Coffey, Jordan

e13890 Background: Many oncology practices have reported continued use of telemedicine since onset of the COVID-19 pandemic. However, the future sustainability of telemedicine within oncology practices remains unclear, particularly because federal policies that enabled adoption in response to the public health emergency (PHE) have not been made permanent. Understanding similarities and differences in telemedicine utilization at high-volume oncology centers is crucial for shaping evidence-based policy that enables equitable access to oncology care across varying geographies. Methods: This is a cross-sectional, year-over-year, retrospective cohort analysis of trends in telemedicine utilization across all medical oncology clinical appointments conducted among adult patients (age >18) from January 2019 to December 2023 at Stanford Medicine Cancer Center (SM) – comprising multiple campuses in the California Bay Area – and the Mayo Clinic Comprehensive Cancer Center (MC) – a multiregional cancer practice with tertiary referral campuses in Minnesota, Florida, and Arizona, as well as community-based clinics throughout the Upper Midwest. Results: Both institutions exhibited a similar trend in telemedicine adoption over time. This was characterized by an initial surge in utilization following the PHE declaration in March 2020, followed by sustained utilization rates from 2021 to 2023. However, telemedicine use as a proportion of the overall practice differed significantly between institutions. At SM, the rate of telemedicine visits as a proportion of overall clinical practice was 60.3% (49,136/ 81,502) in 2021, 59.9% (52,247/ 87,215) in 2022, and 57.2% (55,007/ 96,163) in 2023. At MC, the proportion of telemedicine utilization was 12.8% in 2021 (23,873/186,086), 14.7% in 2022 (28,985/196,777), and 14.0% in 2023 (30,208/215,941). Deployment in the setting of new versus return visits also differed between institutions. In 2023, telemedicine accounted for 53.7% (6,440/11,998) of new and 57.7% (48,567/84,165) of return visits at SM, compared to 15.5% (3,359/21,728) and 13.8% (26,849/194,213) at MC. Additionally, while video visits were the predominant modality used to conduct telemedicine visits at both institutions from 2021 onward, in 2023 phone visits still made up 17.9% (5,394/30,208) of all telemedicine visits at MC, compared to just 5.4% (2,990/55,007) at SM. Conclusions: Stanford Medicine and Mayo Clinic exhibited a pattern of initial growth of telemedicine adoption in 2020, followed by sustained utilization from 2021-2023. We observed unique differences in the overall proportion, visit type deployment, and modality of telemedicine utilization between institutions. However, this study confirms telemedicine visits have remained a fixture of medical oncology care for many patients at these institutions since the COVID-19 pandemic.

The demand for telehealth services in the United States saw an unprecedented increase following the 2020 COVID-19 pandemic. Prior to the pandemic, intake and virtual rooming functions for scheduled, synchronous video appointments at Mayo Clinic were supported by a small centralised team of virtual agents within its Center for Connected Care (Connected Care). In response to the pandemic-generated demand, Connected Care leveraged its existing model and the support of temporary staff to rapidly scale support for the increased video visit demand across the enterprise. Once demand stabilised in the summer of 2020, Mayo Clinic’s destination medical centres in Minnesota, Florida and Arizona, and Mayo Clinic Health System’s sites in the Midwest, launched pilots to evaluate alternative telehealth delivery models. The goal of these pilots was to test the flexibility of alternative intake models for supporting long-term scalability and growth. Through these pilots, each site developed unique workflows and staffing plans with the overall intake function remaining standardised across all locations. The workflows delineated responsibilities between clinical departments and virtual agents, leveraged current infrastructure and maximised best practices from the original model. This paper outlines the models implemented, and presents lessons learned and best practices garnered from each implementation.

Introduction While chimeric antigen receptor (CAR)-T cell therapy has transformed the management of hematologic malignancies, it requires frequent monitoring for acute toxicities, almost universally achieved through mandatory hospitalizations, therefore utilizing a significant amount of healthcare resources. At Mayo Clinic in Rochester, MN, our standard practice is administering CAR-T cell therapy in the outpatient setting through a multi-disciplinary hospital-based outpatient (HBO) unit and remote patient monitoring (RPM) program. Our CAR-T RPM program consists of in-home, electronic health record-integrated technology to monitor vital signs and neurologic symptoms for 30 days post CAR-T infusion. Here we report the healthcare utilization outcomes of CAR-T patients managed in the outpatient setting enrolled in the RPM program. Methods Patients undergoing FDA-approved CAR-T cell treatment in the outpatient setting and participating in the CAR-T RPM program at Mayo Clinic Rochester were included in this IRB-approved study. Patient characteristics, healthcare utilization metrics, inpatient interventions, and patient satisfaction data regarding the RPM program were reviewed. Length of stay (LOS) includes any hospitalizations for which the admission date falls within the RPM monitoring period (CAR-T cell infusion to day +30), even if the hospitalization continued past day +30. Results A total of 123 patients participated in the CAR-T RPM program and underwent treatment in the outpatient setting. Median age at CAR-T infusion was 63 years (range 22 - 83), and most patients were male (n=78, 63%). Non-Hodgkin lymphoma was the most common diagnosis (n=74, 60%), followed by multiple myeloma (n=47, 38%), ALL (n=1, 1%) and other (n=1, 1%). During the PRM monitoring period, 19 (16%) patients were managed entirely in the outpatient setting. The remaining 104 (84%) patients required at least one hospitalization, with most patients (n=70, 57%) hospitalized only once [26 (21%) patients were hospitalized twice, and 7 (6%) patients were hospitalized 3 times, and 1 (1%) patient hospitalized 4 times during the monitoring period]. When considering the first hospitalization post CAR-T infusion, the median LOS was 6 days (range 2 - 47). When considering all hospitalizations post CAR-T, the median LOS was 8 days (range 2 - 47). Only 7 (6%) patients required an ICU stay, with a mean ICU stay of 0.2 days (standard deviation 0.9). Cytokine release syndrome (CRS) was noted in 92 (75%) of patients at a median of 3 days (range 0 - 18) post CAR-T infusion. All patients with CRS received tocilizumab, at a median of 4 days post CAR-T infusion. Steroids for CRS were administered in 63 (68%) of these patients. Neurotoxicity was reported in 45 (37%) patients at a median of 7 days (range 1 - 19) post CAR-T infusion. During the RPM monitoring period, patients were seen in our HBO unit with a median of 18 outpatient visits (range 4 - 30). Patient survey data, obtained at the end of the RPM monitoring period, were available from 37 patients. The majority of patients felt that the RPM program helped them feel comfortable managing their health at home, were able to reach a team member, and receive an appropriate response promptly. The majority of patients felt comfortable using the RPM equipment and interacting with providers virtually. Overall, 83% of patients were satisfied with the RPM program and 6% were neither satisfied nor dissatisfied. When asked about the reason for disappointment, technical difficulties/issues with the devices were the themes mentioned by the few patients (11%) who were unsatisfied with the RPM program. Conclusion: Despite the complexities and frequent monitoring associated with CAR-T cell therapy, outpatient CAR-T cell treatment is a feasible model of care. As the standard practice at our institution, the CAR-T RPM program was part of the management of nearly all patients undergoing an FDA-approved CAR-T product in the outpatient setting. RPM in the acute setting post CAR-T infusion is a well-received practice initiative by the majority of patients, including a high level of comfort utilizing the RPM equipment and virtual interaction with the RPM providers.

Electronic health record (EHR) systems collate patient data, and the integration and standardization of documents through Health Information Exchange (HIE) play a pivotal role in refining patient management. Although the clinical implications of AI in EHR systems have been extensively analyzed, its application in HIE as a crucial source of patient data is less explored. Addressing this gap, our systematic review delves into utilizing AI models in HIE, gauging their predictive prowess and potential limitations. Employing databases such as Scopus, CINAHL, Google Scholar, PubMed/Medline, and Web of Science and adhering to the PRISMA guidelines, we unearthed 1021 publications. Of these, 11 were shortlisted for the final analysis. A noticeable preference for machine learning models in prognosticating clinical results, notably in oncology and cardiac failures, was evident. The metrics displayed AUC values ranging between 61% and 99.91%. Sensitivity metrics spanned from 12% to 96.50%, specificity from 76.30% to 98.80%, positive predictive values varied from 83.70% to 94.10%, and negative predictive values between 94.10% and 99.10%. Despite variations in specific metrics, AI models drawing on HIE data unfailingly showcased commendable predictive proficiency in clinical verdicts, emphasizing the transformative potential of melding AI with HIE. However, variations in sensitivity highlight underlying challenges. As healthcare’s path becomes more enmeshed with AI, a well-rounded, enlightened approach is pivotal to guarantee the delivery of trustworthy and effective AI-augmented healthcare solutions.

Telemedicine approaches provide many benefits to patients across both primary and specialty care. Patient acceptance is imperative to successful telemedicine implementation. As telemedicine utilization continues to surge, it is imperative that healthcare organizations have a method for evaluating the patient experience with these types of visits. Previous studies on experience with telemedicine have focused on smaller patient populations with narrow inclusion criteria and limited geographical reach. This research described how patients’ satisfaction with video telemedicine-based visits varied based on patient characteristics and how they compare with in-person visits. We obtained and analyzed results from standardized patient experience surveys to compare telemedicine and in-person clinic visits during the COVID-19 pandemic (between July 1, 2020, and June 30, 2021) across a diverse patient population. During the study timeframe, surveys were sent to 1,521,398 patients with a response rate of 20% (307,185). Our organization’s unique structure, size, and geographic spread allowed for a deeper and more comprehensive examination of telemedicine participants. Though a few trends emerged in the results, there were no significant differences in patient ratings of telemedicine visits and in-person clinic visits during the same period (p=0.672). This study demonstrated that patient satisfaction with telemedicine visits was non-inferior to in-person outpatient visits during the study timeframe. Experience Framework This article is associated with the Innovation & Technology lens of The Beryl Institute Experience Framework (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens

ImportanceThere was a shift in patient volume from in-person to video telemedicine visits during the COVID-19 pandemic.ObjectiveTo determine the concordance of provisional diagnoses established at a video telemedicine visit with diagnoses established at an in-person visit for patients presenting with a new clinical problem.Design, Setting, and ParticipantsThis is a diagnostic study of patients who underwent a video telemedicine consultation followed by an in-person outpatient visit for the same clinical problem in the same specialty within a 90-day window. The provisional diagnosis made during the video telemedicine visit was compared with the reference standard diagnosis by 2 blinded, independent medical reviewers. A multivariate logistic regression model was used to determine factors significantly related to diagnostic concordance. The study was conducted at a large academic integrated multispecialty health care institution (Mayo Clinic locations in Rochester, Minnesota; Scottsdale and Phoenix, Arizona; and Jacksonville, Florida; and Mayo Clinic Health System locations in Iowa, Wisconsin, and Minnesota) between March 24 and June 24, 2020. Participants included Mayo Clinic patients residing in the US without age restriction. Data analysis was performed from December 2020 to June 2021.ExposuresNew clinical problem assessed via video telemedicine visit to home using Zoom Care Anyplace integrated into Epic.Main Outcomes and MeasuresConcordance of provisional diagnoses established over video telemedicine visits compared against a reference standard diagnosis.ResultsThere were 2393 participants in the analysis. The median (IQR) age of patients was 53 (37-64) years; 1381 (57.7%) identified as female, and 1012 (42.3%) identified as male. Overall, the provisional diagnosis established over video telemedicine visit was concordant with the in-person reference standard diagnosis in 2080 of 2393 cases (86.9%; 95% CI, 85.6%-88.3%). Diagnostic concordance by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision chapter ranged from 64.7% (95% CI, 42.0%-87.4%) for diseases of the ear and mastoid process to 96.8% (95% CI, 94.7%-98.8%) for neoplasms. Diagnostic concordance by medical specialty ranged from 77.3% (95% CI, 64.9%-89.7%) for otorhinolaryngology to 96.0% (92.1%-99.8%) for psychiatry. Specialty care was found to be significantly more likely than primary care to result in video telemedicine diagnoses concordant with a subsequent in-person visit (odds ratio, 1.69; 95% CI, 1.24-2.30; P < .001).Conclusions and RelevanceThis diagnostic study of video telemedicine visits yielded a high degree of diagnostic concordance compared with in-person visits for most new clinical concerns. Some specific clinical circumstances over video telemedicine were associated with a lower diagnostic concordance, and these patients may benefit from timely in-person follow-up.

PURPOSE: The goal of this study was to assess the impact of an interdisciplinary remote patient monitoring (RPM) program on clinical outcomes and acute care utilization in cancer patients with COVID-19. METHODS: This is a cross-sectional analysis following a prospective observational study performed at Mayo Clinic Cancer Center. Adult patients receiving cancer-directed therapy or in recent remission on active surveillance with polymerase chain reaction–confirmed SARS-CoV-2 infection between March 18 and July 31, 2020, were included. RPM was composed of in-home technology to assess symptoms and physiologic data with centralized nursing and physician oversight. RESULTS: During the study timeframe, 224 patients with cancer were diagnosed with COVID-19. Of the 187 patients (83%) initially managed in the outpatient setting, those who did not receive RPM were significantly more likely to experience hospitalization than those receiving RPM. Following balancing of patient characteristics by inverse propensity score weighting, rates of hospitalization for RPM and non-RPM patients were 2.8% and 13%, respectively, implying that the use of RPM was associated with a 78% relative risk reduction in hospital admission rate (95% CI, 54 to 102; P = .002). Furthermore, when hospitalized, these patients experienced a shorter length of stay and fewer prolonged hospitalizations, intensive care unit admissions, and deaths, although these trends did not reach statistical significance. CONCLUSION: The use of RPM and a centralized virtual care team was associated with a reduction in hospital admission rate and lower overall acute care resource utilization among cancer patients with COVID-19.

1503 Background: Patients with cancer and COVID-19 are at risk for poor clinical outcomes. An established multi-site remote patient monitoring (RPM) service was rapidly adapted to support a novel, interdisciplinary COVID-19 program for outpatient management of patients at high-risk for severe illness. The goal of this study was to assess the impact of the RPM program on clinical outcomes and acute care utilization in cancer patients diagnosed with COVID-19. Methods: This is a cross-sectional analysis following a multi-site prospective observational study performed at Mayo Clinic Cancer Center (MCCC). All adult patients with active cancer – defined as currently receiving cancer-directed therapy or in recent remission on active surveillance – and PCR-confirmed SARS-CoV-2 infection between March 18 and July 31, 2020 were included. RPM was comprised of in-home technology to assess symptoms and physiologic data with centralized nurse and physician oversight. Results: During the study timeframe 224 cancer patients were diagnosed with COVID-19 at MCCC. Initial management included urgent hospitalization (within 48 hours of diagnosis) in 34 patients (15%). Of the remaining 190 patients (85%) initially managed in the outpatient setting, those who did not receive RPM were significantly more likely to experience hospitalization than those receiving RPM (OR 3.6, 95% CI 1.036 to 12.01, P = 0.044). Following balancing of patient characteristics by inverse propensity weighting, rates of hospital admission for RPM and non-RPM patients were 3.1% and 11% respectively, implying that RPM was associated with an 8% reduction in hospital admission rate (-0.077; 95% CI: -0.315 to -0.019, P = 0.009). Use of RPM was also associated with lower rates of prolonged hospitalization, ICU admission, and mortality, though these trends did not reach statistical significance. Conclusions: In the midst of a global pandemic associated with inpatient bed, ventilator, and PPE shortages, the RPM program provided an effective strategy for outpatient clinical management and was associated with decreased rates of hospitalization, ICU admission, and mortality in cancer patients with COVID-19. This care model enabled simultaneous opportunity to mitigate the increased risks of exposure, transmission, and resource utilization associated with conventional care.