Date of Graduation

Fall 12-16-2017

Document Type

Project

Degree Name

Doctor of Nursing Practice (DNP)

College/School

School of Nursing and Health Professions

Department/Program

Nursing

Program

Healthcare Systems Leadership DNP

First Advisor

Dr. Brian Budds

Second Advisor

Dr. Robin Buccheri

Abstract

Problem: For most hospitals, a major cost is the operating room. Inefficiency increases costs and risks for adverse events. An efficient operating room can also be a major revenue generator.

Context: This evidence-based performance improvement project was conducted in a small rural Veteran’s Hospital, which belongs to an integrated health network in Central California. The facility has four operating rooms and is expanding services provided to their patients.

Intervention: The intervention was the use of analytics and evaluations to improve the operating room efficiency by five percent. The use of 3 separate queries which were combined to generate reports and then some data were entered separately into IBM SPSS 24 for descriptive analytics. The reports provided measures to gauge operating room efficiency.

Measures: The analytic results were broken down into three reports. The first was titled Operating Room Times. The second was titled Operating Room Efficiency, and the third was titled Operating Room Utilization. The first was utilized to discern data errors and missing elements of data and to detect cancellations. The second to measure the difference between scheduled times and actual times. The third was for Operating Room utilization and overtime.

Results: Data errors decreased by 60% whereas cancellations, surgery start, and surgery end variance fluctuated. On-time starts did show some improvement by over 5%. Operating room utilization and overtime did not improve

Conclusion: The project did not achieve its objectives. There was not large buy-in for the project. There are other extenuating factors such as staffing shortages and no beds to admit patients to after surgery that further confounded the data. Data analytics alone cannot improve any area. There must be a desire from top-down for improvement. Plus, there must be a consensus and agreement on what needs to be improved.

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