The US “Quick COVID-19 Primary Care” Survey: A weekly/Monthly Pulse of Primary Care During the Pandemic

Context: Primary care is the first contact for most entering the health care system, providing a vital relationship and force for integration in an impersonal, fragmented system. The COVID-19 pandemic has exposed the costs of chronic and fragmented under-investment in primary care. There is no organization, federal agency, or national dataset that provides a real-time picture of US primary care practice. A routine yet brief rapid survey of primary care clinicians during the pandemic can provide data to inform policy decisions

Objective: To capture and rapidly disseminate the on-the-ground evolution of primary care transformation and adoption of digital health to address the unique challenges of COVID-19: providing access and comprehensive care while assuring the safety of patients and staff and the survival of their practices.

Study design and Analysis: A rapid cycle survey of national cross-sectional cohorts (with an embedded longitudinal cohort), fielded using a mixed methods exploratory sequential design, first weekly, then monthly, during the pandemic.

Setting: US primary care clinicians practicing in primary care settings.

Dataset: 36,000 surveys collected from 8100 unique individuals, March 2020 to March 2022.

Population studied: clinicians practicing in US primary care settings.

Instrument: a brief survey, taking 3-5 minutes to complete, completed anonymously using a web-based platform

Results: primary care practices were ill-supported during the pandemic, suffering major drawbacks in payment, workforce, patient relationships, and sense of fulfillment, and yet displayed uncommon ability to adapt, steward population health, and act as a safety-net to the population during an extended public health crisis. Examples include extended previously unoffered services, assistance with social drivers, and public health leadership.

Conclusions: Rapid cycle, adaptive surveys can be used effectively to facilitate data-based policy decisions when national datasets are non-existent.