F., & Singh, H. (2010). A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Quality and Safety in Health Care, 19(Suppl. 3), i68-i74. https://doi.org/10.1136/qshc.2010.042085

Tsou, A. Y., Lehmann, C. U., Michel, J., Solomon, R., Possanza, L., & Gandhi, T. (2017). Safe practices for copy and paste in the EHR: Systematic review, recommendations, and novel model for health IT collaboration. Applied Clinical Informatics, 8(1), 12-34. https://doi.org/10.4338/ACI-2016-09-R-0150