Evidence map›Paper›PMID 32541458›Full record

Observational studyMedicine2020

Developing a template matching algorithm for benchmarking hospital performance in a diverse, integrated healthcare system.

Daniel Molling, Brenda M Vincent, Wyndy L Wiitala, Gabriel J Escobar, Timothy P Hofer, Vincent X Liu, Amy K Rosen, Andrew M Ryan, Sarah Seelye, Hallie C Prescott

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.2field-weighted citation impact, top 11% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. 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

10 authors at 4 institutions in 1 country.

Daniel MollingVA Center for Clinical Management Research, Ann Arbor, MI.
Brenda M VincentVA Center for Clinical Management Research, Ann Arbor, MI.
Wyndy L WiitalaVA Center for Clinical Management Research, Ann Arbor, MI.
Gabriel J EscobarDivision of Research, Kaiser Permanente Northern California, Oakland, CA.
Timothy P HoferVA Center for Clinical Management Research, Ann Arbor, MI.
Vincent X LiuDivision of Research, Kaiser Permanente Northern California, Oakland, CA.
Amy K RosenVA Center for Healthcare Organization and Implementation Research, VA Boston Healthcare System, Boston, MA.
Andrew M RyanDepartment of Health Management and Policy, School of Public Health, University of Michigan, Ann Arbor, MI.
Sarah SeelyeVA Center for Clinical Management Research, Ann Arbor, MI.
Hallie C PrescottVA Center for Clinical Management Research, Ann Arbor, MI.
VA Center for Clinical Management Research · USKaiser Permanente · USUniversity of Michigan–Ann Arbor · USVA Boston Healthcare System · US

Funding

Benchmarking Hospital Quality: Template Matching versus Conventional Regression ApproachesI01HX002546 · VA · VETERANS HEALTH ADMINISTRATION · PI PRESCOTT, HALLIE CHRISTINE · 2018 to 2021
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6 · The paper itself

Abstract

Template matching is a proposed approach for hospital benchmarking, which measures performance based on matching a subset of comparable patient hospitalizations from each hospital. We assessed the ability to create the required matched samples and thus the feasibility of template matching to benchmark hospital performance in a diverse healthcare system.Nationwide Veterans Affairs (VA) hospitals, 2017.Observational cohort study.We used administrative and clinical data from 668,592 hospitalizations at 134 VA hospitals in 2017. A standardized template of 300 hospitalizations was selected, and then 300 hospitalizations were matched to the template from each hospital.There was substantial case-mix variation across VA hospitals, which persisted after excluding small hospitals, hospitals with primarily psychiatric admissions, and hospitalizations for rare diagnoses. Median age ranged from 57 to 75 years across hospitals; percent surgical admissions ranged from 0.0% to 21.0%; percent of admissions through the emergency department, 0.1% to 98.7%; and percent Hispanic patients, 0.2% to 93.3%. Characteristics for which there was substantial variation across hospitals could not be balanced with any matching algorithm tested. Although most other variables could be balanced, we were unable to identify a matching algorithm that balanced more than ∼20 variables simultaneously.We were unable to identify a template matching approach that could balance hospitals on all measured characteristics potentially important to benchmarking. Given the magnitude of case-mix variation across VA hospitals, a single template is likely not feasible for general hospital benchmarking.

Indexed as

AgedAlgorithmsBenchmarkingCohort StudiesDelivery of Health Care, IntegratedDiagnosis-Related GroupsEmergency Service, HospitalFeasibility StudiesFemaleHispanic or LatinoHospitalizationHospitals, VeteransHumansMaleMiddle AgedMortality

Identifiers

PMID32541458
PMCPMC7302661
OpenAlexW3035450689

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.