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Background: Virtual care increased dramatically during the COVID‐19 pandemic. The specific modality of virtual care (video, audio, eVisits, eConsults, and remote patient monitoring) has important implications for the accessibility and quality of care, but rates of use are relatively unknown. Methods for identifying virtual care modalities, especially in electronic health records (EHR) are inconsistent. This study (a) developed a method to identify virtual care modalities using EHR data and (b) described the distribution of these modalities over a 3‐year study period. Methods: EHR data from 316 primary care safety net clinics throughout the study period (4/1/2020‐3/31/2023) were included. Visit type (in‐person vs virtual) by adults >18 years old were classified. Expert consultation informed the development of two algorithms to classify virtual care visit modalities; these algorithms prioritized different EHR data elements. We conducted descriptive analyses comparing algorithms and the frequency of virtual care modalities. Results: Agreement between the algorithms was 96.5% for all visits and 89.3% for virtual care visits. The majority of disagreement between the algorithms was among encounters scheduled as audio‐only but billed as a video visit. Restricting to visits where the algorithms agreed on visit modality, there were 2‐fold more audio‐only than video visits. Conclusion: Visit modality classification varies depending upon which data in the EHR are prioritized. Regardless of which algorithm is utilized, safety net clinics rely on audio‐only and video visits to provide care in virtual visits. Elimination of reimbursement for audio visits may exacerbate existing inequities in care for low‐income patients. [ABSTRACT FROM AUTHOR]

Objective Data Sources and Study Setting Study Design Data Collection/Extraction Methods Principal Findings Conclusion To compare morbidity burden captured from multimorbidity indices and aggregated measures of clinically meaningful categories captured in primary care community‐based health center (CBHC) patients.Electronic health records of patients seen in 2019 in OCHIN's national network of CBHCs serving patients in rural and underserved communities.Age‐stratified analyses comparing the most common conditions captured by the Charlson, Elixhauser, and Multimorbidity Weighted (MWI) indices, and Classification Software Refined (CCSR) and Chronic Condition Indicator (CCI) algorithms.Active ICD‐10 conditions on patients' problem list in 2019.Approximately 35%–56% of patients with at least one condition are not captured by the Charlson, Elixhauser, and MWI indices. When stratified by age, this range broadens to 9%–90% with higher percentages in younger patients. The CCSR and CCI reflect a broader range of acute and chronic conditions prevalent among CBHC patients.Three commonly used indices to capture morbidity burden reflect conditions most prevalent among older adults, but do not capture those on problem lists for younger CBHC patients. An index with an expanded range of care conditions is needed to understand the complex care provided to primary care populations across the lifespan. [ABSTRACT FROM AUTHOR]