Validation of use of billing codes for identifying telemedicine encounters in administrative data

A, Clare C. B, Corey J. H, Hari E. The association between rurality, dual Medicare/Medicaid eligibility and chronic conditions with telehealth utilization: An analysis of 2019-2020 national Medicare claims

INTRODUCTION

Telehealth services have the potential to increase healthcare access among underserved populations, such as rural residents and racial/ethnic minority groups. The COVID-19 public health emergency led to unprecedented growth in telehealth utilization, but evidence suggests the growth has not been equitable across all patient populations. This study aimed to explore whether telehealth utilization and expansion changed equitably from 2019 to 2020 among sub-groups of Medicare beneficiaries.

METHODS

We conducted an analysis of telehealth utilization among a 20% random sample of 2019-2020 Medicare beneficiaries on a national level. We fit multivariable logistic regression models and calculated average marginal effects (AME) to assess the association between demographic and clinical characteristics on telehealth utilization.

RESULTS

We found telehealth utilization was less likely among non-Hispanic Black/African-American (2019: adjusted odds ratio [aOR] = 0.77, AME = -0.15; 2020: aOR = 0.85, AME = -3.50) and Hispanic (2019: aOR = 0.79, AME = -0.13; 2020: aOR = 0.87, AME = -2.89) beneficiaries, relative to non-Hispanic White beneficiaries in both 2019 and 2020, with larger disparities in 2020. Rural beneficiaries were more likely to utilize telehealth than urban beneficiaries in 2019 (aOR = 2.62, AME = 0.84), but less likely in 2020 (aOR = 0.57, AME = -14.47). In both years, dually eligible Medicare/Medicaid beneficiaries were more likely than non-dually eligible beneficiaries to utilize telehealth (2019: aOR = 4.75, AME = 0.84; 2020: aOR = 1.34, AME = 2.25). However, the effects of dual eligibility and rurality changed in both models as the number of chronic conditions increased.

DISCUSSION

We found evidence of increasing disparities in telehealth utilization among several Medicare beneficiary sub-groups in 2020 relative to 2019, including individuals of minority race/ethnicity, rural residents, and dually eligible beneficiaries, with disparities increasing among individuals with more chronic conditions. Although telehealth has the potential to address health inequities, our findings suggest that many of the patients in greatest need of healthcare are least likely to utilize telehealth services.

Characteristics of Veterans Experiencing Homelessness using Telehealth for Primary Care Before and After COVID-19 Pandemic Onset

BACKGROUND

The COVID-19 pandemic expanded telehealth use across healthcare systems, including the Veterans Health Administration (VA). Little is known about how large-scale telehealth rollout affected access to primary care for patients experiencing homelessness.

OBJECTIVE

To examine the extent to which homeless-experienced veterans used telehealth services in primary care and to characterize users before and after the onset of the COVID-19 pandemic.

DESIGN

Retrospective cohort study, 3/16/2019-3/15/2022.

PARTICIPANTS

394,731 veterans with homelessness diagnoses nationally using 4,068,109 primary care visits.

MAIN MEASURES

The outcomes were use of 1 + telehealth visits (video, phone, secure messaging) for primary care during each year. Through multivariable regression models, we examined associations between telehealth use, patient characteristics (e.g., age, sex, race-ethnicity, comorbidity), and VA homeless services use (e.g., homeless-tailored primary care (HPACT), permanent supportive housing).

KEY RESULTS

Compared to pre-pandemic, telehealth in primary care among homeless-experienced veterans increased substantially 2 years post-pandemic (video: 1.37% versus 20.56%, phone: 60.74% versus 76.58%). Secure messaging was low over time (1.57-2.63%). In adjusted models, video users were more likely to be young (65 + years: OR = 0.43, CI: 0.42-0.44), women (OR = 1.74, CI: 1.70-1.78), Black (OR = 1.14, CI: 1.12-1.16), Hispanic (OR = 1.34, CI: 1.30-1.38), and with more comorbidities (2 + on the Charlson Comorbidity Index; OR = 1.16, CI: 1.14-1.19), compared to video non-users. HPACT patients were less likely to use video (OR = 0.68, CI: 0.66-0.71) than other primary care patients. This was not observed among users of other VA homeless services.

CONCLUSIONS

Despite decreased access to health information technology and low pre-pandemic telehealth use, veterans experiencing homelessness still sustained high use of telehealth in primary care post-pandemic. Women and racial-ethnic minorities had higher video uptake proportionately, suggesting that telehealth may address access disparities among these homeless-experienced patient groups. Identifying and targeting organizational characteristics (e.g., HPACT users) that predict telehealth use for improvement may be key to increasing adoption among VA primary care patients experiencing homelessness.

Impact of the COVID-19 Pandemic on the Management of Juvenile Idiopathic Arthritis: Analysis of United States Commercial Insurance Data

BACKGROUND/OBJECTIVE

Given limited information on health care and treatment utilization for juvenile idiopathic arthritis (JIA) during the pandemic, we studied JIA-related health care and treatment utilization in a commercially insured retrospective US cohort.

METHODS

We studied rates of outpatient visits, new disease-modifying antirheumatic drug (DMARD) initiations, intra-articular glucocorticoid injections (iaGC), dispensed oral glucocorticoids and opioids, DMARD adherence, and DMARD discontinuation by quarter in March 2018-February 2021 (Q1 started in March). Incident rate ratios (IRR, pandemic vs prepandemic) with 95% confidence intervals (CIs) were estimated using multivariable Poisson or Quasi-Poisson models stratified by diagnosis recency (incident JIA, <12 months ago; prevalent JIA, ≥12 months ago).

RESULTS

Among 1294 children diagnosed with JIA, total and in-person outpatient visits for JIA declined during the pandemic (IRR, 0.88-0.90), most markedly in Q1 2020. Telemedicine visits, while higher during the pandemic, declined from 21% (Q1) to 13% (Q4) in 2020 to 2021. During the pandemic, children with prevalent JIA, but not incident JIA, had lower usage of iaGC (IRR, 0.60; 95% CI, 0.34-1.07), oral glucocorticoids (IRR, 0.47; 95% CI, 0.33-0.67), and opioids (IRR, 0.44; 95% CI, 0.26-0.75). Adherence to and discontinuation of DMARDs was similar before and during the pandemic.

CONCLUSIONS

In the first year of the pandemic, visits for JIA dropped by 10% to 12% in commercially insured children in the United States, declines partly mitigated by use of telemedicine. Pandemic-related declines in intra-articular glucocorticoids, oral glucocorticoids, and opioids were observed for children with prevalent, but not incident, JIA. These changes may have important implications for disease control and quality of life.

Nanayakkara PW, Grover J, Paranjape K, Singh S, Sharma P, Bhoi S, Sinha T, Stawicki SP, Garg M, Sardesai I. The World Health Organization Collaborating Center for Emergency and Trauma (WHO-CCET) in South East Asia, The World Academic Council of Emergency Medicine (WACEM), and The American College of Academic International Medicine (ACAIM) 2021 Framework for using Telemedicine Technology at Healthcare Institutions

The coronavirus disease 2019 crisis has forced the world to integrate telemedicine into health delivery systems in an unprecedented way. To deliver essential care, lawmakers, physicians, patients, payers, and health systems have all adopted telemedicine and redesigned delivery processes with accelerated speed and coordination in a fragmented way without a long-term vision or uniformed standards. There is an opportunity to learn from the experiences gained by this pandemic to help shape a better health-care system that standardizes telemedicine to optimize the overall efficiency of remote health-care delivery. This collaboration focuses on four pillars of telemedicine that will serve as a framework to enable a uniformed, standardized process that allows for remote data capture and quality, aiming to improve ongoing management outside the hospital. In this collaboration, we recommend learning from this experience by proposing a telemedicine framework built on the following four pillars-patient safety and confidentiality; metrics, analytics, and reform; recording of audio-visual data as a health record; and reimbursement and accountability.

ChartSweep: A HIPAA-compliant Tool to Automate Chart Review for Plastic Surgery Research

Retrospective chart review (RCR) is the process of manual patient data review to answer research questions. Large and heterogeneous datasets make the RCR process time-consuming, with potential to introduce errors. The authors therefore designed and developed ChartSweep to expedite the RCR process while remaining faithful to its methodological rigor. ChartSweep is an open-source tool that can be customized for use with any electronic health record system. ChartSweep was developed by the authors to extract information from electronic health records using the Python coding language. As proof-of-concept, the tool was tested in three studies: RCR1-Identification of subjects who underwent radiofrequency ablation in a cohort of patients who had undergone headache surgery (n = 172); RCR2-Identification of patients with a diagnosis of thoracic outlet syndrome in patients who underwent peripheral neuroplasty (n = 806); RCR3-Identification of patients with a history of implant illness or breast implant-associated anaplastic large cell lymphoma in patients who had undergone implant-based breast augmentation or reconstruction (n = 1133). Inter-rater reliability was assessed. ChartSweep reduced the time required to conduct RCR1 by 1315 minutes (21.9 hours), RCR2 by 1664 minutes (27.7 hours), and RCR3 by 2215 minutes (36.9 hours). Inter-rater reliability was uncompromised (k = 1.00). Open-source Python libraries as leveraged by ChartSweep significantly accelerate the RCR process in plastic surgery research. Quality of data review is not compromised. Further analyses with larger, heterogeneous study populations are required to further validate ChartSweep as a research tool.

Ambulatory cardiology telemedicine: a large academic pediatric center experience

We performed a retrospective study of cardiology telemedicine visits at a large academic pediatric center between 2016 and 2019 (pre COVID-19). Telemedicine patient visits were matched to data from their previous in-person visits, to evaluate any significant differences in total charge, insurance compensation, patient payment, percent reimbursement and zero reimbursement. Miles were measured between patient’s home and the address of previous visit. We found statistically significant differences in mean charges of telemedicine versus in-person visits (2019US$) (172.95 vs 218.27, p=0.0046), patient payment for telemedicine visits versus in-person visits (2019US$) (11.13 vs 62.83, p≤0.001), insurance reimbursement (2019US$) (65.18 vs 110.85, p≤0.001) and insurance reimbursement rate (43% vs 61%, p=0.0029). Rate of zero reimbursement was not different. Mean distance from cardiology clinic was 35 miles. No adverse outcomes were detected. This small retrospective study showed cost reduction and a decrease in travel time for families participating in telemedicine visits. Future work is needed to enhance compensation for telemedicine visits.

Telemedicine and Outpatient Subspecialty Visits Among Pediatric Medicaid Beneficiaries

OBJECTIVE

Live interactive telemedicine is increasingly covered by state Medicaid programs, but whether telemedicine is improving equity in utilization of subspecialty care is not known. We examined patterns of telemedicine use for outpatient pediatric subspecialty care within the state Medicaid programs.

METHODS

We identified children ≤17 years old in 2014 Medicaid Analysis eXtract data for 12 states. We identified telemedicine-using and telemedicine-nonusing medical and surgical subspecialists. Among children cared for by telemedicine-using subspecialists, we assessed child and subspecialist characteristics associated with any telemedicine visit using logistic regression with subspecialist-level random effects. Among children cared for by telemedicine-using and nonusing subspecialists, we compared visit rates across child characteristics by assessing negative binomial regression interaction terms.

RESULTS

Of 12,237,770 pediatric Medicaid beneficiaries, 2,051,690 (16.8%) had ≥1 subspecialist visit. Of 42,695 subspecialists identified, 146 (0.3%) had ≥1 telemedicine claim. Among children receiving care from telemedicine-using subspecialists, likelihood of any telemedicine use was increased for rural children (odds ratio [OR] 10.4, 95% confidence interval [CI] 6.3-17.1 compared to large metropolitan referent group) and those >90 miles from the subspecialist (OR 13.4, 95% CI 10.2-17.7 compared to 0-30 mile referent group). Compared to children receiving care from telemedicine-nonusing subspecialists, matched children receiving care from telemedicine-using subspecialists had larger differences in visit rates by distance to care, county rurality, ZIP code median income, and child race/ethnicity (P < .001 for interaction terms).

CONCLUSIONS

Children in rural communities and at distance to subspecialists had increased likelihood of telemedicine use. Use overall was low, and results indicated that early telemedicine policies and implementation did not close disparities in subspecialty visit rates by child geographic and sociodemographic characteristics.