HLTH 6882 Week 3 Performance Gap Analysis Paper Example

Gap Analysis: Booked and Kept Specialist Slots at Prairie Bend's Visiting Clinic Over Twelve Months

Student Name

Walden University

HLTH 6882: Improving Healthcare Performance Through Social Entrepreneurship

Instructor Name

Month Day, Year

Gap Analysis: Booked and Kept Specialist Slots at Prairie Bend's Visiting Clinic Over Twelve Months

Introduction

This analysis measures the gap between how many booked specialist slots at Prairie Bend Community Hospital's visiting clinic end in a completed visit and how many could. It uses 12 months of the clinic's own scheduling records, drawn from a composite organization, so every figure is illustrative. The current kept rate is 75%, about 40 lost slots in a typical month. The proposed target is 90%. The analysis then divides the gap by specialty, weekday, booking lead time and travel distance, so that next week's cause analysis knows where to look. This paper does not explain why slots are lost; it describes where and how many.

Defining a Lost Slot

A booked slot is an appointment slot holding a patient's name at 8 a.m. two business days before the session day. A booked slot is kept if the patient arrives and is seen by the visiting physician or advanced practice provider that day. It is lost if the patient does not arrive, or cancels within the two business days before the session and the slot is not refilled. Three cases are excluded. A cancellation made earlier than two business days before the session is excluded, because the slot reopens and is normally rebooked. A same-day move to another slot in the same session counts as kept. And sessions canceled by the specialist group are excluded entirely, since no patient could be seen.

On this definition, the kept rate equals kept slots divided by booked slots, and the lost rate equals one minus that figure. Both are reported per session day and summed by month.

Current State

Over the 12 months, the clinic had 1,920 booked slots and 1,440 kept visits, a kept rate of 75.0%. The 480 lost slots average 40 a month. Monthly kept rates ranged from 68% to 91%, with no steady upward or downward drift across the year.

Table 1

Booked, Kept and Lost Slots by Specialty, Twelve Months (Illustrative)

Target

The proposed target is a kept rate of 90%. It is not drawn from an industry figure; it is drawn from the clinic's own record. In its best three months of the year, the clinic kept 89% of booked slots on average, and in its single best month it kept 91%. A target the clinic has already reached for a quarter is demanding but plausible. Reaching it would mean about 16 lost slots a month instead of 40, recovering about 24 visits a month.

For context, a systematic review of 105 studies of missed appointments found an average no-show rate of around 23% across settings (Dantas et al., 2018). Prairie Bend's lost rate, which also includes late cancellations, sits close to that figure, which suggests the clinic is not unusually poor but that its own best months show room to improve.

Where the Gap Sits

Breaking the gap down by specialty shows orthopedics losing the most, at 29%, and cardiology the least, at 22% (Table 1). Weekday cannot be separated from specialty at Prairie Bend, because each specialty comes on a fixed weekday: orthopedics on Mondays, cardiology on Tuesdays and urology on Thursdays. Monday's higher loss rate is therefore the orthopedics rate, and the analysis does not treat weekday as a separate factor.

Lead time, the number of days between booking and the session, shows a much sharper pattern.

Table 2

Lost Slots by Days From Booking to Session, Twelve Months (Illustrative)

Lead Time and Distance

Slots booked more than six weeks ahead were lost at almost four times the rate of slots booked within three weeks, and they account for two-thirds of all lost slots. Orthopedics, with one session day a month, has the largest share of long-lead bookings, which is consistent with its higher overall loss rate. Long lead time is one of the most commonly reported determinants of no-shows in the published literature (Dantas et al., 2018), so the pattern is not surprising, but its size at Prairie Bend is. Advanced access work in primary care reached a similar conclusion: long waits generate missed appointments and rework, and shortening the delay between request and visit reduces both (Murray & Berwick, 2003).

Travel distance, calculated from each patient's home ZIP code to the hospital, shows little pattern.

Table 3

Lost Slots by Patient Travel Distance, Twelve Months (Illustrative)

What the Gap Costs

The gap has two consequences. First, about 40 residents a month who were booked to see a specialist do not see one at Prairie Bend; some are seen later, some travel to the regional city and some are not seen at all. The clinic's records cannot yet say which. Second, the hospital pays a fixed session fee for each specialist day, so a lost slot is a share of a fee already paid. At a round illustrative session fee of $3,000 per day and 40 slots per day, each lost slot represents about $75 in fee paid for no visit, or about $3,000 a month, the cost of one full session day. These figures will be replaced by the finance office's own in the business case. Lost visits carry costs in larger systems too, where a 12-year review of administrative data at one medical center found no-show rates highest in subspecialty clinics (Kheirkhah et al., 2016).

The next step is to ask why long-lead bookings are lost so often. This paper deliberately stops before that question, because answering it is the work of the root cause analysis.

References

Dantas, L. F., Fleck, J. L., Cyrino Oliveira, F. L., & Hamacher, S. (2018). No-shows in appointment scheduling: A systematic literature review. Health Policy, 122(4), 412-421. https://doi.org/10.1016/j.healthpol.2018.02.002

Kheirkhah, P., Feng, Q., Travis, L. M., Tavakoli-Tabasi, S., & Sharafkhaneh, A. (2016). Prevalence, predictors and economic consequences of no-shows. BMC Health Services Research, 16, Article 13. https://doi.org/10.1186/s12913-015-1243-z

Murray, M., & Berwick, D. M. (2003). Advanced access: Reducing waiting and delays in primary care. JAMA, 289(8), 1035-1040. https://doi.org/10.1001/jama.289.8.1035