Evidence map›Paper›PMID 29036406›Full record

SynthesisJournal of the American Medical Informatics Association : JAMIA2018

Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review.

Julian Varghese, Maren Kleine, Sophia Isabella Gessner, Sarah Sandmann, Martin Dugas

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed, 4 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

55 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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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

5 authors.

Julian VargheseInstitute of Medical Informatics, University of Münster, Münster, Germany.
Maren KleineBioinformatics/Medical Informatics Department, Bielefeld University, Bielefeld, Germany.
Sophia Isabella GessnerInstitute of Medical Informatics, University of Münster, Münster, Germany.
Sarah SandmannInstitute of Medical Informatics, University of Münster, Münster, Germany.
Martin DugasInstitute of Medical Informatics, University of Münster, Münster, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To systematically classify the clinical impact of computerized clinical decision support systems (CDSSs) in inpatient care. Materials and Methods: Medline, Cochrane Trials, and Cochrane Reviews were searched for CDSS studies that assessed patient outcomes in inpatient settings. For each study, 2 physicians independently mapped patient outcome effects to a predefined medical effect score to assess the clinical impact of reported outcome effects. Disagreements were measured by using weighted kappa and solved by consensus. An example set of promising disease entities was generated based on medical effect scores and risk of bias assessment. To summarize technical characteristics of the systems, reported input variables and algorithm types were extracted as well. Results: Seventy studies were included. Five (7%) reported reduced mortality, 16 (23%) reduced life-threatening events, and 28 (40%) reduced non-life-threatening events, 20 (29%) had no significant impact on patient outcomes, and 1 showed a negative effect (weighted κ: 0.72, P < .001). Six of 24 disease entity settings showed high effect scores with medium or low risk of bias: blood glucose management, blood transfusion management, physiologic deterioration prevention, pressure ulcer prevention, acute kidney injury prevention, and venous thromboembolism prophylaxis. Most of the implemented algorithms (72%) were rule-based. Reported input variables are shared as standardized models on a metadata repository. Discussion and Conclusion: Most of the included CDSS studies were associated with positive patient outcomes effects but with substantial differences regarding the clinical impact. A subset of 6 disease entities could be filtered in which CDSS should be given special consideration at sites where computer-assisted decision-making is deemed to be underutilized. Registration number on PROSPERO: CRD42016049946.

Indexed as

Decision Making, Computer-AssistedDecision Support Systems, ClinicalTreatment OutcomeAlgorithmsHospital MortalityHumansInpatientsMedical Order Entry Systems

Identifiers

PMID29036406
PMCPMC7646949

What OpenQuestion holds

Textmetadata
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.