Evidence map›Paper›PMID 42113515›Full record

ArticleJAMA network open2026

Telemedicine Adoption, US Ambulatory Visits, and Total Medical Spending, 2019-2023.

John N Mafi, Sitaram Vangala, Jonathan Cantor, Melody Craff, Manying Cui, Artem Romanov, Ziyi Li, Michelle Rockwell, Chi-Hong Tseng, Dale Skinner and 5 more

Abstract read
In one paragraph

Article in JAMA network open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

John N MafiDivision of General Internal Medicine and Health Services Research, David Geffen School of Medicine at UCLA, Los Angeles, California.
Sitaram VangalaDivision of General Internal Medicine and Health Services Research, David Geffen School of Medicine at UCLA, Los Angeles, California.
Jonathan CantorRAND, Santa Monica, California.
Melody CraffMedInsight, Milliman Inc, Seattle, Washington.
Manying CuiDivision of General Internal Medicine and Health Services Research, David Geffen School of Medicine at UCLA, Los Angeles, California.
Artem RomanovDivision of General Internal Medicine and Health Services Research, David Geffen School of Medicine at UCLA, Los Angeles, California.
Ziyi LiDivision of General Internal Medicine and Health Services Research, David Geffen School of Medicine at UCLA, Los Angeles, California.
Michelle RockwellFamily & Community Medicine, Virginia Tech Carilion School of Medicine, Roanoke.
Chi-Hong TsengDivision of General Internal Medicine and Health Services Research, David Geffen School of Medicine at UCLA, Los Angeles, California.
Dale SkinnerMedInsight, Milliman Inc, Seattle, Washington.
Michael HadfieldMedInsight, Milliman Inc, Seattle, Washington.
A Mark FendrickInternal Medicine, Health Management and Policy, University of Michigan Medical School, Ann Arbor.
Cheryl L DambergRAND, Santa Monica, California.
Catherine SarkisianDivision of General Internal Medicine and Health Services Research, David Geffen School of Medicine at UCLA, Los Angeles, California.
Katherine L KahnDivision of General Internal Medicine and Health Services Research, David Geffen School of Medicine at UCLA, Los Angeles, California.

Funding

Midcareer Award in Patient-Oriented Community-Academic Partnered Aging ResearchK24AG047899 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SARKISIAN, CATHERINE A · 2014 to 2025
$2.0M
NIA NIH HHS K24 AG047899
6 · The paper itself

Abstract

Importance: Telemedicine is now widely used, stimulated by pandemic-era expansion rules and payment parity to in-person visits. Lawmakers continue to consider how to revise existing policies because of uncertainty about the potential for telemedicine to increase utilization and spending. Objective: To quantify the association between telemedicine adoption and visits and spending. Design, Setting, and Participants: This cohort study used multipayer medical claims data from MedInsight's research database for a national sample of adults continuously enrolled in Medicare fee-for-service, Medicare Advantage, dual-eligible, Medicaid, or commercial insurance, from January 1, 2019, to October 31, 2023. Data were analyzed from April 30, 2024, to February 10, 2026. Exposure: Regional telemedicine adoption (measured at the hospital referral region [HRR] level). Main Outcomes and Measures: The primary outcomes were (1) total combined telemedicine and in-person ambulatory visits (primary care, specialist, and preventive screening visits), and (2) total combined per-member-per-month spending on professional, inpatient, facility outpatient, prescription drug, and ancillary payments. By use of difference-in-differences analyses on visit-level and spending-level changes before (January 1, 2019, to December 31, 2019) vs after (January 1, 2021, to October 31, 2023) telemedicine expansion in high-telemedicine vs low-telemedicine adoption quintiles (measured at the HRR level), age-adjusted, sex-adjusted, and diagnosis-adjusted Poisson regressions were estimated, accounting for repeated measurements over time. Analysis was also stratified by urbanicity, payer, and Centers for Disease Control and Prevention Social Vulnerability Index quintiles to explore heterogeneous associations. Results: The sample included 3.04 million US individuals (mean [SD] age, 54.2 [17.2] years; 55.7% female) who utilized 120 million visits and incurred $178.4 billion in spending during 2019 to 2023. In 2019, the mean (SD) visit rate was 0.66 (0.035), and the mean (SD) spending rate was $774.59 ($36.78) per-member-per-month. Overall, point estimates suggested high-adopting areas had 2.4% (95% CI, -8.1% to 3.6%) fewer visits and 0.5% (95% CI, -13.1% to 13.9%) lower spending; however, 95% CIs crossed the null. Similarly, point estimates varied across subgroups but none achieved statistical significance: there were 4.4% (95% CI, -11.2% to 3.0%) fewer visits and 2.3% (95% CI, -18.9% to 17.8%) lower spending among urban populations, 2.5% (95% CI, -12.9% to 8.0%) lower spending for Medicaid-insured individuals, 5.3% (95% CI, -47.1% to 66.2%) lower spending for dual-eligible individuals, 3.0% (95% CI, -9.2% to 3.5%) lower spending for Medicare Advantage-insured individuals, and 1.5% (95% CI, -19.1% to 19.8%) lower spending among the most socially vulnerable populations. Conversely, point estimates suggested 3.4% (95% CI, -4.9% to 12.5%) greater visits and 3.8% (95% CI, -12.2% to 21.4%) higher spending in rural areas, 1.1% (95% CI, -12.8% to 17.3%) higher spending for commercially insured individuals, 1.0% (95% CI, -7.1% to 11.5%) higher spending for Medicare fee-for-service-insured individuals, and 4.5% (95% CI, -12.7% to 23.1%) higher spending among the least socially vulnerable groups. All 95% CIs crossed the null. Conclusions and Relevance: Nationwide telemedicine adoption was not significantly associated with changes in visits or spending, either overall or when stratified by urbanicity, payer type, or area-level social vulnerability, thus easing concerns about large utilization and spending increases from telemedicine expansion.

Indexed as

Ambulatory CareHealth ExpendituresTelemedicineAgedCohort StudiesFemaleHumansMaleMedicaidMedicareMiddle AgedUnited States

Identifiers

PMID42113515
PMCPMC13162073

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.