Evidence map›Paper›PMID 42712323›Full record

ReviewBMJ digital health & AI2025

Clinical decision support systems' effect in reducing medication errors and adverse drug events: an expanded review from Making Healthcare Safer IV.

Ania Syrowatka, Aneesa Motala, Emily Lawson, Paul G Shekelle

Abstract readReview
In one paragraph

Review in BMJ digital health & AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ania SyrowatkaBrigham and Women's Hospital, Boston, Massachusetts, USA.
Aneesa MotalaUniversity of Southern California, Los Angeles, California, USA.ORCID 0000-0003-0840-8580
Emily LawsonRAND, Pittsburgh, Pennsylvania, USA.
Paul G ShekelleRAND, Santa Monica, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clinical decision support systems (CDSSs) are used to provide alerts and recommendations on prescribing and order verification with the intent of reducing the frequency and harm of medication errors (MEs) and adverse drug events (ADEs). This review assessed recent evidence on the effects of CDSSs on reducing MEs and ADEs. Related implementation outcomes such as alert overrides and unintended consequences of use were also assessed, and literature around the effective implementation of CDSSs was summarised. The review followed processes proposed by the Agency for Healthcare Research and Quality Evidence-based Practice Center Programme. PubMed and the Cochrane Library were searched from 2015 to 2023. Results were narratively synthesised, and overall certainty of evidence was assessed for outcomes of interest. The search yielded 1335 unique abstracts, of which 34 articles met the criteria for inclusion. The type of clinical decision support and outcomes were heterogeneous between studies. Overall, computerised provider order entry with medication-related clinical decision support functions was associated with reduced MEs (

Indexed as

Decision Support Systems, Clinical

Identifiers

PMID42712323
PMCPMC13492544

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.