Evidence map›Paper›PMID 29367187›Full record

ReviewJMIR medical informatics2018

Quality of Decision Support in Computerized Provider Order Entry: Systematic Literature Review.

Delphine Carli, Guillaume Fahrni, Pascal Bonnabry, Christian Lovis

Abstract readReview
In one paragraph

Review in JMIR medical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

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

  1. Alert fatigue measurement in clinical decision support: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2026
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  14. Creating a High-Specificity Acute Kidney Injury Detection System for Clinical and Research Applications.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2021
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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

4 authors.

Delphine CarliDivision of Pharmacy, University Hospitals of Geneva, Geneva, Switzerland.ORCID http://orcid.org/0000-0003-0876-8906
Guillaume FahrniDivision of Medical Information Sciences, University Hospitals of Geneva, Geneva, Switzerland.ORCID http://orcid.org/0000-0003-1583-9602
Pascal BonnabryDivision of Pharmacy, University Hospitals of Geneva, Geneva, Switzerland.ORCID http://orcid.org/0000-0002-8690-649X
Christian LovisDivision of Medical Information Sciences, University Hospitals of Geneva, Geneva, Switzerland.ORCID http://orcid.org/0000-0002-2681-8076

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundComputerized decision support systems have raised a lot of hopes and expectations in the field of order entry. Although there are numerous studies reporting positive impacts, concerns are increasingly high about alert fatigue and effective impacts of these systems. One of the root causes of fatigue alert reported is the low clinical relevance of these alerts.

objectiveThe objective of this systematic review was to assess the reported positive predictive value (PPV), as a proxy to clinical relevance, of decision support systems in computerized provider order entry (CPOE).

methodsA systematic search of the scientific literature published between February 2009 and March 2015 on CPOE, clinical decision support systems, and the predictive value associated with alert fatigue was conducted using PubMed database. Inclusion criteria were as follows: English language, full text available (free or pay for access), assessed medication, direct or indirect level of predictive value, sensitivity, or specificity. When possible with the information provided, PPV was calculated or evaluated.

resultsAdditive queries on PubMed retrieved 928 candidate papers. Of these, 376 were eligible based on abstract. Finally, 26 studies qualified for a full-text review, and 17 provided enough information for the study objectives. An additional 4 papers were added from the references of the reviewed papers. The results demonstrate massive variations in PPVs ranging from 8% to 83% according to the object of the decision support, with most results between 20% and 40%. The best results were observed when patients' characteristics, such as comorbidity or laboratory test results, were taken into account. There was also an important variation in sensitivity, ranging from 38% to 91%.

conclusionsThere is increasing reporting of alerts override in CPOE decision support. Several causes are discussed in the literature, the most important one being the clinical relevance of alerts. In this paper, we tried to assess formally the clinical relevance of alerts, using a near-strong proxy, which is the PPV of alerts, or any way to express it such as the rate of true and false positive alerts. In doing this literature review, three inferences were drawn. First, very few papers report direct or enough indirect elements that support the use or the computation of PPV, which is a gold standard for all diagnostic tools in medicine and should be systematically reported for decision support. Second, the PPV varies a lot according to the typology of decision support, so that overall rates are not useful, but must be reported by the type of alert. Finally, in general, the PPVs are below or near 50%, which can be considered as very low.

Indexed as

decision support systems, clinicalmedical order entry systemspredictive value of testssensitivityspecificitysystem, medication alert

Identifiers

PMID29367187
PMCPMC5803531

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.