CREAT: Census Research Exploration and Analysis Tool

The Underserved Have Less Access to Employer-Sponsored Telemedicine Coverage

September 2022

Working Paper Number:

CES-22-40

Abstract

Telemedicine has been proposed as one means of improving health care access for underserved communities, and information about insurance coverage for telemedicine (TMC) is important in understanding its utilization and provision. We use 2018-2019 Medical Expenditure Panel Survey-Insurance Component data on employer-sponsored coverage to examine pre-pandemic TMC relative to employer, worker, and health plan characteristics. We find that the share of employees in private sector establishments offering TMC was lower in the most rural counties, in smaller firms, in establishments without unionized employees, and in establishments where most workers were low wage, part-time and older when compared to other establishments. These findings reflect differences across establishments in insurance offers, as well as differences in TMC conditional on an insurance offer, which suggests that TMC may function as a premium plan feature with limited availability and potential support for improving healthcare access for the underserved.

Document Tags and Keywords

Keywords Keywords are automatically generated using KeyBERT, a powerful and innovative keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant keywords.

By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the text, highlighting the most significant topics and trends. This approach not only enhances searchability but provides connections that go beyond potentially domain-specific author-defined keywords.
:
survey, insurance, workforce, policy, coverage, premium, healthcare, medicare, health, medicaid, health insurance, insurance employer, insured, benefit, enrollee, insurance plan, pandemic

Tags Tags are automatically generated using a pretrained language model from spaCy, which excels at several tasks, including entity tagging.

The model is able to label words and phrases by part-of-speech, including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are identified to contain references to specific institutions, datasets, and other organizations.
:
Medical Expenditure Panel Survey, Agency for Healthcare Research and Quality, Department of Health and Human Services, AHRQ Medical Expenditure Panel Survey Insurance Component, Disclosure Review Board, Data Management System, Centers for Medicare

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