Evidence map›Paper›PMID 33904912›Full record

ArticleJAMA network open2021

Assessment of Overuse of Medical Tests and Treatments at US Hospitals Using Medicare Claims.

Kelsey Chalmers, Paula Smith, Judith Garber, Valerie Gopinath, Shannon Brownlee, Aaron L Schwartz, Adam G Elshaug, Vikas Saini

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in JAMA network open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
10.2field-weighted citation impact, top 2% of its field
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

26 citing papers in PubMed, 42 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 2 countries.

Kelsey ChalmersLown Institute, Brookline, Massachusetts.
Paula SmithLown Institute, Brookline, Massachusetts.
Judith GarberLown Institute, Brookline, Massachusetts.
Valerie GopinathLown Institute, Brookline, Massachusetts.
Shannon BrownleeLown Institute, Brookline, Massachusetts.
Aaron L SchwartzDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, The University of Pennsylvania, Philadelphia.
Adam G ElshaugCentre for Health Policy, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
Vikas SainiLown Institute, Brookline, Massachusetts.
Lown Institute · USBrookings Institution · USUniversity of Pennsylvania · USUniversity of Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Overuse of health care services exposes patients to unnecessary risk of harm and costs. Distinguishing patterns of overuse among hospitals requires hospital-level measures across multiple services. Objective: To describe characteristics of hospitals associated with overuse of health care services in the US. Design, Setting, and Participants: This retrospective cross-sectional analysis used Medicare fee-for-service claims data for beneficiaries older than 65 years from January 1, 2015, to December 31, 2017, with a lookback of 1 year. Inpatient and outpatient services were included, and services offered at specialty and federal hospitals were excluded. Patients were from hospitals with the capacity (based on a claims filter developed for this study) to perform at least 7 of 12 investigated services. Statistical analyses were performed from July 1, 2020, to December 20, 2020. Main Outcomes and Measures: Outcomes of interest were a composite overuse score ranging from 0 (no overuse of services) to 1 (relatively high overuse of services) and characteristics of hospitals clustered by overuse rates. Twelve published low-value service algorithms were applied to the data to find overuse rates for each hospital, normalized and aggregated to a composite score and then compared across 6 hospital characteristics using multivariable regression. A k-means cluster analysis was used on normalized overuse rates to identify hospital clusters. Results: The primary analysis was performed on 2415 cohort A hospitals (ie, hospitals with capacity for 7 or more services), which included 1 263 592 patients (mean [SD] age, 72.4 [14] years; 678 549 women [53.7%]; 101 017 191 White patients [80.5%]). Head imaging for syncope was the highest-volume low-value service (377 745 patients [29.9%]), followed by coronary artery stenting for stable coronary disease (199 579 [15.8%]). The mean (SD) composite overuse score was 0.40 (0.10) points. Southern hospitals had a higher mean score than midwestern (difference in means: 0.06 [95% CI, 0.05-0.07] points; P < .001), northeast (0.08 [95% CI, 0.06-0.09] points; P < .001), and western hospitals (0.08 [95% CI, 0.07-0.10] points; P < .001). Nonprofit hospitals had a lower adjusted mean score than for-profit hospitals (-0.03 [95% CI, -0.04 to -0.02] points; P < .001). Major teaching hospitals had significantly lower adjusted mean overuse scores vs minor teaching hospitals (difference in means, -0.07 [95% CI, -0.08 to -0.06] points; P < .001) and nonteaching hospitals (-0.10 [95% CI, -0.12 to -0.09] points; P < .001). Of the 4 clusters identified, 1 was characterized by its low counts of overuse in all services except for spinal fusion; the majority of major teaching hospitals were in this cluster (164 of 223 major teaching hospitals [73.5%]). Conclusions and Relevance: This cross-sectional study used a novel measurement of hospital-associated overuse; results showed that the highest scores in this Medicare population were associated with nonteaching and for-profit hospitals, particularly in the South.

Indexed as

AgedAged, 80 and overCross-Sectional StudiesFee-for-Service PlansFemaleHospital Bed CapacityHospitalsHospitals, ProprietaryHospitals, RuralHospitals, TeachingHospitals, UrbanHospitals, VoluntaryHumansMaleMedical OveruseMedicare

Identifiers

PMID33904912
PMCPMC8080218
OpenAlexW3159223050

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.