Evidence map›Paper›PMID 41587028›Full record

ArticleJAMA network open2026

Trends in Urgent Care Utilization Among Medicare Beneficiaries From 2012 to 2019.

Joel J Mantilla, Ryan C Burke, E John Orav, Barbara A Masser, Richard E Wolfe, Amber K Sabbatini, Michelle P Lin, Ari B Friedman, Laura G Burke

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. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

9 authors.

Joel J MantillaHerbert Wertheim College of Medicine, Florida International University, Miami.
Ryan C BurkeDepartment of Emergency Medicine, Brown University Health Medical Group, Providence, Rhode Island.
E John OravDepartment of Medicine, Division of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
Barbara A MasserDepartment of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
Richard E WolfeDepartment of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
Amber K SabbatiniDepartment of Emergency Medicine, University of Washington, Seattle.
Michelle P LinDepartment of Emergency Medicine, Stanford University, Palo Alto, California.
Ari B FriedmanDepartment of Emergency Medicine, University of Pennsylvania, Philadelphia.
Laura G BurkeDepartment of Emergency Medicine, Brown University Health Medical Group, Providence, Rhode Island.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Urgent care (UC) centers have proliferated rapidly, yet research on how utilization has changed among older adults is limited. Objectives: To examine UC utilization among older adults and assess whether utilization rates varied by beneficiary sociodemographic and community characteristics. Design, Setting, and Participants: This cross-sectional study used data from a 20% national sample of fee-for-service Medicare beneficiaries aged 65 years or older using UC centers from January 1, 2012, to December 31, 2019. Statistical analysis was performed from May 1, 2021, to November 24, 2025. Main Outcome and Measures: Among Medicare beneficiaries aged 65 years or older, unadjusted UC visits were calculated by year from 2012 to 2019 overall and stratified by demographic characteristics, frailty, community rurality, Social Deprivation Index (SDI), and physician supply. Adjusted incidence rate ratios (IRRs) were calculated for UC visits in 2018 and 2019 using negative binomial models. Trends in the distribution of UC visits among the most frequent clinician specialty categories (primary care, emergency medicine, and advanced practice practitioners [APPs]) were examined using linear models. Results: There were 3 516 816 UC visits among 9 514 946 beneficiaries (mean [SD] age across visits, 75.2 [7.5] years; 63.4% women). UC visits increased from 47.7 to 117.2 per 1000 from 2012 to 2019 (9.0 [95% CI, 9.0-9.1] visits per 1000 per year). The growth in UC utilization was slowest for beneficiaries aged 85 years or older (4.0 [95% CI, 3.8-4.1] visits per 1000 per year), Medicaid-eligible beneficiaries (4.0 [95% CI, 3.9-4.2] visits per 1000 per year), those residing in communities that were rural (5.0 [95% CI, 4.8-5.2] visits per 1000 per year), thow who were disadvantaged (6.8 [95% CI, 6.0-7.6] visits per 1000 per year), and those with fewer physicians (7.2 [95% CI, 5.5-8.8] visits per 1000 per year). In 2018 and 2019, beneficiaries residing in rural communities had 45% lower adjusted UC utilization compared with urban communities (IRR, 0.55 [95% CI, 0.54-0.55]) and those residing in zip codes in the SDI fourth quartile had 23% lower adjusted UC utilization (IRR, 0.77 [95% CI, 0.77-0.78]) compared with those in the most advantaged quartile. The percentage of beneficiaries managed by APPs increased from 21.0% in 2012 to 50.8% in 2019. Conclusions and Relevance: In this cross-sectional analysis, UC utilization increased markedly among older adults, with a disproportionate concentration in urban, less-disadvantaged communities. The distribution of clinician training and specialty also changed, with APPs delivering care for more than half of UC visits among older adults in 2019. These findings highlight the evolving patterns of acute care delivery for this growing population and the need for additional evidence on how these trends are associated with patient-centered outcomes and the efficiency of care.

Indexed as

Ambulatory CareMedicarePatient Acceptance of Health CareAgedAged, 80 and overCross-Sectional StudiesEmergency Room VisitsFemaleHumansMaleUnited States

Identifiers

PMID41587028
PMCPMC12836136

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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.