Evidence map›Paper›PMID 38102524›Full record

ArticleThe European journal of health economics : HEPAC : health economics in prevention and care2024

The elicitation of patient and physician preferences for calculating consumer-based composite measures on hospital report cards: results of two discrete choice experiments.

Martin Emmert, Stefan Rohrbacher, Florian Meier, Laura Heppe, Cordula Drach, Anja Schindler, Uwe Sander, Christiane Patzelt, Cornelia Frömke, Oliver Schöffski and 1 more

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Article in The European journal of health economics : HEPAC : health economics in prevention and care, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 2 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

3 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. 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

11 authors.

Martin EmmertFaculty of Law, Business and Economics, Institute for Healthcare Management and Health Sciences, University of Bayreuth, Prieserstraße 2, 95444, Bayreuth, Germany. Martin.Emmert@uni-bayreuth.de.ORCID http://orcid.org/0000-0002-0154-6641
Stefan RohrbacherFaculty of Law, Business and Economics, Institute for Healthcare Management and Health Sciences, University of Bayreuth, Prieserstraße 2, 95444, Bayreuth, Germany.
Florian MeierDepartment of Management and Economics, SRH Wilhelm Löhe University of Applied Sciences, 90763, Fürth, Germany.
Laura HeppeSchool of Business and Economics, Chair of Health Care Management, Friedrich-Alexander-University of Erlangen-Nuremberg, Lange Gasse 20, 90403, Nuremberg, Germany.
Cordula DrachSchool of Business and Economics, Chair of Health Care Management, Friedrich-Alexander-University of Erlangen-Nuremberg, Lange Gasse 20, 90403, Nuremberg, Germany.
Anja SchindlerDepartment of Information and Communication, Faculty for Media, Information and Design, University of Applied Sciences and Arts, Hannover, Germany.
Uwe SanderDepartment of Information and Communication, Faculty for Media, Information and Design, University of Applied Sciences and Arts, Hannover, Germany.
Christiane PatzeltDepartment of Information and Communication, Faculty for Media, Information and Design, University of Applied Sciences and Arts, Hannover, Germany.
Cornelia FrömkeDepartment of Information and Communication, Faculty for Media, Information and Design, University of Applied Sciences and Arts, Hannover, Germany.
Oliver SchöffskiSchool of Business and Economics, Chair of Health Care Management, Friedrich-Alexander-University of Erlangen-Nuremberg, Lange Gasse 20, 90403, Nuremberg, Germany.
Michael LauererFaculty of Law, Business and Economics, Institute for Healthcare Management and Health Sciences, University of Bayreuth, Prieserstraße 2, 95444, Bayreuth, Germany.

Funding

The German health care Innovation Fund 01VSF18034
6 · The paper itself

Abstract

purposeThe calculation of aggregated composite measures is a widely used strategy to reduce the amount of data on hospital report cards. Therefore, this study aims to elicit and compare preferences of both patients as well as referring physicians regarding publicly available hospital quality information

methodsBased on systematic literature reviews as well as qualitative analysis, two discrete choice experiments (DCEs) were applied to elicit patients' and referring physicians' preferences. The DCEs were conducted using a fractional factorial design. Statistical data analysis was performed using multinomial logit models

resultsApart from five identical attributes, one specific attribute was identified for each study group, respectively. Overall, 322 patients (mean age 68.99) and 187 referring physicians (mean age 53.60) were included. Our models displayed significant coefficients for all attributes (p < 0.001 each). Among patients, "Postoperative complication rate" (20.6%; level range of 1.164) was rated highest, followed by "Mobility at hospital discharge" (19.9%; level range of 1.127), and ''The number of cases treated" (18.5%; level range of 1.045). In contrast, referring physicians valued most the ''One-year revision surgery rate'' (30.4%; level range of 1.989), followed by "The number of cases treated" (21.0%; level range of 1.372), and "Postoperative complication rate" (17.2%; level range of 1.123)

conclusionWe determined considerable differences between both study groups when calculating the relative value of publicly available hospital quality information. This may have an impact when calculating aggregated composite measures based on consumer-based weighting.

Indexed as

Choice BehaviorPatient PreferencePhysiciansAdultAgedFemaleHospitalsHumansMaleMiddle AgedQuality Indicators, Health CareComposite measuresDiscrete choice experimentHospital choiceHospital report cardsPublic reporting

Identifiers

PMID38102524
PMCPMC11283427

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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.