NUR 633 Module 9 Final Project Example

Final Project: An Informatics Proposal to Reduce Falls on 6 West by Integrating Fall Risk Across the Record, Nurse Call and Mobile Devices

[Student Name]

Southern New Hampshire University

NUR 633: Informatics and Communication Technology

Final Project

[Instructor Name]

[Date]

The organization, setting and figures below are a composite written as a model document. No real employer, client, colleague or patient is described.

Final Project: An Informatics Proposal to Reduce Falls on 6 West by Integrating Fall Risk Across the Record, Nurse Call and Mobile Devices

Hospitals rarely lack information about which patients are likely to fall. What they lack is a way to get that information to the right person at the moment a high-risk patient tries to stand. This proposal closes that gap for the 32 beds of composite unit 6 West by connecting systems the hospital already owns. It asks the nursing and information technology steering committee to approve a modest integration project, and it argues that the project is justified by the unit's own data, supported by evidence, designed around the people who will use it and structured so that its value can be measured and its continuation decided on results.

The Problem in the Unit's Data

Over the last year, 6 West recorded 38 falls and 9 injuries, among them a broken hip in a woman in her eighties who died after surgery. The usual safeguards, a validated risk scale, rounding every hour and bed sensors, have run for three years without bending the curve. Linking incident times with nurse call logs showed that 61% of falls occurred at night and that 45% followed within 10 minutes an alarm or call no one had yet answered. About 1,860 bed-exit alarms sound in a typical week, and most are canceled within half a minute because the patient only shifted in bed. The Morse score is charted for nearly every patient, yet it stays in a flowsheet that no responder sees. The problem is not missing information; it is information that stops short of the bedside.

What the Evidence Supports

Three findings shape the design. A study of call light use found that units where patients called more often had less fall-related harm, suggesting that encouraging calls, not suppressing them, should be the goal (Tzeng & Yin, 2009). A cluster randomized trial found that increasing bed alarm use alone did not reduce falls (Shorr et al., 2012), and a review of alarm research reported that most clinical alarms are false or insignificant, leading to desensitization (Sendelbach & Funk, 2013). By contrast, a randomized trial of a toolkit that drew on the electronic record to produce an individualized fall prevention plan, shared with patients and posted at the bedside, reduced falls, particularly among patients 65 and older (Dykes et al., 2010). Together these suggest that targeted, patient-specific information delivered where decisions are made works better than more undifferentiated alarms.

How the Work System Fails at Night

A work system analysis using the SEIPS model, which treats people, tasks, tools, environment and organization as interacting parts (Carayon et al., 2006), traced a typical night event. Five nurses and one assistant cover up to 32 patients on an L-shaped unit whose far rooms sit 60 meters from the station. A bed-exit alarm from a high-risk patient looks identical to one from a low-risk patient, sounds at a station that is often empty and never reaches a badge. Sensors default to their most sensitive setting. The patient's risk and prevention plan are visible to no one who responds. The analysis found four weak links: every alarm looked equally urgent, alarms went to a console instead of a caregiver, sensors were set too twitchy by default and the risk score never left the chart.

The Proposed Design

The design corrects each failure point using existing systems. When a nurse documents a Morse score of 45 or higher, the integration engine sends a risk flag to the nurse call system, which marks the room. Bed-exit alerts from flagged rooms go to the badges of the assigned nurse and assistant with the room and risk level, and escalate to the charge nurse when a minute passes without a response. Sensors default to a middle sensitivity, raised only for a documented reason. Each flagged patient receives an individualized prevention plan on the whiteboard, reviewed with patient and family. Calls from unflagged patients continue to reach staff as they do now, and patients are encouraged, not discouraged, to call.

Privacy, Usability and Documentation

Badge messages will display only the room number and a risk level, not names or diagnoses, applying the minimum necessary standard to information shown on mobile devices, and badges are hospital-managed and encrypted. The design adds no documentation: the risk flag comes from a score nurses already chart. Night nurses and assistants will test the design in scripted scenarios before go-live, and any step that adds clicks or alerts without benefit will be removed.

Budget and Value

The project is inexpensive because it uses existing systems. Estimated costs are about $13,200 for 120 hours of interface build and testing, $9,600 for badge alert licensing for one year, $8,400 for staff time in scenario testing and training, $12,000 for informatics nurse support during go-live and $4,800 for whiteboard templates and printing, a total of about $48,000. Against this, a study at three hospitals found that falls with serious injury added about $13,800 in operational costs and nearly a week to the hospital stay (Wong et al., 2011). Preventing four serious-injury falls in a year would roughly repay the investment, before counting the harm to patients themselves.

Implementation

Implementation runs over 16 weeks. The first month builds and tests the integration in a test environment and on four beds. The second month runs scripted scenarios with night staff and fixes problems. In weeks nine and ten only half the unit switches over, giving a brief side-by-side comparison, and the remaining rooms follow from week eleven. Barriers identified with the Consolidated Framework for Implementation Research (Damschroder et al., 2009), such as thin night staffing and skepticism left by a faded 2022 pilot, are addressed with shift-based super users, 20-minute training at the start of each shift type, informatics support on nights during each go-live week and a downtime procedure that posts printed lists of high-risk patients if the interface fails.

Evaluation and Decision Rule

Success will be judged first by the fall rate per thousand patient days for the year after implementation compared with the year before, aiming for a drop of about 30%; falls that cause harm are tracked as the most important secondary result. Process measures from system logs include the share of flagged alerts acknowledged within a minute at night, weekly bed-exit alarm counts and the share canceled almost immediately. A balancing measure tracks response times for unflagged patients. A staff survey measures alarm burden. At week 16, the steering committee will choose among keeping, modifying or retiring the system, looking first at prompt acknowledgment of flagged alerts and at staff reports of alarm burden; the annual fall rate will guide the decision at one year.

Conclusion

6 West's falls continue not because nurses do too little but because the right information does not reach them at the right moment. For about $48,000, using systems the hospital already owns, this project would deliver patient-specific risk to the people who respond at night, cut unnecessary alarms and give patients an individual plan. It is designed to be tested honestly and kept only if it works.

References

Carayon, P., Schoofs Hundt, A., Karsh, B.-T., Gurses, A. P., Alvarado, C. J., Smith, M., & Flatley Brennan, P. (2006). Work system design for patient safety: The SEIPS model. Quality and Safety in Health Care, 15(Suppl. 1), i50-i58. https://doi.org/10.1136/qshc.2005.015842

Damschroder, L. J., Aron, D. C., Keith, R. E., Kirsh, S. R., Alexander, J. A., & Lowery, J. C. (2009). Fostering implementation of health services research findings into practice: A consolidated framework for advancing implementation science. Implementation Science, 4, Article 50. https://doi.org/10.1186/1748-5908-4-50

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

Sendelbach, S., & Funk, M. (2013). Alarm fatigue: A patient safety concern. AACN Advanced Critical Care, 24(4), 378-386. https://doi.org/10.1097/NCI.0b013e3182a903f9

Shorr, R. I., Chandler, A. M., Mion, L. C., Waters, T. M., Liu, M., Daniels, M. J., Kessler, L. A., & Miller, S. T. (2012). Effects of an intervention to increase bed alarm use to prevent falls in hospitalized patients: A cluster randomized trial. Annals of Internal Medicine, 157(10), 692-699. https://doi.org/10.7326/0003-4819-157-10-201211200-00005

Tzeng, H.-M., & Yin, C.-Y. (2009). Relationship between call light use and response time and inpatient falls in acute care settings. Journal of Clinical Nursing, 18(23), 3333-3341. https://doi.org/10.1111/j.1365-2702.2009.02916.x

Wong, C. A., Recktenwald, A. J., Jones, M. L., Waterman, B. M., Bollini, M. L., & Dunagan, W. C. (2011). The cost of serious fall-related injuries at three Midwestern hospitals. Joint Commission Journal on Quality and Patient Safety, 37(2), 81-87. https://doi.org/10.1016/S1553-7250(11)37010-9