HA599 Unit 3 Ambulance Handoff Needs Assessment Example

Eleven Minutes by the Hospital's Clock, Twenty-Four by the County's: A Capstone Needs Assessment Reconciling Two Measures of Ambulance Handoff at a Composite Regional Medical Center by Matching One Quarter's Arrivals Patient by Patient

[Student Name]

Purdue University Global

HA599: Master's Capstone in Health Care Administration

Unit 3 Needs Assessment

[Instructor Name]

[Date]

The hospital, the county and every figure are composite and bracketed for the writer's own data.

Why the Two Clocks Disagree

The hospital's dashboard starts the clock at triage registration. But a patient is registered at triage only after a nurse is free to take report, so the time a crew waits beside the desk before that moment never appears. Nobody falsified the figure; the delay is invisible by design. The county's clock starts when the ambulance arrives at the door, recorded in the crew's electronic care report, and stops when a nurse signs for the patient.

Method

Sources: county electronic care reports and hospital tracking-board entries for one quarter, [months]. Matching: records were joined on date, an arrival window of plus or minus [ten] minutes and patient age within one year. Of [3,112] county records, [2,900] matched one hospital record; [212] could not be matched. Unmatched records were more often overnight arrivals and transfers between hospitals, and their county handoff times were similar to the matched set, so excluding them is unlikely to change the result. Offload time was measured from the county's arrival time to the nurse's signature. Direct observation of [60] arrivals over [six] shifts checked that the county times matched what happened at the door; they agreed within [three] minutes in [55] of [60] cases. Direct observation has been used in other studies of offload delay for the same purpose (Cooney et al., 2013).

Results

Median offload time by the county's clock: [24] minutes. 90th percentile: [71] minutes. Share over [30] minutes: [27] percent. Of handoffs over [30] minutes, [78] percent occurred between [12:00] and [00:00]. By hour: delay climbs after [13:00], peaks between [16:00] and [20:00] and eases near midnight. Long delays cluster on days when the department held [12] or more admitted patients waiting for beds, consistent with research linking offload delay to crowding (Li et al., 2019). The hospital's dashboard was not wrong; it was measuring a different moment, after the wait had already happened.

Who Waits Longest

Long waits were not spread evenly across patients. Crews carrying patients with lower triage urgency waited longest, because nurses rightly took the sickest patients first; [64] percent of handoffs over [60] minutes involved patients later triaged at the two least urgent levels. Patients from nursing homes and assisted living made up [22] percent of arrivals but [31] percent of long waits, often because they arrived without a family member and needed a fuller report. Crews from the county's busiest station waited no longer than others, which suggests the delay belongs to the hospital's side of the door rather than to any one crew. These patterns matter for design: an arrival point would serve mostly stable patients, which is where it can safely free crews quickly, while the sickest patients would continue to go straight to a bed.

Cost to the County

Every minute beyond a [20]-minute standard keeps an ambulance and its crew out of service. In the quarter, that excess totaled about [1,230] unit-hours, or [410] a month, roughly the output of one full-time ambulance around the clock.

Cost to the Hospital

Diversion: [384] hours last year, each redirecting about [1.4] ambulances; [31] percent of those patients would have been admitted. Overtime: [$64,000] a year in overtime tied to triage and charge nurses covering hallway handoffs. Relationship: two formal letters from the county EMS medical director.

Charts

Chart 1 shows the distribution of offload times by the county's clock, with the hospital's median marked for comparison. Chart 2 shows median and 90th percentile offload time by hour of arrival. Both charts would be updated monthly during any pilot, using run charts to separate real change from noise (Provost & Murray, 2011).

What This Means for the Project

The problem is real and larger than the dashboard suggests. It is concentrated in twelve hours of the day. It is linked to boarding, which this project excludes, so any solution must protect itself from becoming a holding area for admitted patients. And the right measure for the project is the county's clock, not the hospital's.

Who Reviewed the Findings

The emergency department director, the county EMS quality coordinator and the house supervisor reviewed the method and results. The director accepted that the dashboard measured triage, not arrival. The county coordinator confirmed the arrival times. The house supervisor corrected [three] diversion entries that had been logged twice.

Limits

One quarter may not represent a full year. Matching by date, time and age could mis-pair a few records. Direct observation covered only [60] arrivals.

Conclusion

Measured from the door, the delay is twice what the hospital reports and concentrated in the afternoon and evening. That is the gap the rest of the capstone addresses.

References

Cooney, D. R., Wojcik, S., Seth, N., Vasisko, C., & Stimson, K. (2013). Evaluation of ambulance offload delay at a university hospital emergency department. International Journal of Emergency Medicine, 6(1), 15. https://doi.org/10.1186/1865-1380-6-15

Li, M., Vanberkel, P., & Carter, A. J. E. (2019). A review on ambulance offload delay literature. Health Care Management Science, 22(4), 658-675. https://doi.org/10.1007/s10729-018-9450-x

Provost, L. P., & Murray, S. K. (2011). The health care data guide: Learning from data for improvement. Jossey-Bass.