NUR 521 Module 3 Applying Information Technology in Practice Paper Example

A Score That Sees Trouble Coming: Applying an AI Deterioration Index on a Community Hospital Medical Unit

Student Name

Edson College of Nursing and Health Innovation, Arizona State University

NUR 521: Health Care Evidence, Informatics and Analysis

Instructor Name

Month Day, Year

A Score That Sees Trouble Coming: Applying an AI Deterioration Index on a Community Hospital Medical Unit

Introduction

On a 32-bed medical unit at night, a charge nurse has to decide which patients to watch most closely with only a few nurses on the floor. Artificial intelligence now offers a tool for that decision: deterioration indexes that combine vital signs, laboratory values and other data in the electronic record to estimate each patient's risk of clinical decline. This paper describes one such model, the evidence about how well it travels between hospitals, and what would affect its use on my unit.

The Technology

A deterioration index is a predictive model trained on large numbers of past hospital stays. It looks for patterns in structured data, such as rising heart rate, falling blood pressure, changes in oxygen requirements or abnormal laboratory results, that tend to come before transfer to intensive care or death. The model updates a score for each patient as new data arrive, and the score can be displayed on a unit dashboard or trigger a notification.

What the Evidence Shows

A key concern with AI models is whether they work outside the hospital where they were built. A study of a ward deterioration index called PICTURE tested a model trained on more than 165,000 encounters at an academic medical center in a second, community hospital with 11,083 encounters, a different patient population and different data patterns (Cummings et al., 2023). Despite differences in missing data, deterioration rates and racial makeup, the model's discrimination was similar at both sites, with discrimination, measured by the area under the curve, of about 0.87. The authors emphasized that validating models at multiple institutions is critical because data shift between settings.

For a charge nurse, the numbers mean that the model ranks patients by risk fairly well, but also that many high scores will not end in deterioration, because deterioration is relatively rare. A score is therefore a prompt to look, not a diagnosis.

Application in Practice

On my unit, the index could support the charge nurse's rounding: at the start of each shift and every four hours, the charge nurse would review the five highest-risk patients with their assigned nurses, look for early warning signs and judge whether a rapid response call is needed or increase monitoring. The score would add to, not replace, nurses' clinical judgment and existing early warning criteria. Nurses would document what action, if any, followed a high score, which would let the unit evaluate whether the tool changes outcomes.

Strengths

The model uses data that already exist in the record, so it adds no documentation work. It can watch every patient continuously, which no charge nurse can. It may help newer nurses recognize subtle patterns, especially since nurses tend to rate their own information and knowledge management skills lowest among informatics competencies (Kleib & Nagle, 2018), and it gives a common language for escalation discussions with physicians.

Resistance

Resistance is likely and partly reasonable. Nurses may distrust a score they cannot explain, especially if it contradicts what they see at the bedside. Experience with earlier tools also shapes trust; when barcode scanning was introduced, nurses developed many workarounds to systems that did not fit their work (Koppel et al., 2008). Alert fatigue is a real concern: if scores trigger frequent notifications that rarely lead to action, nurses will learn to ignore them, as they have with other alerts. Some will worry that a score will be used to judge them after a bad outcome. Others will raise equity concerns, since a model built on one hospital's patients can behave differently elsewhere, which is why the validation evidence matters. Leaders can reduce resistance by involving nurses in designing how the score is displayed, avoiding interruptive alerts at first, sharing local performance data and making clear that the score supports judgment rather than replacing it.

Conclusion

An AI deterioration index offers a way to focus scarce nursing attention on the patients most at risk. External validation suggests such models can perform consistently across hospitals, but adoption depends on thoughtful display, attention to alert fatigue and honest communication with nurses about what the score can and cannot do.

References

Cummings, B. C., Blackmer, J. M., Motyka, J. R., Farzaneh, N., Cao, L., Bisco, E. L., Glassbrook, J. D., Roebuck, M. D., Gillies, C. E., Admon, A. J., Medlin, R. P., Singh, K., Sjoding, M. W., Ward, K. R., & Ansari, S. (2023). External validation and comparison of a general ward deterioration index between diversely different health systems. Critical Care Medicine, 51(6), 775-786. https://doi.org/10.1097/CCM.0000000000005837

Kleib, M., & Nagle, L. (2018). Development of the Canadian Nurse Informatics Competency Assessment Scale and evaluation of Alberta's registered nurses' self-perceived informatics competencies. CIN: Computers, Informatics, Nursing, 36(7), 350-358. https://doi.org/10.1097/CIN.0000000000000435

Koppel, R., Wetterneck, T., Telles, J. L., & Karsh, B.-T. (2008). Workarounds to barcode medication administration systems: Their occurrences, causes, and threats to patient safety. Journal of the American Medical Informatics Association, 15(4), 408-423. https://doi.org/10.1197/jamia.M2616