HLTH 6882 Week 4 Five Whys Root Cause Analysis Example

Root Cause Analysis: Why Specialist Slots Booked Six or More Weeks Ahead Go Unused at Prairie Bend

Student Name

Walden University

HLTH 6882: Improving Healthcare Performance Through Social Entrepreneurship

Instructor Name

Month Day, Year

Root Cause Analysis: Why Specialist Slots Booked Six or More Weeks Ahead Go Unused at Prairie Bend

Introduction

Last week's gap analysis found that slots booked 43 or more days before the session day at Prairie Bend Community Hospital's visiting specialist clinic were lost at a rate of 37%, and that these bookings make up most of the clinic's lost slots. This analysis asks why, using a five whys chain. It follows one rule: no answer is accepted until it is checked against the clinic's own records or a documented interview, and only then is the next why asked. One tempting branch, that transport explains the loss, was tested and abandoned, and it is shown below. All figures come from the composite organization and are illustrative.

A Caution About the Method

Five whys is a simple tool with known weaknesses. A critique in a leading quality and safety journal argued that it tends to follow a single causal path when most problems have several, and that its conclusions depend heavily on what the people asking already believe (Card, 2017). This analysis responds in two ways. It requires evidence at every step, so an answer cannot rest on belief alone, and it records a branch that the evidence did not support, so the reader can see that more than one path was tried.

The Chain

The chain runs from the observed problem to a cause the organization controls. Each row gives the question, the answer and the evidence that confirmed it before the next question was asked.

Table 1

Five Whys Chain for Lost Long-Lead Slots, With Evidence (Illustrative)

Reading the Chain

The first two answers describe what patients do, and they are important evidence, but they are not where the chain should stop. A chain that ends at patients forgetting has found a symptom that no organization can change directly. The third answer moves from patient behavior to the clinic's own process: visits are booked far ahead because of how referrals are handled, not because patients ask for distant dates. The fourth explains the booking practice in terms of the scheduler's divided job and the referral log's design. The fifth reaches a condition the hospital owns: an unwritten booking rule that nobody chose deliberately and nobody measures. That is consistent with work on advanced access, which treated long waits as a product of scheduling rules that clinics can redesign rather than as a fixed feature of demand (Murray & Berwick, 2003).

The second step also connects to the literature. A systematic review found long lead time to be one of the factors most often linked to missed appointments (Dantas et al., 2018), and the reasons in the call-back notes, forgetting a date and having been seen elsewhere, are the reasons one would expect to grow as the wait grows.

The Abandoned Branch: Transport

The first explanation offered by several staff was transport: the clinic serves two rural counties, and many patients are older adults who may not drive. If transport were the main cause, lost rates would rise with distance. They barely do. Patients living under 15 miles from the hospital lost 24.0% of their slots, those living 15 to 30 miles away lost 25.0% and those over 30 miles lost 27.0%. Only 108 of the 480 lost slots belonged to patients living more than 30 miles away. In the 60 call-back notes, only 4 patients mentioned a ride as the reason, all of them within the conflict category.

Transport may matter for some patients, and the call-back notes are a small sample, but the data does not support it as the main cause of the loss. The branch is therefore abandoned here, and any change built mainly around transport would contradict this analysis.

The Root Cause

The root cause is a booking rule: every referral is booked into the next available slot on arrival, with no limit on how far ahead a visit can be booked and no list of patients who could fill a slot at short notice. Under that rule, the clinic's monthly session pattern guarantees that a large share of visits are booked six weeks or more ahead, and those are the visits that are lost. The rule persists because it was never written down or reviewed, and because the clinic's monthly report does not show lost slots by lead time.

This is not a finding about the scheduler, who follows the only practice the clinic has ever had while also covering registration. Directing the change at a person would leave the rule in place for the next one. The rule itself is something the outpatient manager can change, and the next weeks will look at how a peer hospital handles the same problem and what a different rule would look like.

References

Card, A. J. (2017). The problem with 5 whys. BMJ Quality & Safety, 26(8), 671-677. https://doi.org/10.1136/bmjqs-2016-005849

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

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