Visit and Between-Visit Interaction Frequency Before and After COVID-19 Telehealth Implementation

Abstract

ImportanceTelehealth implementation associated with the COVID-19 public health emergency (PHE) affected patient–clinical team interactions in numerous ways. Yet, studies have narrowly examined billed patient-clinician visits rather than including visits with other team members (eg, pharmacists) or between-visit interactions.ObjectiveTo evaluate rates of change over time in visits (in-person, telehealth) and between-visit interactions (telephone calls, patient portal messages) overall and by key patient characteristics.Design, Setting, and ParticipantsThis retrospective cohort study included adults with diabetes receiving primary care at urban academic (University of California San Francisco [UCSF]) and safety-net (San Francisco Health Network [SFHN]) health care systems. Encounters from April 2019 to March 2021 were analyzed.ExposureTelehealth implementation over 3 periods: pre-PHE (April 2019 to March 2020), strict shelter-in-place (April to June 2020), and hybrid-PHE (July 2020 to March 2021).Main Outcomes and MeasuresThe main outcomes were rates of change in monthly mean number of total encounters, visits with any health care team member, visits with billing clinicians, and between-visit interactions. Key patient-level characteristics were age, race and ethnicity, language, and neighborhood socioeconomic status (nSES).ResultsOf 15 148 patients (4976 UCSF; 8975 SFHN) included, 2464 (16%) were 75 years or older, 7734 (51%) were female patients, 9823 (65%) self-identified as racially or ethnically minoritized, 6223 (41%) had a non-English language preference, and 4618 (31%) lived in the lowest nSES quintile. After accounting for changes to care delivery through an interrupted time-series analysis, total encounters increased in the hybrid-PHE period (UCSF: 2.3% per patient/mo; 95% CI, 1.6%-2.9% per patient/mo; SFHN: 1.8% per patient/mo, 95% CI, 1.3%-2.2% per patient/mo), associated primarily with growth in between-visit interactions (UCSF: 3.1% per patient/mo, 95% CI, 2.3%-3.8% per patient/mo; SFHN: 2.9% per patient/mo, 95% CI, 2.3%-3.4% per patient/mo). In contrast, rates of visits were stable during the hybrid-PHE period. Although there were fewer differences in visit use by key patient-level characteristics during the hybrid-PHE period, pre-PHE differences in between-visit interactions persisted during the hybrid-PHE period at SFHN. Asian and Chinese-speaking patients at SFHN had fewer monthly mean between-visit interactions compared with White patients (0.46 [95% CI, 0.42-0.50] vs 0.59 [95% CI, 0.53-0.66] between-visit interactions/patient/mo; P < .001) and English-speaking patients (0.52 [95% CI, 0.47-0.58] vs 0.61 [95% CI, 0.56-0.66] between-visit interactions/patient/mo; P = .03).Conclusions and RelevanceIn this study, pre-PHE growth in overall patient-clinician encounters persisted after PHE-related telehealth implementation, driven in both periods by between-visit interactions. Differential utilization based on patient characteristics was observed, which may indicate disparities. The implications for health care team workload and patient outcomes are unknown, particularly regarding between-visit interactions. Therefore, to comprehensively understand care utilization for patients with chronic diseases, research should expand beyond billed visits.

Applying Machine Learning Techniques to Implementation Science

“…The need for social distancing and the uptake of telemedicine during the COVID-19 pandemic led to a rise in patient portal use, which could improve the reach of such strategies [ 88 ]. The uptake in patient portal during the pandemic was also associated with a rise in “e-visits,” which were communications between patients and clinicians between in-person visits [ 89 , 90 ]. This led to health care systems to bill for these messages following existing federal rules [ 90 , 91 ], which in turn may limit the use of patient portals and impact their effectiveness as an implementation strategy.…”

Racial and Ethnic Differences in Telemedicine Use

“…For example, a report from the US Department of Health and Human Services found that Hispanic individuals had substantially higher rates of telemedicine use than White individuals . Other studies have found the opposite, that Black and Hispanic individuals are less likely to use telemedicine than White individuals . Similarly, mixed results in telemedicine use have been found by income or other markers of socioeconomic status …”

“…However, Black and Hispanic individuals in those communities may be less likely to use telemedicine than their White neighbors. This may explain why several studies that used data from a single health system, in which patients typically come from within a limited geographic area, found lower rates of telemedicine use among Black and Hispanic individuals. The question of differences in telemedicine use by race and ethnicity and what underlies those differences is critical to guide policy interventions.…”