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.

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.

Abstract P372: Drivers of Visit Modality in Primary Care Clinics at an Academic Medical Center

Background: Informed by drivers of visit modality in specialty clinics, we sought to identify factors associated with visit modality selection (telemedicine vs. in-person) for new and return visits in primary care clinics at a large academic medical center (AMC).

Methods: We used electronic health record data from 3/2020 - 5/2022 from 13 primary care clinics with 98 unique clinicians for new and 131 for return visits (21,031 new & 207,292 return visits), with about 55% telemedicine (telephone and video) use. We used hierarchical logistic regression and cross-validation methods to estimate the variation in visit modality associated with the patient, clinician, and visit factors (measured with Area Under the Curve).

Results: For both new and return visits, there was significant variation in telemedicine use among clinicians (ranging from 0 - 100%) for specific clinical diagnoses (Figure 1). Most variation in telemedicine use was attributed to the clinician seen (new visits) and primary visit diagnosis (return visits). Other visit and patient characteristics were less predictive. For new visits, the clinician model had an AUC of 0.79, followed by clinic site 0.69, whereas for return visits, the primary diagnosis was 0.77, followed by the clinician seen at 0.65. Diagnoses most commonly seen via telemedicine include acute respiratory infection and suspected COVID-19 exposure. Diagnoses commonly seen in-person include annual physical and gynecological exams.

Conclusions: Our findings show high variability in telemedicine use for the same diagnosis and differential drivers of visit modality between new and return visits in primary care clinics at a large AMC. Provider ID was the most predictive factor of visit modality for new patients, indicating that clinician preference and individual practice patterns influence the modality of care much more than patient preference. Primary care clinics may reduce the variability in visit modality through standardized processes that integrate clinical factors and patient preference.

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.

1149. Reducing Piperacillin/Tazobactam Use in Children with Acute Perforated Appendicitis

Background

Children with perforated appendicitis have more frequent complications compared with nonperforated appendicitis. Existing data suggest broad-spectrum antibiotics are not superior to narrow-spectrum antibiotics for this condition. In an effort to safely decrease broad-spectrum antibiotic use at our hospital, we evaluated the impact of an antimicrobial stewardship program (ASP) intervention on the use of piperacillin/tazobactam (PT) and clinical outcomes in children with perforated appendicitis.

Methods

Single-center, retrospective cohort study of children ≤ 18 years with perforated appendicitis who underwent primary appendectomy. Children with primary nonoperative management or interval appendectomy were excluded. Prior to the intervention, children at our hospital routinely received PT for perforated appendicitis. An electronic health record (EHR)-integrated guideline that recommended ceftriaxone and metronidazole for perforated appendicitis was released on July 1, 2017 (Figure 1). We compared PT utilization, measured in days of therapy (DOT) per 1,000 patient-days, and clinical outcomes before and after the intervention.

Results

A total of 74 children with perforated appendicitis were identified: 23 during the pre-intervention period (June 1, 2016 to June 30, 2017) and 51 post-intervention (July 1, 2017 to September 30, 2018). Thirty-three patients (45%) were female and the median age was 8 years (IQR: 5–11.75 years). Post-intervention rate of guideline compliance was 84%. PT use decreased from 556 DOT per 1000 patient-days to 131 DOT per 1000 patient-days; incidence rate ratio of 0.24 (95% CI: 0.16–0.35), post-intervention vs. pre-intervention. There was no statistically significant difference in duration of intravenous antibiotics, total antibiotic duration, postoperative length of stay (LOS), total LOS, ED visits/readmission, or surgical site infection (SSI) between pre- and post-intervention periods (Table 1).

Conclusion

An EHR-integrated ASP intervention targeting children with perforated appendicitis resulted in decreased broad-spectrum antibiotic use with no statistically significant difference in clinical outcomes. Larger, multicenter trials are needed to confirm our findings.

Disclosures

All authors: No reported disclosures.

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.