Publications
The Center exists in part to create original evidence and information that support and advance conversations around professionalism, value, and other health care issues.
READ about scientific publications, briefs, and reports emerging from the Center and its collaborators below.
Self-Reported Panel Size Among Family Physicians Declined by Over 25% Over a Decade (2013-2022)
- Andrew Bazemore
Submitted on: September, 2024
Underinvestment in primary care and erosion of the primary care physician workforce are resulting in patients across the US experiencing growing difficulty in obtaining access to primary care. Compounding this access problem, we find that the average patient panel size among US family physicians may have decreased by 25% over the past decade (2013 to 2022). Reversing the decline in access to primary care in the face of decreasing panel sizes requires both better supporting family physicians to manage larger panels, such as by expanding primary care teams, and substantially increasing the supply of family physicians.
Read MoreImpact of response bias in three surveys on primary care providers’ experiences with electronic health records
Submitted on: June, 2024
Physicians in primary care spend more time documenting care than other physicians and also coordinate care for their patients with other specialists, so it is vital to have high quality data sources about how they use EHRs. In particular, it is important to find policies that maximize the benefits of EHRs while minimizing their potential to add to physicians’ burdens. Thus, in this study, we compared primary care physicians’ (PCPs’) responses to three surveys, each intended to gather information on physicians’ use of EHRs but fielded with substantially different strategies: (1) the 2021 NEHRS; (2) the 2022 Continuous Certification Questionnaire (CCQ) from the American Board of Family Medicine (ABFM); and (3) the inaugural version of University of California, San Francisco (UCSF) Physician Health IT Survey, which was also fielded in 2022.
Read MoreWhat Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care
- Richard A. Young
Submitted on: May, 2024
Primary care physicians are likely both excited and apprehensive at the prospects for artificial intelligence (AI) and machine learning (ML). Complexity science may provide insight into which AI/ML applications will most likely affect primary care in the future. AI/ML has successfully diagnosed some diseases from digital images, helped with administrative tasks such as writing notes in the electronic record by converting voice to text, and organized information from multiple sources within a health care system. AI/ML has less successfully recommended treatments for patients with complicated single diseases such as cancer; or improved diagnosing, patient shared decision making, and treating patients with multiple comorbidities and social determinant challenges.
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