Drivers of telemedicine in primary care clinics at a large academic medical centre.

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.

Sociodemographic disparities in the use of cardiovascular ambulatory care and telemedicine during the COVID-19 pandemic.

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.

Telemedicine and the environment: life cycle environmental emissions from in-person and virtual clinic visits.

Concern over climate change is growing in the healthcare space, and telemedicine has been rapidly expanding since the start of the COVID19 pandemic. Understanding the various sources of environmental emissions from clinic visits-both virtual and in-person-will help create a more sustainable healthcare system. This study uses a Life Cycle Assessment with retrospective clinical data from Stanford Health Care (SHC) in 2019-2021 to determine the environmental emissions associated with in-person and virtual clinic visits. SHC saw 13% increase in clinic visits, but due to the rise in telemedicine services, the Greenhouse Gas emissions (GHGs) from these visits decreased 36% between 2019 and 2021. Telemedicine (phone and video appointments) helped SHC avoid approximately 17,000 metric tons of GHGs in 2021. Some departments, such as psychiatry and cancer achieved greater GHG reductions, as they were able to perform more virtual visits. Telemedicine is an important component for the reduction of GHGs in healthcare systems; however, telemedicine cannot replace every clinic visit and proper triaging and tracking systems should be in place to avoid duplicative care.

Drivers of variation in telemedicine use during the COVID-19 pandemic: The experience of a large academic cardiovascular practice.

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.

Changes in telemedicine use and ambulatory visit volumes at a multispecialty cardiovascular center during the COVID-19 pandemic.

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.

Use of Prospective Audit and Feedback to Reduce Antibiotic Exposure in a Pediatric Cardiac ICU.

OBJECTIVES: We sought to determine whether a prospective audit and feedback intervention decreased antibiotic utilization in a pediatric cardiac ICU and to describe the characteristics of prospective audit and feedback audits and recommendations. DESIGN: Before-after study. SETTING: Pediatric cardiac ICU of a freestanding children's hospital. PATIENTS: All patients admitted to the cardiac ICU. INTERVENTIONS: A prospective audit and feedback program was established in our hospital's pediatric cardiac ICU on December 7, 2015. The antimicrobial stewardship program audited IV antibiotics, communicated prospective audit and feedback recommendations to the cardiac ICU, and regularly reviewed recommendation adherence. Mean monthly antibiotic utilization 18 months before ("preprospective audit and feedback"; from June 1, 2014 to November 30, 2015) and 24 months after ("prospective audit and feedback"; from January 1, 2016 to December 31, 2017) prospective audit and feedback implementation was compared. Antibiotic audit data during the prospective audit and feedback period were reviewed to capture the characteristics of prospective audit and feedback audits, recommendations, and adherence. MEASUREMENTS AND MAIN RESULTS: Mean cardiac ICU IV antibiotic use decreased 20% (701 vs 880 days of therapy per 1,000 patient days, p = 0.001) during the prospective audit and feedback period compared with the preprospective audit and feedback period. There was no difference in mean cardiac ICU length of stay (p = 0.573), mean hospital length of stay (p = 0.722), or the rate of discharge due to death (p = 0.541). There were 988 antibiotic audits and 370 prospective audit and feedback recommendations (37% recommendation rate) during the study period. The most commonly audited antibiotic category was broad-spectrum gram-negative agents and the most common indication for use was sepsis. Broad-spectrum gram-positive agents were more likely to be associated with a recommendation. CONCLUSIONS: There was a significant reduction in antibiotic use following implementation of a prospective audit and feedback program in our pediatric cardiac ICU. Over one-third of antibiotics audited in our cardiac ICU were associated with a prospective audit and feedback recommendation, revealing important targets for future antimicrobial stewardship efforts in this population.

Online disease management of diabetes: engaging and motivating patients online with enhanced resources-diabetes (EMPOWER-D), a randomized controlled trial.

OBJECTIVE: To evaluate an online disease management system supporting patients with uncontrolled type 2 diabetes. MATERIALS AND METHODS: Engaging and Motivating Patients Online With Enhanced Resources for Diabetes was a 12-month parallel randomized controlled trial of 415 patients with type 2 diabetes with baseline glycosylated hemoglobin (A1C) values ≥7.5% from primary care sites sharing an electronic health record. The intervention included: (1) wirelessly uploaded home glucometer readings with graphical feedback; (2) comprehensive patient-specific diabetes summary status report; (3) nutrition and exercise logs; (4) insulin record; (5) online messaging with the patient's health team; (6) nurse care manager and dietitian providing advice and medication management; and (7) personalized text and video educational 'nuggets' dispensed electronically by the care team. A1C was the primary outcome variable. RESULTS: Compared with usual care (UC, n=189), patients in the intervention (INT, n=193) group had significantly reduced A1C at 6 months (-1.32% INT vs -0.66% UC; p<0.001). At 12 months, the differences were not significant (-1.14% INT vs -0.95% UC; p=0.133). In post hoc analysis, significantly more INT patients had improved diabetes control (>0.5% reduction in A1C) than UC patients at 12 months (69.9 (95% CI 63.2 to 76.5) vs 55.4 (95% CI 48.4 to 62.5); p=0.006). CONCLUSIONS: A nurse-led, multidisciplinary health team can manage a population of diabetic patients in an online disease management program. INT patients achieved greater decreases in A1C at 6 months than UC patients, but the differences were not sustained at 12 months. More INT than UC patients achieved improvement in A1C (>0.5% decrease). Trial registered in clinical trials.gov: #NCT00542204.

Comparison of methodologies for calculating quality measures based on administrative data versus clinical data from an electronic health record system: implications for performance measures.

New reimbursement policies and pay-for-performance programs to reward providers for producing better outcomes are proliferating. Although electronic health record (EHR) systems could provide essential clinical data upon which to base quality measures, most metrics in use were derived from administrative claims data. We compared commonly used quality measures calculated from administrative data to those derived from clinical data in an EHR based on a random sample of 125 charts of Medicare patients with diabetes. Using standard definitions based on administrative data (which require two visits with an encounter diagnosis of diabetes during the measurement period), only 75% of diabetics determined by manually reviewing the EHR (the gold standard) were identified. In contrast, 97% of diabetics were identified using coded information in the EHR. The discrepancies in identified patients resulted in statistically significant differences in the quality measures for frequency of HbA1c testing, control of blood pressure, frequency of testing for urine protein, and frequency of eye exams for diabetic patients. New development of standardized quality measures should shift from claims-based measures to clinically based measures that can be derived from coded information in an EHR. Using data from EHRs will also leverage their clinical content without adding burden to the care process.