DNP 825 Module 2 A Dashboard That Supports Decisions Example

Where Does the Next Nurse Go? Redesigning a House Supervisor's Staffing Dashboard Around a Decision

Student Name

Doctor of Nursing Practice Program, Aspen University

DNP 825: Health Information Management and Informatics

Instructor Name

Month Day, Year

Where Does the Next Nurse Go? Redesigning a House Supervisor's Staffing Dashboard Around a Decision

Hospitals are full of dashboards that display numbers without helping anyone decide anything. A dashboard earns its place when it is built around a specific decision, made by a specific person, at a specific time. This paper describes the redesign of a staffing dashboard used by house supervisors in a composite 320-bed hospital, who twice a day decide where to assign float nurses, whether to call in additional staff, and which units should hold admissions. It reviews the evidence on dashboards and on staffing, analyzes the problems with the old display, and describes a design in which each element supports the decision.

Evidence on Dashboards

A review of dashboards in clinical settings found only 11 studies meeting its criteria, with considerable variation in users, settings and indicators. Where dashboards were easily accessible to clinicians, their use was associated with better care processes and outcomes, but the authors called for more rigorous research and for design guidance (Dowding et al., 2015). A review of dashboards used for nursing analytics described five properties that shape design: how databases are integrated, visual properties, purpose, time focus, whether retrospective, real time or predictive, and the type of process monitored, and it emphasized that these properties should follow from the organization, the user and the purpose (Wilbanks & Langford, 2014). Both reviews point to the same principle: design starts with the user's decision.

Why Staffing Decisions Matter

Staffing decisions have consequences for patients. Analyzing 197,961 admissions across 43 units of one academic medical center, researchers found that the hazard of death rose 2% for every shift a patient experienced with registered nurse staffing 8 or more hours below target, and each shift with high patient turnover with a 4% increase (Needleman et al., 2011). The finding shows that what matters is not only the number of nurses but how staffing compares with the needs of the patients on the unit at that time, which is precisely what a supervisor must judge when assigning a float nurse.

The Old Dashboard

The existing staffing screen listed every unit with its census, number of nurses scheduled and a ratio of patients to nurses, updated every four hours, with cells colored red when the ratio exceeded a fixed threshold. Supervisors described several problems in interviews. The ratio ignored acuity, so a unit with several patients on insulin infusions and a patient in alcohol withdrawal looked the same as a unit of stable patients awaiting placement. The data were up to four hours old. Pending admissions, discharges and transfers, which determine the next shift's workload, were not shown. And the red cells appeared on so many units at night that supervisors ignored them and called charge nurses instead.

The Redesign

The redesigned dashboard is built around the question supervisors ask at 2 p.m. and 10 p.m.: where will the next available nurse do the most good over the coming shift? It has one row per unit and five columns. The first shows current census and registered nurses on duty, refreshed every 15 minutes from the admission and time and attendance systems. The second shows workload intensity from the nursing acuity tool nurses already complete each shift, expressed as needed nursing hours compared with scheduled hours. The third shows expected change over the next eight hours: pending admissions from the emergency department and operating rooms, planned discharges and transfers. The fourth shows the projected gap in registered nurse hours for the coming shift, the difference between needed and scheduled hours after expected changes. The fifth flags units with high turnover expected, based on scheduled admissions and discharges.

Units are sorted by projected gap, largest first, rather than alphabetically, so the answer to the supervisor's question is at the top. Color is used only for units whose projected gap exceeds eight registered nurse hours, the threshold associated with mortality in the staffing study, so that red means something.

Testing the Design With Supervisors

Before launch, the informatics team built a working prototype and asked five house supervisors to use it alongside the old screen for two weeks. Each time they made a float assignment, they noted which display they relied on and whether the new one agreed with their judgment. The test changed the design. Supervisors wanted the name of the charge nurse and a direct call link on each row, because they still wanted to confirm the picture by phone; they asked that the acuity column show whether scores were missing, since a unit that had not completed scoring could look deceptively light; and they asked for a history view showing where float nurses had gone in the past week, to avoid repeatedly sending the same nurses to the hardest units. All three changes were adopted.

Data Sources and Quality

The dashboard depends on four systems: admission and bed management, time and attendance, the acuity tool within the electronic health record, and the surgical scheduling system. Each feed has a data steward responsible for accuracy. The largest quality risk is acuity scoring, which varies by nurse, so unit educators will audit a sample of acuity scores monthly. Because projections depend on planned discharges, which are often late, the dashboard shows discharges only when a discharge order has been written.

Evaluation

The redesign will be evaluated by whether supervisors use it and whether decisions change. Measures include how often supervisors open the dashboard at decision times, the proportion of shifts with a registered nurse gap of eight or more hours, overtime and agency hours, and supervisors' ratings of usefulness. Staffing-sensitive outcomes such as falls will be tracked but interpreted cautiously, since many factors affect them.

Conclusion

A staffing dashboard that lists census and ratios displays data; one that projects each unit's gap in needed registered nurse hours over the coming shift, sorted and colored by an evidence-based threshold, supports a decision. Grounding the design in the supervisor's question, in evidence that staffing below target is associated with mortality, and in reliable data feeds turns the dashboard into a tool for placing nurses where patients need them most.

References

Dowding, D., Randell, R., Gardner, P., Fitzpatrick, G., Dykes, P., Favela, J., Hamer, S., Whitewood-Moores, Z., Hardiker, N., Borycki, E., & Currie, L. (2015). Dashboards for improving patient care: Review of the literature. International Journal of Medical Informatics, 84(2), 87-100. https://doi.org/10.1016/j.ijmedinf.2014.10.001

Needleman, J., Buerhaus, P., Pankratz, V. S., Leibson, C. L., Stevens, S. R., & Harris, M. (2011). Nurse staffing and inpatient hospital mortality. New England Journal of Medicine, 364(11), 1037-1045. https://doi.org/10.1056/NEJMsa1001025

Wilbanks, B. A., & Langford, P. A. (2014). A review of dashboards for data analytics in nursing. CIN: Computers, Informatics, Nursing, 32(11), 545-549. https://doi.org/10.1097/CIN.0000000000000106