Identifying virtual care modality in electronic health record data.

Area of Science:

• Health Informatics
• Healthcare Accessibility
• Digital Health Equity

Background:

• The COVID-19 pandemic accelerated virtual care adoption, highlighting the need to understand different modalities (video, audio, eVisits, eConsults, remote patient monitoring).
• Inconsistent methods for identifying virtual care modalities within electronic health records (EHR) hinder accurate usage analysis.
• This study addresses the gap by developing a method to identify virtual care modalities and describing their distribution over time.

Purpose of the Study:

• To develop and validate a method for classifying virtual care modalities using EHR data.
• To describe the distribution of virtual care modalities in primary care safety net clinics over a three-year period.

Main Methods:

• Utilized EHR data from 316 primary care safety net clinics between April 2020 and March 2023.
• Developed two algorithms, informed by expert consultation, to classify virtual care visit modalities based on different EHR data elements.
• Conducted descriptive analyses to compare algorithm agreement and the frequency of different virtual care modalities.

Main Results:

• Algorithms demonstrated high agreement (96.5% for all visits, 89.3% for virtual care visits).
• Discrepancies often occurred with audio-only visits billed as video visits.
• Audio-only visits were twice as common as video visits when algorithms agreed on modality.

Conclusions:

• Virtual care modality classification is sensitive to the EHR data elements prioritized by algorithms.
• Safety net clinics heavily rely on audio-only and video visits for virtual care delivery.
• Potential elimination of reimbursement for audio-only visits could worsen health inequities for low-income populations.