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Building a Better Staffing Model: Using Predictive Workload Analysis for Biomedical Technician Staffing

Bohman, Jacklyn BS, MS, EIT, CCE; Day, Michael BS, MS, MBA; Gandillon, Rebecca BS, MS; Kusters, Andrew BS; Newman, Jason BS, ME; Pourcillie, Ryan BS, MS

doi: 10.1097/JCE.0000000000000072
FEATURE ARTICLES

A model was built using data from the Department of Veterans Affairs (VA), to predict frontline staffing using predictive workload analysis. This model attempts to improve on previous models that utilized the number of medical devices and medical equipment asset value. The predictive workload model is, on average, within 0.6 full-time equivalent of the ideal staffing levels obtained from experienced clinical engineers at 5 VA medical centers. This average deviation is an improvement over the deviations resulting from previous staffing models. There are still limitations to this model, including applicability to facilities outside the VA and scalability at small facilities.

Corresponding author: Rebecca Gandillon, BS, MS, is a biomedical engineer at St Louis VA Healthcare System, Missouri. She can be reached at Rebecca.Gandillon@va.gov.

Jacklyn Bohman, BS, MS, EIT, CCE, is a biomedical engineer at Clement J. Zablocki VA Medical Center in Milwaukee, Wisconsin.

Michael Day, BS, MS, MBA, is the chief biomedical engineer at Robley Rex VA Medical Center in Louisville, Kentucky.

Andrew Kusters, BS, is a biomedical engineer for Clinical Information Systems at Clement J. Zablocki VA Medical Center in Milwaukee, Wisconsin.

Jason Newman is the chief of biomedical engineering for Veterans Integrated Service Network 16 and is based in Houston, Texas.

Jason Newman, BS, ME, is the chief of biomedical engineering for Veterans Integrated Service Network 16 and is based in Houston, Texas.

Ryan Pourcillie, BS, MS, is a Technical Career Field biomedical engineer at the Center for Engineering Occupational Safety and Health in St Louis, Missouri.

The author declares no conflicts of interest.

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