Longitudinal Monitoring of Clinician-Patient Video Visits During the Peak of the COVID-19 Pandemic: Adoption and Sustained Challenges in an Integrated Health Care Delivery System.

BACKGROUND: Numerous prior opinion papers, administrative electronic health record data studies, and cross-sectional surveys of telehealth during the pandemic have been published, but none have combined assessments of video visit success monitoring with longitudinal assessments of perceived challenges to the rapid adoption of video visits during the pandemic. OBJECTIVE: This study aims to quantify (1) the use of video visits (compared with in-person and telephone visits) over time during the pandemic, (2) video visit successful connection rates, and (3) changes in perceived video visit challenges. METHODS: A web-based survey was developed for the dual purpose of monitoring and improving video visit implementation in our health care system during the COVID-19 pandemic. The survey included questions regarding rates of in-person, telephone, and video visits for clinician-patient encounters; the rate of successful connection for video visits; and perceived challenges to video visits (eg, software, hardware, bandwidth, and technology literacy). The survey was distributed via email to physicians, advanced practice professionals, and clinicians in May 2020. The survey was repeated in March 2021. Differences between the 2020 and 2021 responses were adjusted for within-respondent correlation across surveys and tested using generalized estimating equations. RESULTS: A total of 1126 surveys were completed (511 surveys in 2020 and 615 surveys in 2021). In 2020, only 21.7% (73/336) of clinicians reported no difficulty connecting with patients during video visits and 28.6% (93/325) of clinicians reported no difficulty in 2021. The distribution of the percentage of successfully connected video visits ("Over the past two weeks of scheduled visits, what percentage did you successfully connect with patients by video?") was not significantly different between 2020 and 2021 (P=.74). Challenges in conducting video visits persisted over time. Poor connectivity was the most common challenge reported by clinicians. This response increased over time, with 30.5% (156/511) selecting it as a challenge in 2020 and 37.1% (228/615) in 2021 (P=.01). Patients not having access to their electronic health record portals was also a commonly reported challenge (109/511, 21.3% in 2020 and 137/615, 22.3% in 2021, P=.73). CONCLUSIONS: During the pandemic, our health care delivery system rapidly adopted synchronous patient-clinician communication using video visits. As experience with video visits increased, the reported failure rate did not significantly decline, and clinicians continued to report challenges related to general network connectivity and patient access to technology.

Monitoring the Implementation of Tobacco Cessation Support Tools: Using Novel Electronic Health Record Activity Metrics.

BACKGROUND: Clinical decision support (CDS) tools in electronic health records (EHRs) are often used as core strategies to support quality improvement programs in the clinical setting. Monitoring the impact (intended and unintended) of these tools is crucial for program evaluation and adaptation. Existing approaches for monitoring typically rely on health care providers' self-reports or direct observation of clinical workflows, which require substantial data collection efforts and are prone to reporting bias. OBJECTIVE: This study aims to develop a novel monitoring method leveraging EHR activity data and demonstrate its use in monitoring the CDS tools implemented by a tobacco cessation program sponsored by the National Cancer Institute's Cancer Center Cessation Initiative (C3I). METHODS: We developed EHR-based metrics to monitor the implementation of two CDS tools: (1) a screening alert reminding clinic staff to complete the smoking assessment and (2) a support alert prompting health care providers to discuss support and treatment options, including referral to a cessation clinic. Using EHR activity data, we measured the completion (encounter-level alert completion rate) and burden (the number of times an alert was fired before completion and time spent handling the alert) of the CDS tools. We report metrics tracked for 12 months post implementation, comparing 7 cancer clinics (2 clinics implemented the screening alert and 5 implemented both alerts) within a C3I center, and identify areas to improve alert design and adoption. RESULTS: The screening alert fired in 5121 encounters during the 12 months post implementation. The encounter-level alert completion rate (clinic staff acknowledged completion of screening in EHR: 0.55; clinic staff completed EHR documentation of screening results: 0.32) remained stable over time but varied considerably across clinics. The support alert fired in 1074 encounters during the 12 months. Providers acted upon (ie, not postponed) the support alert in 87.3% (n=938) of encounters, identified a patient ready to quit in 12% (n=129) of encounters, and ordered a referral to the cessation clinic in 2% (n=22) of encounters. With respect to alert burden, on average, both alerts fired over 2 times (screening alert: 2.7; support alert: 2.1) before completion; time spent postponing the screening alert was similar to completing (52 vs 53 seconds) the alert, and time spent postponing the support alert was more than completing (67 vs 50 seconds) the alert per encounter. These findings inform four areas where the alert design and use can be improved: (1) improving alert adoption and completion through local adaptation, (2) improving support alert efficacy by additional strategies including training in provider-patient communication, (3) improving the accuracy of tracking for alert completion, and (4) balancing alert efficacy with the burden. CONCLUSIONS: EHR activity metrics were able to monitor the success and burden of tobacco cessation alerts, allowing for a more nuanced understanding of potential trade-offs associated with alert implementation. These metrics can be used to guide implementation adaptation and are scalable across diverse settings.