HLTH 5693 Module 2 Capstone Evidence Review Example

Six Sources on Late First Cases, Not Sixty on Operating Room Efficiency: A Focused Evidence Review for the Start-Time Capstone

Student Name

American College of Education

HLTH5693: Capstone Experience for Healthcare Administration

Module 2 Assignment

Instructor Name

January 22, 2029

Search and Inclusion

The capstone's problem is narrow: first cases of the day in a community hospital's eight operating rooms start late 59 percent of the time. Operating room efficiency is a large literature covering turnover, block scheduling, case duration prediction and staffing models, and most of it does not bear on this problem. The search therefore used PubMed and CINAHL with terms combining first case, on-time start, start time and delay with operating room or operating theater, for studies published since 2005. Studies were included if they measured or tried to improve the start time of first cases in hospital operating rooms. They were excluded if they studied turnover time only, ambulatory surgery centers only, endoscopy suites or cardiac catheterization laboratories, where the preparation process differs, or pediatric or specialty settings without a general surgical mix.

The search returned many reports of single-site improvement projects. From these, six sources were selected because each answers a different capstone question with usable data: one systematic review, three improvement studies with before-and-after data, one analysis of the statistical pattern of start times and one economic analysis. A capstone review earns its length by what it leaves out.

How the Sources Map to the Questions

The capstone asks five questions, and the six sources were chosen to cover the first four. For the question of how often and by how much first cases start late, Tiwari et al. (2018) supply a method: look at the whole distribution of start times, including its spread, not only the share on time. For causes, Saul et al. (2022) and Phieffer et al. (2017) provide attributed delay data from a community and an academic setting. For interventions, Halim et al. (2018) summarize the field and Phieffer et al. (2017) and Kane et al. (2021) show a broad and a narrow approach with measured results. For financial value, Dexter and Epstein (2009) provide the method that keeps the estimate honest. The fifth question, on implementation requirements, depends on the hospital's own policies and is addressed in the recommendations module, where regulatory and staffing sources will be added.

What Causes Late First Cases

The most directly comparable study examined an orthopedic department at a community teaching hospital. Saul et al. (2022) reviewed 398 first cases over 159 days and found that 39.2 percent started late. When a cause was assigned, surgeon practices accounted for 56.5 percent of delays, preoperative processes for 18.3 percent, room-related causes for 13.0 percent, anesthesia for 6.9 percent and patients for 5.3 percent. A fishbone analysis added patient factors, inefficiency in preoperative work and staff tardiness. At a large academic center, Phieffer et al. (2017) reported that the most common delay owners were the patient, the surgeon, the facility and the anesthesia department.

These findings suggest that surgeons and preoperative processes are the usual leading causes, but they also show that causes differ by setting and by how delays are assigned. In both studies, delays were attributed by staff to a single owner, which can hide delays with several contributing causes. The capstone's own analysis will therefore measure the time each preoperative step is completed, not only the reason code entered by the circulating nurse.

Which Interventions Have Worked

The 14 studies pooled by Halim et al. (2018) reported gains in start times from financial incentives, education, system-based techniques, better communication, confirming the first patient's readiness the day before and structured programs for theater productivity, but concluded that it remains unclear which approach is most effective, how long gains last and whether results generalize beyond their setting. Individual studies show the range of effects. Phieffer et al. (2017) used Six Sigma methods across 26 operating rooms, with changes to preoperative readiness, informatics support and continuous measurement and feedback; on-time starts rose from 49 percent to a peak of 92 percent and remained at 78 percent a year later.

A narrower intervention produced a smaller effect. At a tertiary academic hospital, Kane et al. (2021) required attending surgeons to confirm their availability each morning by badge swipe or text message; on-time starts rose from 61.6 percent to 66.9 percent. Together, the studies suggest that multi-part programs addressing preoperative readiness and measurement produce larger gains than single measures aimed at one group, though the designs do not allow a strong comparison.

Why Improvement Works and Whether It Lasts

Tiwari et al. (2018) asked a useful question about a successful first-case program: did it work because staff started earlier, or because the process became more consistent? Analyzing 28,882 first cases, they found that the median delay fell from 5 to 2 minutes and the interquartile range of start times narrowed from 13 to 10 minutes, while preoperative activities did not start earlier. The improvement came from bringing the process under control, which means staff did not need to arrive earlier, and the gains were sustained. This matters for the capstone because a recommendation to start everyone 15 minutes earlier would add labor cost without addressing variation. The Phieffer et al. (2017) results, which declined from 92 to 78 percent after the peak, show that some erosion is common once attention moves on, which the implementation module will need to address.

What Savings Are Realistic

Dexter and Epstein (2009), using three years of data from a six-room surgical facility, calculated that a minute trimmed from late first starts bought back a little over a minute of paid staff time, and that the saving appeared only where a room's list, turnovers included, filled more than an eight-hour day. Their conclusion was that typical savings equal the product of that ratio, the reduction in tardiness per room, the number of rooms running longer than eight hours and the labor cost per scheduled minute, which is often smaller than committees assume. For the capstone, this means the financial analysis must count how many of the hospital's rooms actually run full days before estimating savings, and must not multiply every late minute by the full cost of operating room time.

Appraisal and Implications

The evidence is useful but not strong. Apart from the systematic review, the sources are single-site studies, most with before-and-after designs and no comparison group, so secular trends and the attention effect of a project could explain part of their results. Delay attribution relies on staff coding. The economic analysis comes from one facility, and cost figures from one state may not transfer. Against these limits, the sources agree on several points: causes are usually concentrated in surgeon arrival and preoperative readiness, programs that combine process redesign with measurement do better than single measures, control of variation matters more than earlier start times and savings must be calculated carefully.

For the next module, the evidence sets three tasks: measure each preoperative step's completion time to identify where variation begins, test whether this hospital's delays follow the surgeon and preoperative pattern and count how many rooms run long enough for reduced tardiness to reduce paid time. The literature cannot say what causes late starts in this hospital; it can only say where to look first.

References

Dexter, F., & Epstein, R. H. (2009). Typical savings from each minute reduction in tardy first case of the day starts. Anesthesia & Analgesia, 108(4), 1262-1267. https://doi.org/10.1213/ane.0b013e31819775cd

Halim, U. A., Khan, M. A., & Ali, A. M. (2018). Strategies to improve start time in the operating theatre: A systematic review. Journal of Medical Systems, 42(9), Article 160. https://doi.org/10.1007/s10916-018-1015-5

Kane, W. J., Shilling, A. M., & Schroen, A. T. (2021). A surgeon badge or text message sign-in intervention improves operating room start efficiency. Journal of Surgical Research, 264, 129-137. https://doi.org/10.1016/j.jss.2021.02.009

Phieffer, L., Hefner, J. L., Rahmanian, A., Swartz, J., Ellison, C. E., Harter, R., Lumbley, J., & Moffatt-Bruce, S. D. (2017). Improving operating room efficiency: First case on-time start project. Journal for Healthcare Quality, 39(5), e70-e78. https://doi.org/10.1097/JHQ.0000000000000018

Saul, B., Ketelaar, E., Yaish, A., Wagner, M., Comrie, R., Brannan, G. D., Restini, C., & Balancio, M. (2022). Assessing root causes of first case on-time start (FCOTS) delay in the orthopedic department at a busy level II community teaching hospital. Spartan Medical Research Journal, 7(2), Article 36719. https://doi.org/10.51894/001c.36719

Tiwari, V., Ehrenfeld, J. M., & Sandberg, W. S. (2018). Does a first-case on-time-start initiative achieve its goal by starting the entire process earlier or by tightening the distribution of start times? British Journal of Anaesthesia, 121(5), 1148-1155. https://doi.org/10.1016/j.bja.2018.05.043