Evidence map›Paper›PMID 28011592›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2017

Personal health records in the Netherlands: potential user preferences quantified by a discrete choice experiment.

Domino Determann, Mattijs S Lambooij, Dorte Gyrd-Hansen, Esther W de Bekker-Grob, Ewout W Steyerberg, Marcel Heldoorn, Line Bjørnskov Pedersen, G Ardine de Wit

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

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

  1. Pooled it
  2. Pooled it
  3. Attitudes of dermatological patients towards digital health interventions: a cross-sectional survey and cluster analysis.Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG · 2026
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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

8 authors.

Domino DetermannCentre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands.
Mattijs S LambooijCentre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands.
Dorte Gyrd-HansenCOHERE - Centre of Health Economics Research, Department of Business and Economics, University of Southern Denmark, Odense, Denmark.
Esther W de Bekker-GrobDepartment of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.
Ewout W SteyerbergDepartment of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.
Marcel HeldoornDutch Federation of Patients and Consumer Organizations (NPCF), Utrecht, the Netherlands.
Line Bjørnskov PedersenCOHERE - Centre of Health Economics Research, Department of Business and Economics, University of Southern Denmark, Odense, Denmark.
G Ardine de WitCentre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo identify groups of potential users based on their preferences for characteristics of personal health records (PHRs) and to estimate potential PHR uptake.

methodsWe performed a discrete choice experiment, which consisted of 12 choice scenarios, each comprising 2 hypothetical PHR alternatives and an opt-out. The alternatives differed based on 5 characteristics. The survey was administered to Internet panel members of the Dutch Federation of Patients and Consumer Organizations. We used latent class models to analyze the data.

resultsA total of 1,443 potential PHR users completed the discrete choice experiment. We identified 3 latent classes: "refusers" (class probability 43%), "eager adopters" (37%), and "reluctant adopters" (20%). The predicted uptake for the reluctant adopters ranged from 4% in the case of a PHR with the worst attribute levels to 68% in the best case. Those with 1 or more chronic diseases were significantly more likely to belong to the eager adopter class. The data storage provider was the most decisive aspect for the eager and reluctant adopters, while cost was most decisive for the refusers. Across all classes, health care providers and independent organizations were the most preferred data storage providers.

conclusionWe identified 3 groups, of which 1 group (more than one-third of potential PHR users) indicated great interest in a PHR irrespective of PHR characteristics. Policymakers who aim to expand the use of PHRs will be most successful when health care providers and health facilities or independent organizations store PHR data while refraining from including market parties.

Indexed as

Electronic Health RecordsHealth Records, PersonalPatient PreferenceAdolescentAgedAged, 80 and overAttitude to ComputersFemaleHumansMaleMiddle AgedNetherlandsSurveys and QuestionnairesYoung Adultdiscrete choice experimentpersonal health recordspreferences

Identifiers

PMID28011592
PMCPMC7651889

What OpenQuestion holds

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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.