NUR 521 Module 7 Integrating Informatics and Health Care Final Project Example

On Time, Every Time? A Quality Improvement Question, Integrated Review and Measurement Plan for Time-Critical Medications

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

On Time, Every Time? A Quality Improvement Question, Integrated Review and Measurement Plan for Time-Critical Medications

Introduction

Information systems in hospitals record nearly every step of medication administration, yet nurses rarely use that information to improve care in real time. This project uses the data our barcode system already collects to address a persistent problem on my 32-bed medical unit: time-critical scheduled medications given late. The paper presents the quality improvement question, an integrated review of the literature, the outcome data to be collected and a proposed outcome measure.

The QI Question

On a 32-bed adult medical unit, does adding a real-time overdue-dose dashboard for charge nurses, compared with current practice, increase the percentage of time-critical scheduled medications administered within 30 minutes of the scheduled time over six months? This is a quality improvement question because it aims to improve local practice using existing evidence and local data, not to produce generalizable knowledge.

Integrated Literature Review

Defining Timely Administration

National guidance sets the standard. The safe medication practices institute recommends (Institute for Safe Medication Practices, 2011) that each hospital list its time-critical scheduled medications, those whose delay past half an hour could cause harm or reduced effect, and give each within a half hour either side of its scheduled time, while allowing wider windows for other scheduled doses. This guidance provides the definition for the project's outcome.

Why Doses Are Late

Two lines of research explain lateness. First, workflow interruptions are common and harmful. An observational study of 98 nurses administering 4,271 doses found that interruptions occurred in about half of administrations and that the odds of a clinical error climbed 12.7% with each interruption (Westbrook et al., 2010). Interruptions slow the medication pass as well as introducing errors. Second, barcode systems themselves create delays and workarounds. The five-hospital barcode study (Koppel et al., 2008) found 15 kinds of workaround and 31 kinds of cause, including unreadable barcodes, failing scanners and wireless problems, and found alert overrides for 10.3% of medications charted (Koppel et al., 2008). Workarounds such as scanning after administration also distort timing data, which matters for measurement.

Technology That Directs Attention

Predictive and display tools can help nurses direct scarce attention. A study of an AI-based deterioration index showed that a model trained at one hospital performed consistently at a second, quite different hospital, with an area under the curve around 0.87 at both (Cummings et al., 2023). Although that tool predicts clinical decline rather than late doses, it illustrates a broader principle relevant to this project: tools built from routinely collected record data can highlight where attention is needed, but they must be validated in the local setting and designed to avoid alert fatigue.

Synthesis

The literature supports three conclusions for this project. The standard for timeliness is clear. Lateness arises from workflow and system design rather than individual carelessness. And existing record data can support tools that direct attention, provided their data quality and local performance are checked. No study in this review tested an overdue-dose dashboard directly, which is why this project is framed as local improvement with careful measurement.

Outcome Data to Be Collected

Data will come from the electronic medication administration record and barcode logs, extracted monthly by the informatics analyst for three months before and six months after the dashboard begins. For each scheduled dose, the dataset will include the medication, whether it is on the hospital's time-critical list, the scheduled time, the scan time, the unit, the shift and the day of the week. No patient identifiers will be included in the analysis file. A brief observation audit of 20 medication passes each month will check whether scan times match administration times, addressing the workaround problem described in the literature.

Proposed Outcome Measure Instrument

The primary outcome measure is the On-Time Time-Critical Dose Rate:

Secondary measures are the percentage of all scheduled doses more than one hour late, dashboard use by charge nurses, medication error reports as a balancing measure, and a short nurse survey on whether the dashboard feels supportive or punitive.

Strengths and Limitations

The measure uses existing data, requires no extra documentation and aligns with national guidance. Its main limitation is that scanning time may not equal administration time; the observation audit estimates this error. Comparing one unit before and after cannot show that the dashboard itself caused a change, since staffing or seasonal patterns could also shift, so a run chart will be used to look for sustained shifts rather than single-month changes.

Barriers, Ethics and Sustainability

Three barriers could undermine the project. First, the dashboard depends on the informatics department's time to build and maintain it, so the unit will start with a simple report refreshed every 15 minutes rather than a custom application. Second, nurses may experience the display as surveillance; to address this, the dashboard will show rooms rather than nurse names to everyone except the charge nurse, and data will never be used for individual performance evaluation. Third, gains could fade once attention shifts elsewhere, so the monthly run chart will be reviewed at the unit council and built into the charge nurse orientation. Ethically, the project uses existing data without identifiers and seeks to benefit patients directly through more timely care, which fits the hospital's quality improvement review process. If the measure improves and holds for six months, the unit will propose the dashboard for other medical units.

Conclusion

This project brings together evidence, informatics and quality improvement around one practical question. By defining timeliness with national guidance, understanding lateness through research on interruptions and workarounds, and measuring outcomes with existing data and a validity check, the unit can learn whether a simple dashboard helps nurses give time-critical medications on time.

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

Institute for Safe Medication Practices. (2011). Acute care guidelines for timely administration of scheduled medications. https://www.ismp.org/resources/acute-care-guidelines-timely-administration-scheduled-medications

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

Westbrook, J. I., Woods, A., Rob, M. I., Dunsmuir, W. T. M., & Day, R. O. (2010). Association of interruptions with an increased risk and severity of medication administration errors. Archives of Internal Medicine, 170(8), 683-690. https://doi.org/10.1001/archinternmed.2010.65