MMHA 6700 Week 11 Operations Improvement Plan Example

From Readiness to a Clean Bed by Early Afternoon: An Operations Improvement Plan for Linden Ford Medical Center

Student Name

Walden University

MMHA 6700: Healthcare Operations

Instructor Name

Month Day, Year

From Readiness to a Clean Bed by Early Afternoon: An Operations Improvement Plan for Linden Ford Medical Center

Summary

This plan brings together ten weeks of analysis at Linden Ford, the community hospital invented for this course, into one program to improve patient flow. Its central change is earlier discharge of medical-surgical patients, which frees beds before the afternoon peak in emergency admissions and shortens boarding. Two supporting changes address scheduled surgical demand and supply timing. The plan reconciles every figure used across the term in one table, phases the work over twelve months and sets a control plan to hold the gains. All figures are illustrative.

The Problem in One Chain

The term's analyses found one chain of cause and effect. Scheduled surgical admissions cluster midweek (Week 5), pushing medical-surgical occupancy to about 94 percent from Tuesday to Thursday (Week 1). Discharges happen late in the day: patients ready at 10:00 leave at 15:50, and their beds are clean at about 17:00 (Week 7), because rounds reach discharge-ready patients late (Week 8). Beds therefore become available after the emergency department's afternoon peak, so admitted patients board for a median of 4.8 hours (Week 4), and boarding, not triage, is the department's bottleneck. Admission orders, bed assignment and cleaning add waits of their own (Week 2), and supplies on the units run short in the same afternoon window (Week 6). Matching when work arrives to when staff and beds are free, instead of building more beds, is the approach supported by the operations literature (Litvak & Fineberg, 2013).

Reconciled Figures

Figures shifted slightly between assignments as each analysis used different samples. Table 1 sets the single value this plan uses for each.

Table 1

Figures Used in This Plan and Their Sources

Using one value for each figure prevents the plan from appearing to promise different results in different sections.

The Program in Three Parts

Part 1: Earlier Discharge

The core change follows the Week 9 charter. Likely discharges are flagged at an 08:30 huddle, hospitalists round on flagged patients first, case managers call families with a target time, signed discharge orders page the nurse, prescriptions for planned discharges are filled the evening before and patient departure sends an automatic cleaning request. The aim is a median departure of 13:30 for medical patients discharged home, with no increase in seven-day readmissions. Hospitals that set explicit discharge-time goals and changed rounding have achieved similar shifts (Wertheimer et al., 2014).

Part 2: Smoother Surgical Demand

Following the Week 5 review, block release moves to 72 hours before surgery, underused blocks shift to open booking and a pilot moves one orthopedic surgeon's Wednesday block to Monday. The aim is to lower midweek occupancy by two to three percentage points, which modeling suggests reduces the frequency of bed shortages more than the same change on a quiet day.

Part 3: Admission and Supply Timing

From Weeks 2 and 6, bed requests are entered when the hospitalist accepts the patient, a level-of-care check is added before the request and high-use supplies such as intravenous start kits are restocked twice daily on busy units.

Phasing

The order reflects evidence and control. The discharge change has the strongest evidence and lies within nursing and hospitalist control; the surgical changes depend on negotiations with surgeons and come later.

Table 2

Twelve-Month Phasing

Phases overlap, but each begins only when the previous gate is met, so a failing change does not spread.

Control Plan

Gains in flow are notoriously hard to hold. The control plan assigns each measure an owner, a review frequency and a response rule.

Statistical process control charts distinguish signals from noise and are well suited to monitoring improvements once in place (Benneyan et al., 2003).

Table 3

Control Plan

Each measure is plotted over time, so leaders respond to real shifts rather than single bad weeks.

Risks

The main risk is that earlier discharge spreads unevenly, with some hospitalist teams resisting the rounding change. The phasing gate addresses this by requiring several improving units before full spread. A second risk is that the financial case, roughly break-even in the base case, weakens support. The plan therefore presents boarding reduction as a safety measure first, since longer boarding is associated with worse outcomes for admitted patients. A third risk is that surgical block changes stall; the discharge change does not depend on them.

Conclusion

Linden Ford's flow problem is a timing problem. Beds free up after the afternoon peak, so admitted patients wait in the emergency department. This plan moves discharges earlier, smooths scheduled surgical demand and tightens admission and supply timing, in that order, with one set of reconciled figures, phase gates and a control plan. It promises boarding relief and safer care, with a financial result near break-even in the base case, and it tells leaders exactly what to watch.

References

Benneyan, J. C., Lloyd, R. C., & Plsek, P. E. (2003). Statistical process control as a tool for research and healthcare improvement. Quality and Safety in Health Care, 12(6), 458-464. https://doi.org/10.1136/qhc.12.6.458

Litvak, E., & Fineberg, H. V. (2013). Smoothing the way to high quality, safety, and economy. New England Journal of Medicine, 369(17), 1581-1583. https://doi.org/10.1056/NEJMp1307699

Wertheimer, B., Jacobs, R. E., Bailey, M., Holstein, S., Chatfield, S., Ohta, B., Horrocks, A., & Hochman, K. (2014). Discharge before noon: An achievable hospital goal. Journal of Hospital Medicine, 9(4), 210-214. https://doi.org/10.1002/jhm.2154