Evidence map›Paper›PMID 30990522›Full record

ReviewJournal of the American Medical Informatics Association : JAMIA2019

A review of measurement practice in studies of clinical decision support systems 1998-2017.

Philip J Scott, Angela W Brown, Taiwo Adedeji, Jeremy C Wyatt, Andrew Georgiou, Eric L Eisenstein, Charles P Friedman

Abstract readReview
In one paragraph

Review in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–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

7 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Replication studies in the clinical decision support literature-frequency, fidelity, and impact.Journal of the American Medical Informatics Association : JAMIA · 2021
    Article
  5. Article
  6. Observational
  7. 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

7 authors.

Philip J ScottCentre for Healthcare Modelling and Informatics, University of Portsmouth, Portsmouth, UK.
Angela W BrownCentre for Healthcare Modelling and Informatics, University of Portsmouth, Portsmouth, UK.
Taiwo AdedejiCentre for Healthcare Modelling and Informatics, University of Portsmouth, Portsmouth, UK.
Jeremy C WyattWessex Institute of Health Research, University of Southampton, Southampton, UK.
Andrew GeorgiouAustralian Institute of Health Innovation, Macquarie University, Sydney, Australia.
Eric L EisensteinDuke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina, USA.
Charles P FriedmanSchools of Medicine, Information and Public Health, University of Michigan, Ann Arbor, Michigan, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo assess measurement practice in clinical decision support evaluation studies. MATERIALS AND

methodsWe identified empirical studies evaluating clinical decision support systems published from 1998 to 2017. We reviewed titles, abstracts, and full paper contents for evidence of attention to measurement validity, reliability, or reuse. We used Friedman and Wyatt's typology to categorize the studies.

resultsThere were 391 studies that met the inclusion criteria. Study types in this cohort were primarily field user effect studies (n = 210) or problem impact studies (n = 150). Of those, 280 studies (72%) had no evidence of attention to measurement methodology, and 111 (28%) had some evidence with 33 (8%) offering validity evidence; 45 (12%) offering reliability evidence; and 61 (16%) reporting measurement artefact reuse. DISCUSSION: Only 5 studies offered validity assessment within the study. Valid measures were predominantly observed in problem impact studies with the majority of measures being clinical or patient reported outcomes with validity measured elsewhere.

conclusionMeasurement methodology is frequently ignored in empirical studies of clinical decision support systems and particularly so in field user effect studies. Authors may in fact be attending to measurement considerations and not reporting this or employing methods of unknown validity and reliability in their studies. In the latter case, reported study results may be biased and effect sizes misleading. We argue that replication studies to strengthen the evidence base require greater attention to measurement practice in health informatics research.

Indexed as

Decision Support Systems, ClinicalEvaluation Studies as TopicMedical InformaticsReproducibility of ResultsResearch Designclinical decision support systemshealth informaticsmeasurementreliabilityvalidity

Identifiers

PMID30990522
PMCPMC6748820

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.