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BACKGROUND: Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI. OBJECTIVE: This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals. METHODS: We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles. RESULTS: Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care. CONCLUSIONS: GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.

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BACKGROUND: COVID-19 disrupted healthcare routines and prompted rapid telemedicine implementation. We investigated the drivers of visit modality selection (telemedicine versus in-person) in primary care clinics at an academic medical centre. METHODS: We used electronic medical record data from March 2020 to May 2022 from 13 primary care clinics (N = 21,031 new, N = 207,292 return visits), with 55% overall telemedicine use. Hierarchical logistic regression and cross-validation methods were used to estimate the variation in visit modality explained by the patient, clinician and visit factors as measured by the mean-test area under the curve (AUC). RESULTS: There was significant variation in telemedicine use across clinicians (ranging from 0-100%) for the same visit diagnosis. The strongest predictors of telemedicine were the clinician seen for new visits (mean AUC of 0.79) and the primary visit diagnosis for return visits (0.77). Models based on all patient characteristics combined accounted for relatively little variation in modality selection, 0.54 for new and 0.58 for return visits, respectively. Amongst patient characteristics, males, patients over 65 years, Asians and patient's with non-English language preferences used less telemedicine; however, those using interpreter services used significantly more telemedicine. CONCLUSION: Clinician seen and primary visit diagnoses were the best predictors of visit modality. The distinction between new and return visits and the minimal impact of patient characteristics on visit modality highlights the complexity of clinical care and warrants research approaches that go beyond linear models to uncover the emergent causal effects of specific technology features mediated by tasks, people and organisations.

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BACKGROUND: The COVID-19 pandemic accelerated adoption of telemedicine in cardiology clinics. Early in the pandemic, there were sociodemographic disparities in telemedicine use. It is unknown if these disparities persisted and whether they were associated with changes in the population of patients accessing care. METHODS: We examined all adult cardiology visits at an academic and an affiliated community practice in Northern California from March 2019 to February 2020 (pre-COVID) and March 2020 to February 2021 (COVID). We compared patient sociodemographic characteristics between these periods. We used logistic regression to assess the association of patient/visit characteristics with visit modality (in-person vs telemedicine and video- vs phone-based telemedicine) during the COVID period. RESULTS: There were 54,948 pre-COVID and 58,940 COVID visits. Telemedicine use increased from <1% to 70.7% of visits (49.7% video, 21.0% phone) during the COVID period. Patient sociodemographic characteristics were similar during both periods. In adjusted analyses, visits for patients from some sociodemographic groups were less likely to be delivered by telemedicine, and when delivered by telemedicine, were less likely to be delivered by video versus phone. The observed disparities in the use of video-based telemedicine were greatest for patients aged ≥80 years (vs age <60, OR 0.24, 95% CI 0.21, 0.28), Black patients (vs non-Hispanic White, OR 0.64, 95% CI 0.56, 0.74), patients with limited English proficiency (vs English proficient, OR 0.52, 95% CI 0.46-0.59), and those on Medicaid (vs privately insured, OR 0.47, 95% CI 0.41-0.54). CONCLUSIONS: During the first year of the pandemic, the sociodemographic characteristics of patients receiving cardiovascular care remained stable, but the modality of care diverged across groups. There were differences in the use of telemedicine vs in-person care and most notably in the use of video- vs phone-based telemedicine. Future studies should examine barriers and outcomes in digital healthcare access across diverse patient groups.

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BACKGROUND: COVID-19 spurred rapid adoption and expansion of telemedicine. We investigated the factors driving visit modality (telemedicine vs. in-person) for outpatient visits at a large cardiovascular center. METHODS: We used electronic health record data from March 2020 to February 2021 from four cardiology subspecialties (general cardiology, electrophysiology, heart failure, and interventional cardiology) at a large academic health system in Northern California. There were 21,912 new and return visits with 69% delivered by telemedicine. We used hierarchical logistic regression and cross-validation methods to estimate the variation in visit modality explained by patient, clinician, and visit factors as measured by the mean area under the curve. RESULTS: Across all subspecialties, the clinician seen was the strongest predictor of telemedicine usage, while primary visit diagnosis was the next most predictive. In general cardiology, the model based on clinician seen had a mean area under the curve of 0.83, the model based on the primary diagnosis had a mean area under the curve of 0.69, and the model based on all patient characteristics combined had a mean area under the curve of 0.56. There was significant variation in telemedicine use across clinicians within each subspecialty, even for visits with the same primary visit diagnosis. CONCLUSION: Individual clinician practice patterns had the largest influence on visit modality across subspecialties in a large cardiovascular medicine practice, while primary diagnosis was less predictive, and patient characteristics even less so. Cardiovascular clinics should reduce variability in visit modality selection through standardized processes that integrate clinical factors and patient preference.

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The Coronavirus Disease 2019 (COVID-19) pandemic curtailed clinical trial activity. Decentralized clinical trials (DCTs) can expand trial access and reduce exposure risk but their feasibility remains uncertain. We evaluated DCT feasibility for atrial fibrillation (AF) patients on oral anticoagulation (OAC). DeTAP (Decentralized Trial in Afib Patients, NCT04471623) was a 6-month, single-arm, 100% virtual study of 100 AF patients on OAC aged >55 years, recruited traditionally and through social media. Participants enrolled and participated virtually using a mobile application and remote blood pressure (BP) and six-lead electrocardiogram (ECG) sensors. Four engagement-based primary endpoints included changes in pre- versus end-of-study OAC adherence (OACA), and % completion of televisits, surveys, and ECG and BP measurements. Secondary endpoints included survey-based nuisance bleeding and patient feedback. 100 subjects (mean age 70 years, 44% women, 90% White) were recruited in 28 days (traditional: 6 pts; social media: 94 pts in 12 days with >300 waitlisted). Study engagement was high: 91% televisits, 85% surveys, and 99% ECG and 99% BP measurement completion. OACA was unchanged at 6 months (baseline: 97 ± 9%, 6 months: 96 ± 15%, p = 0.39). In patients with low baseline OACA (<90%), there was significant 6-month improvement (85 ± 16% to 96 ± 6%, p < 0.01). 86% of respondents (69/80) expressed willingness to continue in a longer trial. The DeTAP study demonstrated rapid recruitment, high engagement, and physiologic reporting via the integration of digital technologies and dedicated study coordination. These findings may inform DCT designs for future cardiovascular trials.

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Early in the COVID-19 pandemic, cardiology clinics rapidly implemented telemedicine to maintain access to care. Little is known about subsequent trends in telemedicine use and visit volumes across cardiology subspecialties. We conducted a retrospective cohort study including all patients with ambulatory visits at a multispecialty cardiovascular center in Northern California from March 2019 to February 2020 (pre-COVID) and March 2020 to February 2021 (COVID). Telemedicine use increased from 3.5% of visits (1200/33,976) during the pre-COVID period to 63.0% (21,251/33,706) during the COVID period. Visit volumes were below pre-COVID levels from March to May 2020 but exceeded pre-COVID levels after June 2020, including when local COVID-19 cases peaked. Telemedicine use was above 75% of visits in all cardiology subspecialties in April 2020 and stabilized at rates ranging from over 95% in electrophysiology to under 25% in heart transplant and vascular medicine. From June 2020 to February 2021, subspecialties delivering a greater percentage of visits through telemedicine experienced larger increases in new patient visits (r = 0.81, p = 0.029). Telemedicine can be used to deliver a significant proportion of outpatient cardiovascular care though utilization varies across subspecialties. Higher rates of telemedicine adoption may increase access to care in cardiology clinics.

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BACKGROUND: In response to the COVID-19 pandemic, telemedicine utilization has increased dramatically, yet most institutions lack a standardized approach to determine how much to invest in these programs. METHODS: We used the Quadruple Aim to evaluate the operational impact of CardioClick, a program replacing in-person follow-up visits with video visits in a preventive cardiology clinic. We examined data for 134 patients enrolled in CardioClick with 181 video follow-up visits and 276 patients enrolled in the clinic's traditional prevention program with 694 in-person follow-up visits. RESULTS: Patients in CardioClick and the cohort receiving in-person care were similar in terms of age (43 vs 45 years), gender balance (74% vs 79% male), and baseline clinical characteristics. Video follow-up visits were shorter than in-person visits in terms of clinician time (median 22 vs 30 min) and total clinic time (median 22 vs 68 min). Video visits were more likely to end on time than in-person visits (71 vs 11%, p < .001). Physicians more often completed video visit documentation on the day of the visit (56 vs 42%, p = .002). CONCLUSIONS: Implementation of video follow-up visits in a preventive cardiology clinic was associated with operational improvements in the areas of efficiency, patient experience, and clinician experience. These benefits in three domains of the Quadruple Aim justify expanded use of telemedicine at our institution. IMPLICATIONS: The Quadruple Aim provides a framework to evaluate telemedicine programs recently implemented in many health systems. LEVEL OF EVIDENCE: Level III (retrospective comparative study).

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BACKGROUND: Telehealth use has increased in specialty clinics, but there is limited evidence on the outcomes of telehealth in primary cardiovascular disease (CVD) prevention. OBJECTIVE: The objective of this study was to evaluate the initial outcomes of CardioClick, a telehealth primary CVD prevention program. METHODS: In 2017, the Stanford South Asian Translational Heart Initiative (a preventive cardiology clinic focused on high-risk South Asian patients) introduced CardioClick, which is a clinical pathway replacing in-person follow-up visits with video visits. We assessed patient engagement and changes in CVD risk factors in CardioClick patients and in a historical in-person cohort from the same clinic. RESULTS: In this study, 118 CardioClick patients and 441 patients who received in-person care were included. CardioClick patients were more likely to complete the clinic's CVD prevention program (76/118, 64.4% vs 173/441, 39.2%, respectively; P<.001) and they did so in lesser time (mean, 250 days vs 307 days, respectively; P<.001) than the patients in the historical in-person cohort. Patients who completed the CardioClick program achieved reductions in CVD risk factors, including blood pressure, lipid concentrations, and BMI, which matched or exceeded those observed in the historical in-person cohort. CONCLUSIONS: Telehealth can be used to deliver care effectively in a preventive cardiology clinic setting and may result in increased patient engagement. Further studies on telehealth outcomes are needed to determine the optimal role of virtual care models across diverse preventive medicine clinics.