Publication Spotlight: Dr. Plantinga
Interview with Laura Plantinga, PhD, Associate Professor, School of Medicine, University of California, San Francisco, author of Patient Care Technician Staffing in US Hemodialysis Facilities: An Ecological Study.
What question did your study aim to answer?
We aimed to provide a comprehensive epidemiologic description of dialysis patient care technician (PCT) staffing patterns in the United States.
What inspired you to conduct this study?
We had recently completed a survey of U.S. dialysis PCTs, which showed high levels of burnout and turnover intention among these essential frontline workers. Quantifying patterns of U.S. dialysis PCT staffing was critical to understanding the potential for a PCT workforce crisis. While a few studies examining nurse and social worker staffing have been conducted, data on dialysis PCTs specifically were sparse.
Which USRDS datasets did you use to conduct your study?
We used the patient, medical evidence, and facility datasets to conduct our study.
Using plain language, please summarize your study conclusions in two or three points.
- Levels of staffing varied substantially by U.S. state and end-stage renal disease (ESRD) network in 2019, and there was an overall decline from 2004 to 2019 in median patient : PCT ratios (from 10.6 to 9.9), representing an increase in PCT staffing.
- Several facility characteristics were associated with lower levels of PCT staffing, including: not being affiliated with a large dialysis organization, larger facility size, lower levels of nurse and social worker staffing, presence of licensed vocational or practical nurses at the clinic, and being located in a high-poverty area.
- This information can be used to target staff recruitment and retention interventions at facilities where PCT staffing may be more challenging.
Please share a specific insight about working with USRDS data that you learned during the completion of this study.
While we have used the USRDS data for many years, this was our first experience with the Annual Facility Survey (AFS) data. We learned a lot from this study about what information is collected from facilities by CMS and about the structure of the resulting data.