IHP 640 Module 6 Milestone Two Example

Milestone Two: What the Data Say About Delays in Highland Valley's Operating Rooms

[Student Name]

Southern New Hampshire University

IHP 640: Measurement, Analysis, & Models for Performance Improvement

Module Six Milestone Two

[Instructor Name]

[Date]

The organization, setting and figures below are a composite written as a model document. No real employer, client, colleague or patient is described.

Milestone Two: What the Data Say About Delays in Highland Valley's Operating Rooms

Milestone One recorded many suspected causes of delay, each group blaming another. This milestone tests those claims with twelve months of operating room data: 3,500 weekday first cases, of which 1,610 started late, and 8,700 turnovers between consecutive cases. It uses control charts to confirm the baseline, Pareto analysis to rank first-case delay reasons and regression to identify what predicts prolonged turnovers.

Confirming a Stable Baseline

Before looking for causes, it helps to know whether performance is stable or shifting. Following the tutorial by Mohammed et al. (2008), limits come from a baseline period, and any point beyond them or any unusual run marks a special cause. A p chart of the monthly first-case on-time rate, averaging 54% with about 290 first cases a month, gives limits of roughly 45% to 63%. All twelve months fell within those limits with no runs, so the problem is a stable feature of the current system rather than the result of a few bad months.

Coding Delay Reasons

Circulating nurses record a delay reason for every late first case from a standard list. The analyst reviewed 100 randomly chosen late cases against chart notes and found the recorded reason matched the evidence in 83%, with most mismatches recorded as other when a specific reason applied. Those were recoded before analysis.

Pareto Analysis of Late First Cases

A Pareto chart ranks causes by frequency to show where effort will pay off most. Three causes accounted for 70% of late first cases: patients not ready in preoperative holding, most often because of missing consent or history and physical documents, at 34%; surgeons arriving after the scheduled start at 22%; and incomplete anesthesia evaluations at 14%. Instrument and equipment problems, rooms not ready and transport delays together made up 24%.

Table 1. Causes of Late First Cases, Twelve Months

Note. Composite data for 1,610 late weekday first cases.

Patterns by Day and Service

Late starts were more common on Mondays, 61%, than on Thursdays, 42%; a chi-square test across weekdays gave p below 0.001. Monday first cases more often involved patients whose preoperative testing was completed the previous Friday, leaving documents unfiled over the weekend. Orthopedic first cases ran late more often than other services, largely because of consent and site-marking delays.

Why the Schedule Matters

Wachtel and Dexter (2009) showed that lateness grows as the day goes on and depends heavily on schedule construction, including whether case durations are estimated accurately. At Highland Valley, scheduled durations underestimated actual times by a median of 11 minutes for orthopedic cases, meaning later cases in those rooms started late even when the first case began on time. Improving duration estimates is therefore part of the solution.

Modeling Prolonged Turnovers

To learn which turnovers become prolonged, the analyst fit a logistic regression with prolonged turnover, over 60 minutes with no scheduled gap, as the outcome. Candidate predictors were chosen in advance from staff interviews: whether the next case involved a different surgeon or service, the number of instrument sets required, whether the turnover occurred after noon, whether environmental services had fewer than three staff assigned to the suite and whether the next case was orthopedic.

Is the Model Stable?

Peduzzi et al. (1996) used simulation to show that logistic models lose reliability once outcome events per predictor drop into single digits. With 1,131 prolonged turnovers and five predictors, this model has more than 200 events per predictor, well above that threshold.

What Predicts Long Turnovers

A change of surgeon or service between cases more than doubled the odds of a prolonged turnover. Needing more than three instrument sets and short environmental services staffing each raised the odds by about 70% to 80%. Afternoon turnovers and orthopedic next cases had smaller effects. A companion linear regression on turnover minutes, limited to turnovers under 60 minutes, found similar patterns: a service change added about 10 minutes and short environmental services staffing about 5.

Table 2. Logistic Regression of Prolonged Turnovers

Note. Composite analysis of 8,700 turnovers; 1,131 prolonged.

Testing the Blame Stories

The data support some claims and refute others. Nurses' view that surgeons cause most late starts is partly supported, at 22%, but patient readiness is a larger cause. Anesthesia's view that sterile processing drives turnovers finds some support in the instrument set result, but staffing and service changes matter as much. No single department owns the problem.

Limitations

Delay reasons are recorded by nurses and may reflect their perspective; the audit reduced but did not eliminate this bias. The regression shows associations, not proof that changing a predictor will change turnover, and unmeasured factors such as case complexity may confound results. Recovery room holds were not coded in this data set and are examined in the capacity modeling paper.

Implications for Improvement

The analysis points to four targets: getting documents and consent completed before the day of surgery, especially for Monday cases; surgeon arrival expectations; completing anesthesia evaluations in advance; and, for turnovers, grouping cases by service, preassembling instrument sets and aligning environmental services staffing with the afternoon peak. Each target has a clear owner among the departments involved, which should make accountability easier than in past efforts.

Conclusion

A stable baseline, a clear Pareto pattern and consistent regression results show that Highland Valley's delays arise from a few specific, fixable process failures spread across several departments. That evidence gives the improvement plan focus and gives every group a shared picture of the problem. The next step is to design changes aimed at each target and test them on a small scale.

References

Mohammed, M. A., Worthington, P., & Woodall, W. H. (2008). Plotting basic control charts: Tutorial notes for healthcare practitioners. Quality and Safety in Health Care, 17(2), 137-145. https://doi.org/10.1136/qshc.2004.012047

Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996). A simulation study of the number of events per variable in logistic regression analysis. Journal of Clinical Epidemiology, 49(12), 1373-1379. https://doi.org/10.1016/S0895-4356(96)00236-3

Wachtel, R. E., & Dexter, F. (2009). Influence of the operating room schedule on tardiness from scheduled start times. Anesthesia & Analgesia, 108(6), 1889-1901. https://doi.org/10.1213/ane.0b013e31819f9f0c