MMHA 6700 Week 1 Hospital Operations Profile Example

Demand, Capacity and Flow at Linden Ford Medical Center: An Operations Profile and Its Three Daily Queues

Student Name

Walden University

MMHA 6700: Healthcare Operations

Instructor Name

Month Day, Year

Demand, Capacity and Flow at Linden Ford Medical Center: An Operations Profile and Its Three Daily Queues

Purpose and Approach

Linden Ford Medical Center is an invented 220-bed community hospital that this paper and the rest of the term's work use as a single, consistent setting. Every figure below is illustrative, chosen to be realistic for a hospital of this size rather than drawn from any real organization. The profile describes the hospital in three operational terms. Demand is the work arriving: patients, by source and by hour or day. Capacity is the resource available to serve that demand: beds, operating rooms, staff and the hours they work. Flow is the movement of patients from one resource to the next, and where it slows, queues form.

The purpose is to find those queues. A hospital rarely runs short of capacity across the board; more often, capacity sits idle in one place while patients wait in another because demand and capacity are mismatched by hour or day. Health systems that smooth that mismatch can improve safety and lower cost without adding beds (Litvak & Fineberg, 2013).

Demand

Emergency demand is variable by hour but predictable by day and season. Scheduled surgical demand is under the hospital's control, yet it is the more uneven of the two, because surgeons' block times cluster midweek. Variability that the hospital itself creates through scheduling is often larger than the natural variability of emergencies, a pattern found in a study of surgical caseload and intensive care access (McManus et al., 2003).

Table 1

Daily Demand by Source (Illustrative)

Emergency admissions are steady across the week; scheduled admissions are not. That difference shapes most of what follows.

Capacity

Table 2

Main Capacity Resources (Illustrative)

Beds are constant across the week, but the staff who move patients through them, case managers and cleaners, drop sharply on weekends and evenings.

Flow

Average length of stay is 4.6 days. With 220 beds, average occupancy is about 88 percent across the week, but it runs near 94 percent from Tuesday to Thursday and near 82 percent on Sundays. At 94 percent, the hospital has about 13 empty beds at any time, and many of those are the wrong type or awaiting cleaning. Modeling work on emergency admissions suggests that hospitals running above about 85 percent average occupancy face regular bed shortages and periodic crises, because random peaks in demand cannot be absorbed (Bagust et al., 1999).

Flow into beds depends on flow out of them. The median time a discharge order is written is 13:40, and the median time the patient actually leaves is 15:50. The bed is then cleaned and shown available a median of 70 minutes later. Admissions from the emergency department, meanwhile, peak in the afternoon and evening. A bed freed at 17:00 serves a patient who has waited in the emergency department since the morning.

Three Daily Queues

Reading demand, capacity and flow together locates three queues that form every weekday.

Boarding is the most visible, but it is a symptom. The input-throughput-output model of emergency department crowding places most of the cause in output, the ability to move admitted patients upstairs (Asplin et al., 2003). At Linden Ford, output is limited by when beds are freed and cleaned, not by how many exist.

Table 3

The Three Queues

The three queues are linked: the discharge queue feeds the cleaning queue, which feeds the boarding queue.

What the Profile Suggests for the Term

The profile suggests three lines of work. First, examine the admission process in detail to measure where time is spent between the decision to admit and arrival on the unit. Second, examine the operating room block schedule, because midweek clustering of scheduled admissions pushes occupancy to its peak on the same days boarding is worst. Third, examine the discharge day itself, from the point at which a doctor would let the patient go home to the point at which housekeeping marks the bed ready.

None of these require new beds. All three address the timing of demand and capacity rather than their totals, which is consistent with the finding that managing variability can achieve much of what added capacity would (Litvak & Fineberg, 2013).

Limits

A profile built on daily averages hides hour-to-hour variation and differences between units. It does not include outpatient services, which share imaging and laboratory capacity. Every figure is illustrative, and a real profile would need data from the hospital's admission, discharge and transfer system.

Conclusion

Linden Ford does not lack beds so much as it lacks beds at the right hour. Its emergency demand is steady, its scheduled demand peaks midweek and its discharges and cleaning happen late in the day. Three linked queues follow. Seeing the hospital as demand, capacity and flow makes it clear that the term's improvement work should start upstream of the emergency department, in the timing of discharges and of scheduled surgery.

References

Asplin, B. R., Magid, D. J., Rhodes, K. V., Solberg, L. I., Lurie, N., & Camargo, C. A. (2003). A conceptual model of emergency department crowding. Annals of Emergency Medicine, 42(2), 173-180. https://doi.org/10.1067/mem.2003.302

Bagust, A., Place, M., & Posnett, J. W. (1999). Dynamics of bed use in accommodating emergency admissions: Stochastic simulation model. BMJ, 319(7203), 155-158. https://doi.org/10.1136/bmj.319.7203.155

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

McManus, M. L., Long, M. C., Cooper, A., Mandell, J., Berwick, D. M., Pagano, M., & Litvak, E. (2003). Variability in surgical caseload and access to intensive care services. Anesthesiology, 98(6), 1491-1496. https://doi.org/10.1097/00000542-200306000-00029