Background and Goal: Large-scale, well organized, and open datasets are necessary for primary care–focused artificial intelligence and machine learning (AI/ML) research and development. This article proposes a set of high-level considerations around the data transformation needed to enable the growth of AI/ML applications in primary care.
Key Insights: The authors propose five key considerations for data transformation in primary care: automation of data collection, organization of fragmented data, identification of primary care–specific use cases, integration of AI/ML into human workflows, and surveillance for unintended consequences. The authors further emphasize three factors that will enable each of these efforts to be effective and work cohesively: increased collaboration of the industry and academia AI/ML communities with primary care, increased funding from the private and public sectors, and upgrades to human and data infrastructures.
Why It Matters: Data transformation to advance AI/ML research and implementation in primary care requires cross-sectoral collaborations between government, industry, professional organizations, academia, and frontline primary care.
Data Transformation to Advance AI/ML Research and Implementation in Primary Care
Timothy Tsai, DO, MMCI, et al
Stanford Healthcare AI Applied Research Team, Division of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, California
The Annals of Family Medicine