Evidence map›Paper›PMID 31244481›Full record

ArticleJournal of medical Internet research2019

When Similarity Beats Expertise-Differential Effects of Patient and Expert Ratings on Physician Choice: Field and Experimental Study.

Anne-Madeleine Kranzbühler, Mirella H P Kleijnen, Peeter W J Verlegh, Marije Teerling

Abstract read
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Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Anne-Madeleine KranzbühlerDepartment of Product Innovation Management, Delft University of Technology, Delft, Netherlands.ORCID 0000-0002-1558-3494
Mirella H P KleijnenDepartment of Marketing, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.ORCID 0000-0002-7946-6959
Peeter W J VerleghDepartment of Marketing, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.ORCID 0000-0002-0249-5583
Marije TeerlingEneco, Rotterdam, Netherlands.ORCID 0000-0001-5528-0327

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIncreasing numbers of patients consult Web-based rating platforms before making health care decisions. These platforms often provide ratings from other patients, reflecting their subjective experience. However, patients often lack the knowledge to be able to judge the objective quality of health services. To account for this potential bias, many rating platforms complement patient ratings with more objective expert ratings, which can lead to conflicting signals as these different types of evaluations are not always aligned.

objectiveThis study aimed to fill the gap on how consumers combine information from 2 different sources-patients or experts-to form opinions and make purchase decisions in a health care context. More specifically, we assessed prospective patients' decision making when considering both types of ratings simultaneously on a Web-based rating platform. In addition, we examined how the influence of patient and expert ratings is conditional upon rating volume (ie, the number of patient opinions).

methodsIn a field study, we analyzed a dataset from a Web-based physician rating platform containing clickstream data for more than 5000 US doctors. We complemented this with an experimental lab study consisting of a sample of 112 students from a Dutch university. The average age was 23.1 years, and 60.7% (68/112) of the respondents were female.

resultsThe field data illustrated the moderating effect of rating volume. If the patient advice was based on small numbers, prospective patients tended to base their selection of a physician on expert rather than patient advice (profile clicks beta=.14, P<.001; call clicks beta=.28, P=.03). However, when the group of patients substantially grew in size, prospective patients started to rely on patients rather than the expert (profile clicks beta=.23, SE=0.07, P=.004; call clicks beta=.43, SE=0.32, P=.10). The experimental study replicated and validated these findings for conflicting patient versus expert advice in a controlled setting. When patient ratings were aggregated from a high number of opinions, prospective patients' evaluations were affected more strongly by patient than expert advice (mean

conclusionsWhen confronted with information from both sources simultaneously, prospective patients are influenced more strongly by other patients. This effect reverses when the patient rating has been aggregated from a (very) small number of individual opinions. This has important implications for how to present health care provider ratings to prospective patients to aid their decision-making process.

Indexed as

Decision MakingFemaleHumansMalePhysiciansProspective Studieschoice behaviordecision makingjudgment

Identifiers

PMID31244481
PMCPMC6617917

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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.