NUR 633 Module 3 Milestone One Example

Milestone One: Defining the Inpatient Falls Problem on 6 West With Incident, Nurse Call, Alarm and Electronic Health Record Data

[Student Name]

Southern New Hampshire University

NUR 633: Informatics and Communication Technology

Milestone One

[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.

Milestone One: Defining the Inpatient Falls Problem on 6 West With Incident, Nurse Call, Alarm and Electronic Health Record Data

Informatics projects often begin with a product: a vendor demonstrates a sensor, a camera or a dashboard, and a unit looks for a problem it might solve. That order invites expensive tools that do not fit the work. This milestone reverses it. It defines the problem of inpatient falls on 6 West using data the hospital already collects, before any technology is considered. It argues that the unit's falls cluster at night after unanswered calls and alarms, that the information needed to prevent them exists but is scattered across systems that do not talk to one another and that the informatics project should therefore focus on connecting and filtering information rather than adding more of it.

The Clinical Problem

Hospital falls happen often enough to rank near the top of inpatient adverse events, and falls with injury lengthen stays, add costs and can cause death, particularly in older adults. Over the past 12 months, 38 patients fell on 6 West, which works out to 4.1 for every thousand patient days. Nine falls caused injury, including one hip fracture in an 84-year-old woman who later died. The unit already uses the Morse Fall Scale, hourly rounding and bed-exit alarms for high-risk patients, yet falls have not declined in three years. Staff are frustrated because they feel they are doing everything asked of them, and the unit's leaders have responded twice with reeducation, which produced a brief dip followed by a return to the previous rate. A problem that persists despite standard interventions usually means the interventions are not reaching the right patient at the right moment, which is a question about information.

What the Data Show

Four data sources were reviewed for the 12-month period. Incident reports show that 23 of the 38 falls, 61%, happened on the night shift, after 10 in the evening and before 6 in the morning, and that 29 involved patients walking to the bathroom. Nurse call logs, which record each call and when it is answered, show a median response time of 4.8 minutes on day shift and 7.2 minutes at night. When incident times were matched with the logs, 17 falls, 45%, occurred within 10 minutes after a call light or bed-exit alarm that had not yet been answered.

Bed-exit alarm records show about 1,860 alarms a week on the unit, of which about 70% were canceled within 30 seconds, usually because the patient had only shifted position. The electronic health record shows that Morse scores were documented for 97% of patients each shift, but the score appears only in a flowsheet. It is not displayed on the nurse call screen, the patient's whiteboard or the nurses' mobile badges, so the nurse answering a call does not know whether the caller is a high-risk patient.

What the Research Adds

Research helps interpret these patterns, though each study answers a slightly different question. In a study of acute care units, more call light use was associated with less fall-related harm, and, unexpectedly, longer average response times were associated with fewer falls; the authors concluded that encouraging call light use, rather than reducing it, should be the goal of rounding programs (Tzeng & Yin, 2009). That finding comes from unit-level data and cannot show what happened in individual falls, but it cautions against assuming response time alone explains falls. Bed alarms are also not enough on their own: the Shorr trial raised alarm use substantially on its intervention units and saw no drop in falls (Shorr et al., 2012). By contrast, a randomized trial of a fall prevention toolkit that used the electronic record to generate a patient-specific plan, shared with patients and displayed at the bedside, reduced falls, especially among patients aged 65 and older (Dykes et al., 2010). The lesson is that information tailored to the patient and delivered where decisions are made can prevent falls, while undifferentiated alerts do not.

Problem Statement

On 6 West, most falls occur at night when patients get up to use the bathroom, often shortly after a call or bed-exit alarm that has not been answered. The unit generates about 1,860 bed-exit alarms a week, most of them nonactionable, while the patient-specific fall risk information recorded in the electronic health record never reaches the nurse call system, the mobile badges or the bedside. As a result, nurses cannot tell which calls and alarms come from high-risk patients, and high-risk patients do not receive a faster or different response.

Aim and Measures

The project aims to reduce falls on 6 West by 30%, from 4.1 to about 2.9 for every thousand patient days, within 12 months of implementation, without increasing falls with injury. Supporting measures will include the proportion of calls from high-risk patients answered within three minutes at night, the number of bed-exit alarms per week and the proportion that are nonactionable, and nurses' ratings of alarm burden on a brief survey. A balancing measure will track response times for all other patients to ensure that prioritizing high-risk calls does not delay care for others.

Questions for the Systems Analysis

The data raise four questions for Milestone Two. Can the Morse score or a fall risk flag be sent from the electronic record to the nurse call system and badges? Can bed-exit alarm sensitivity be adjusted to reduce nonactionable alarms, and can alarms route to the assigned nurse's badge rather than sounding at the station? How does the night workflow for answering calls actually run, and where do delays arise? And what privacy and security requirements apply to sending patient information to mobile devices? Answering these before choosing any equipment will prevent the unit from buying a tool that duplicates what it already owns, which is a common and costly mistake in hospital technology projects.

Conclusion

The falls problem on 6 West is not a lack of data or effort. It is information that exists but does not reach the nurse at the moment of decision, alongside alarms so frequent that they lose meaning. Defining the problem this way points the informatics project toward integration and filtering, which the systems analysis will examine.

References

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

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