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Health Services Research & Development

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IIR 20-309 – HSR&D Study

 
IIR 20-309
Developing a New Method to Improve Primary Care Workforce Management
Karin M. Nelson MD MSHS
Seattle, WA
Funding Period: July 2022 - June 2025

Abstract

Background: The Office of Primary Care (OPC) is charged with overseeing primary care in the Department of Veterans Affairs (VA). In 2010, VA primary care moved to a medical home model called the Patient Aligned Care Team (PACT). Over 5.7 million Veterans are assigned, or “empaneled”, to a primary care provider and team. Current VA panel sizes are based on historic means and there are concerns that these metrics should be updated given high rates of burnout among primary care providers. However, the evidence base is thin, and a recent a recent HSR&D Evidence Synthesis Program (ESP) report calls for greater evidence to manage the primary care work force of 7,995 primary care physicians and providers (PCP). Significance/Impact: This study will assist the VA with managing its primary care workforce to balance the needs for efficiency and the wellness of the workforce. The novel methods in this study will set the stage for future randomized program evaluations that can test ways to improve efficiency and outcomes for providers and patients. Innovation: The goal of the proposed study is to provide foundational evidence on primary care panel size by adapting and extending a recently developed methodology to assess the workload of primary care patients. The results of this study have a high probability of immediate impact in the VA by informing primary care work force management, in addition to providing evidence that longitudinal patient histories can be used to estimate time and workload for primary care panels. Specific Aims: Aim 1: To describe weekly demand, weekly capacity, and weekly overflow (i.e., demand > capacity) for every PCP and how these parameters vary by PCP, by team, by clinic and by health care system. Aim 2: To determine whether system-level funding is associated with PCP overflow. We will examine the association between the system funding and PCP overflow. Aim 3: To identify which patient events are leading to overflow. This aim examines random variation in weekly patient flow to determine whether random “shocks” in patient events are leading to overflow. Aim 4: To examine overflow in relation to workforce health, measured by provider burnout and turnover. Methodology: The proposed study extends a recently developed methodology to assess the workload of primary care patients and use this workload to develop time estimates for caring for populations of patients. This approach, recently published by Rossi et al. (2018), uses longitudinal data to enumerate patient-related events, or transactions, including both face to face visits and non-face to face care. These transactions are then combined with time estimates per transaction to determine the time it would take to care for a panel of patients. The method provides information on the flow of patients and the demand on primary care providers, and allows for calculation of overflow, where demand would exceed capacity to supply services. We have piloted this method in one health care system in VA and have demonstrated the feasibility of using this method for VA data and have calculated plausible estimates for overflow hours. Implementation/Next steps: The current proposal builds a methodology for tracking panel management. These results have implications for primary care workforce management in VA and other large integrated health systems. It will be through future randomized program evaluations, in combination with the tools we are building, that we will be able to understand the causal effects of policies on PCP productivity, while tracking adverse outcomes.

External Links for this Project

NIH Reporter

Grant Number: I01HX003349-01A2
Link: https://reporter.nih.gov/project-details/10425164



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PUBLICATIONS:

None at this time.

DRA: Health Systems
DRE: TRL - Applied/Translational
Keywords: Quality Improvement
MeSH Terms: None at this time.

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