Identifying virtual care modality in electronic health record data

2024  Journal Article

Identifying virtual care modality in electronic health record data

Pub TLDR

This study contributes to the field of healthcare by providing reliable methods to identify virtual care modalities using electronic health record (EHR) data, highlighting the preferences and needs of underserved patient populations, and underscoring the importance of policy considerations in the provision of virtual care.

These conclusions have broad implications for healthcare providers, policymakers, and researchers in optimizing virtual care delivery, ensuring equitable access to healthcare services, and informing future healthcare policies.

 

College of Health researcher(s)

OSU Profile

Highlights

The study's main conclusions and their significance to the broader field of healthcare and real-world applications are as follows:

  1. Development of Two Algorithms: The study successfully developed two algorithms to identify virtual care modalities, one prioritizing telehealth mode data and the other prioritizing billing code data. This achievement is significant as it addresses the challenge of accurately determining virtual care modalities in healthcare datasets, which has been a notable difficulty due to the lack of consensus on what EHR information to utilize.
  2. High Agreement Rate Between Algorithms: The algorithms demonstrated a high overall agreement rate of 96.5% for all visits and 89.3% for virtual care visits. This high level of agreement underscores the algorithms' reliability and potential applicability in real-world settings, providing a robust method for healthcare providers and researchers to understand and analyze the distribution of virtual care modalities.
  3. Preference for Audio-Only Visits Among Safety Net Clinic Patients: The study found that safety net clinic patients and their providers were more likely to utilize audio-only visits for virtual care than video visits. This finding is crucial for informing healthcare policy and practice, especially in designing virtual care services that are accessible and equitable for all patient populations, including those served by safety net clinics.
  4. Implications for Measuring and Optimizing Virtual Care Visits: The results have significant implications for measuring and optimizing the use of virtual care visits. Accurately capturing billing information is essential for healthcare providers to avoid lost revenue or fraud. The study suggests that the algorithm prioritizing billing code data may most accurately capture visit modality, highlighting the importance of precise billing practices in virtual care.
  5. Policy Changes and Their Potential Impact on Disparities: The study discusses upcoming policy changes, such as the Centers for Medicare and Medicaid Services ruling to no longer reimburse for audio-only visits after December 31, 2024. This policy change could exacerbate disparities in access to and use of healthcare among patients who are already less likely to access needed and recommended care. The study's findings on the preference for audio-only visits among certain patient populations underscore the potential negative impact of such policy changes on healthcare equity

Abstract

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.

Larson, A.E., Stange, K.C., Heintzman, J., Nishiike, Y., McGrath, B.M., Davis, M.M., Harvey, S.M.(2024)Identifying virtual care modality in electronic health record dataLearning Health Systems