HA535 Unit 10 Capacity Analytics Report Example

Nineteen Beds a Day Held by Patients Ready to Leave: An Analytics Report on a Year of Inpatient Data at a Composite Three-Hospital System, Recommending a Post-Acute Placement Team at $291,000 a Year With a Measured Target and a Date to Judge It

[Student Name]

Purdue University Global

HA535: Data Analytics for Health Care Managers

Unit 10 Analytics Report

[Instructor Name]

[Date]

The system, its data and every figure are composite and bracketed for the writer's own data; the report demonstrates the form of an analytics report, not a real system's results.

Executive Summary

Question: what is holding inpatient capacity across the system, and what should the system fund first? Answer: patients waiting for post-acute placement. Across [29,100] discharges in one fiscal year, [7,120] patient days, [4.6] percent of all days, were spent by patients already cleared to leave. That equals about [19.5] beds every day, and the flagship hospital holds [4,960] of those days. Recommendation: fund a post-acute placement team, two case managers, a post-acute liaison and placement software, at [$291,000] a year. A [30] percent reduction in avoidable days would free [2,136] bed days, about [5.9] beds a day. The team pays for itself if it removes [477] days. Decision needed: approval in the next budget cycle, with a first review after two quarters.

Introduction

In the last year, the flagship ran at [88.1] percent occupancy, emergency boarding rose and the winter forecast showed medical-surgical beds running out in January. Leaders have asked for beds, staff and a new unit. This report asks a narrower question first: of the beds the system already has, how many are used by patients who no longer need them, and what would release them? It does not address emergency department flow, elective surgery scheduling or staffing ratios, which other reviews are examining.

Data and Methods

The data are all inpatient discharges at three hospitals for one fiscal year: [29,100] stays after cleaning. Three repairs were made before analysis, as documented in Unit 2. First, [494] exact duplicate rows created by an interface resend were removed. Second, [176] patients carrying two record numbers after the rural hospital joined the system were linked, which moved the readmission rate from [12.1] to [12.5] percent. Third, [278] short monitoring stays at the rural hospital that had been billed as admissions were moved back to observation. Data quality was judged on completeness, correctness and timeliness, the dimensions most often used to assess clinical records for secondary use (Weiskopf & Weng, 2013). An avoidable day is a day after a physician documents medical readiness for discharge and before the patient leaves, unless a clinical change is recorded. Bed days are converted to beds by dividing by 365. Length of stay was compared across hospitals after adjusting for case mix. The census forecast came from the seasonal exponential smoothing model built in Unit 7, which captures the upward trend and the winter peak and was tested on a held-out year (Hyndman & Athanasopoulos, 2021). Work was done in a spreadsheet and a statistical package, with the code kept in the project folder.

Finding 1: Avoidable Days Hold About 19.5 Beds a Day

Avoidable days totaled [7,120]. The flagship held [4,960], the suburban hospital [1,580] and the rural hospital [580]. Most waits were for skilled nursing beds ([61] percent), followed by inpatient rehabilitation ([19] percent), home health with equipment ([12] percent) and guardianship or payer authorization ([8] percent). The median wait after readiness was [two] days, but the longest tenth of waits, [eight] days or more, accounted for over half of all avoidable days. The capacity the system is trying to buy already exists; it is occupied by patients waiting to leave. Consequence: a team focused on the longest waits would reach most of the lost days by handling a small share of patients. Studies of delayed discharge report that these waits also bring loss of function and distress for patients and families, so the gain is not only in beds (Rojas-García et al., 2018).

Finding 2: In Heart Failure, the Longest Tenth of Stays Uses 24.0 Percent of Bed Days

Heart failure is the system's most common medical diagnosis. Its longest tenth of stays used [24.0] percent of heart failure bed days, and [58] percent of those long stays included avoidable days, mostly waits for skilled nursing.

Consequence: placement delays are the main reason heart failure stays run long, not clinical complexity alone.

Finding 3: Winter Capacity Runs Out in January Regardless

The forecast shows flagship medical-surgical census reaching [355.8] against [356] staffed beds in January. The travel nurse contracts presented in Unit 9 would reopen [16] beds and bring January occupancy to [95.6] percent. Consequence: placement work and reopened beds are complements, not substitutes. Freeing [5.9] beds a day would bring January closer to [94] percent even with the reopened beds.

Options Considered

Option A, do nothing: no cost; avoidable days continue near [7,120] and the January shortage stands. Option B, a skilled nursing partnership alone: preferred-provider agreements with [three] facilities to accept referrals within [24] hours. Low cost, about [$40,000] in staff time, but it addresses only skilled nursing waits and depends on the facilities' own staffing. Option C, a post-acute placement team at [$291,000]: two case managers dedicated to patients nearing readiness, a liaison who visits facilities and builds relationships, and software that sends referrals to many facilities at once and tracks responses. It works on all wait types and can include the partnership as one of its tools.

Break-Even Arithmetic

The variable cost of an inpatient day, the cost that disappears when a day is not used, is about [$610] at the system level (Cleverley & Cleverley, 2018). Dividing [$291,000] by [$610] gives [477] days. That is [6.7] percent of current avoidable days. A [30] percent reduction, the planning assumption, frees [2,136] days and about [$1.3] million in variable cost, or room for about [430] additional admissions at the current average stay.

Recommendation

Fund Option C, with the partnership built into the liaison's first quarter. Place the team at the flagship, where [70] percent of avoidable days occur, with remote support for the other two hospitals.

Limitations

Avoidable days depend on case managers recording readiness consistently; documentation varies by unit, so the true number may be higher or lower. The [30] percent reduction is an assumption drawn from comparable programs, not a result. The variable cost per day is an average and differs by unit. The forecast does not account for an unusual respiratory season. Finally, freed beds create value only if they are filled by patients who would otherwise wait or be diverted.

Monitoring Plan

Primary metric: avoidable days per month by hospital, shown on a run chart against the baseline median (Provost & Murray, 2011). Secondary metrics: median wait after readiness, the share of waits over [seven] days and flagship occupancy. Balancing metric: readmissions within [30] days for patients placed by the team. Owner: the system director of case management. Review: quarterly at the operations committee, with the first formal judgment after two quarters. If avoidable days have not fallen by at least [477] annualized at that review, the council should reconsider the team.

Conclusion

The system does not need to buy all of its winter capacity. About [19.5] beds a day are already in use by patients ready to leave. A placement team at [$291,000] a year is a modest, measurable bet, and its success or failure will be clear within six months.

References

Cleverley, W. O., & Cleverley, J. O. (2018). Essentials of health care finance (8th ed.). Jones & Bartlett Learning.

Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts. https://otexts.com/fpp3/

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

Rojas-García, A., Turner, S., Pizzo, E., Hudson, E., Thomas, J., & Raine, R. (2018). Impact and experiences of delayed discharge: A mixed-studies systematic review. Health Expectations, 21(1), 41-56. https://doi.org/10.1111/hex.12619

Weiskopf, N. G., & Weng, C. (2013). Methods and dimensions of electronic health record data quality assessment: Enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1), 144-151. https://doi.org/10.1136/amiajnl-2011-000681