Evidence map›Paper›PMID 36054345›Full record

ArticleHealth services research2022

Assessing patient, physician, and practice characteristics predicting the use of low-value services.

Eunhae Shin, Chris Fleming, Arkadipta Ghosh, Arvin Javadi, Rhea Powell, Eugene Rich

Abstract read
In one paragraph

Article in Health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Observational
  3. Article
  4. Article
  5. Article
  6. Review
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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

6 authors.

Eunhae ShinMathematica, Oakland, California, USA.ORCID 0000-0002-0581-3997
Chris FlemingMathematica, Washington, DC, USA.
Arkadipta GhoshMathematica, Princeton, New Jersey, USA.
Arvin JavadiMathematica, Princeton, New Jersey, USA.
Rhea PowellMathematica, Princeton, New Jersey, USA.
Eugene RichMathematica, Washington, DC, USA.ORCID 0000-0003-4973-8607

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo examine characteristics of beneficiaries, physicians, and their practice sites associated with greater use of low-value services (LVS) using LVS measures that reflect current care practices. DATA SOURCES: This study was conducted in the context of a large, nationwide primary care redesign initiative (Comprehensive Primary Care Plus), using Medicare claims data in 2018. STUDY

designWe examined beneficiary-level total counts of LVS based on the existing 31 claims-based measures updated by excluding three services provided with diminishing frequency to Medicare beneficiaries and by replacing these with more recently identified LVS. We estimated hierarchical linear models with an extensive list of beneficiary, physician, and practice site characteristics to examine the contribution of characteristics at each level in predicting greater use of LVS. We also examined the proportion of variation in LVS use attributable to the set of characteristics at each level. DATA COLLECTION/EXTRACTION

methodsThe study included 5,074,642 Medicare fee-for-service beneficiaries attributed to 32,406 primary care physicians in 11,009 primary care practice sites. PRINCIPAL

findingsPatients with disabilities, end-stage renal disease, and those in regions with higher poverty rates receive 10 (standard error [SE] = 3.0), 80 (SE = 14.0), and 10 (SE = 1.0) more LVS per 1000 beneficiaries across all 31 measures combined than patients without such attributes, respectively. Greater physician comprehensiveness and an increase in the number of primary care practitioners at a practice were associated with 40 (SE = 20.0) and 20 (SE = 6.0) fewer LVS per 1000 beneficiaries, respectively. Yet, the explanatory variables we examined only account for 11 percent of the variation in LVS use, with most of the variation (87 percent) being due to unobserved differences at the beneficiary level.

conclusionsUnexplained residual variation, from underlying patient preferences and behavior of non-primary care providers, could be important determinants of LVS use.

Indexed as

MedicarePhysiciansAgedComprehensive Health CareFee-for-Service PlansHumansUnited Stateslow-value servicesmeasuresmedicareprimary carequality of care

Identifiers

PMID36054345
PMCPMC9643094

What OpenQuestion holds

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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.