Evidence map›Paper›PMID 30342518›Full record

ArticleBMC health services research2018

Harvesting the wisdom of the crowd: using online ratings to explore care experiences in regions.

Roy J P Hendrikx, Marieke D Spreeuwenberg, Hanneke W Drewes, Jeroen N Struijs, Dirk Ruwaard, Caroline A Baan

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC health services research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 15% 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

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. 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

6 authors at 4 institutions in 4 countries.

Roy J P HendrikxTranzo Scientific Center for Care and Welfare, Research Centre for Technology in Care, Tilburg University, PO Box 90153, 5000, LE, Tilburg, The Netherlands. roy.hendrikx@rivm.nl.ORCID http://orcid.org/0000-0003-3088-6407
Marieke D SpreeuwenbergZuyd University of Applied Sciences, PO Box 550, 6400, AN, Heerlen, The Netherlands.
Hanneke W DrewesDepartment for Quality of Care and Health Economics, Center for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment, PO Box 1, 3720, BA, Bilthoven, The Netherlands.
Jeroen N StruijsDepartment for Quality of Care and Health Economics, Center for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment, PO Box 1, 3720, BA, Bilthoven, The Netherlands.
Dirk RuwaardDepartment of Health Services Research, Care and Public Health Research Institute (CAPHRI) , Faculty of Health, Medicine and Life Sciences, Maastricht University, PO Box 616, 6200, MD, Maastricht, The Netherlands.
Caroline A BaanTranzo Scientific Center for Care and Welfare, Research Centre for Technology in Care, Tilburg University, PO Box 90153, 5000, LE, Tilburg, The Netherlands.
National Institute for Public Health and the Environment · NLTilburg University · NLMaastricht University · NLZuyd University of Applied Sciences · NL

Funding

Rijksinstituut voor Volksgezondheid en Milieu (NL) S/133002
6 · The paper itself

Abstract

backgroundRegional population health management (PHM) initiatives need an understanding of regional patient experiences to improve their services. Websites that gather patient ratings have become common and could be a helpful tool in this effort. Therefore, this study explores whether unsolicited online ratings can provide insight into (differences in) patient's experiences at a (regional) population level.

methodsUnsolicited online ratings from the Dutch website Zorgkaart Nederland (year = 2008-2017) were used. Patients rated their care providers on six dimensions from 1 to 10 and these ratings were geographically aggregated based on nine PHM regions. Distributions were explored between regions. Multilevel analyses per provider category, which produced Intraclass Correlation Coefficients (ICC), were performed to determine clustering of ratings of providers located within regions. If ratings were clustered, then this would indicate that differences found between regions could be attributed to regional characteristics (e.g. demographics or regional policy).

resultsIn the nine regions, 70,889 ratings covering 4100 care providers were available. Overall, average regional scores (range = 8.3-8.6) showed significant albeit small differences. Multilevel analyses indicated little clustering between unsolicited provider ratings within regions, as the regional level ICCs were low (ICC pioneer site < 0.01). At the provider level, all ICCs were above 0.11, which showed that ratings were clustered.

conclusionsUnsolicited online provider-based ratings are able to discern (small) differences between regions, similar to solicited data. However, these differences could not be attributed to the regional level, making unsolicited ratings not useful for overall regional policy evaluations. At the provider level, ratings can be used by regions to identify under-performing providers within their regions.

Indexed as

InternetData CollectionDelivery of Health CareFemaleHumansMaleMiddle AgedNetherlandsPatient SatisfactionQuality of Health CareOnline ratingsPopulation health managementQuality of careRegional evaluationUnsolicited data

Identifiers

PMID30342518
PMCPMC6195971
OpenAlexW2897881284

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.