Evidence map›Paper›PMID 19715591›Full record

SynthesisBMC health services research2009

Do computerised clinical decision support systems for prescribing change practice? A systematic review of the literature (1990-2007).

Sallie-Anne Pearson, Annette Moxey, Jane Robertson, Isla Hains, Margaret Williamson, James Reeve, David Newby

Abstract readSystematic Review
In one paragraph

Synthesis in BMC health services research, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 17 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
90citing papers in PubMed, 17 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

90 citing papers in PubMed, 17 syntheses or guidelines pooled it.

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  10. Use of handheld computers in clinical practice: a systematic review.BMC medical informatics and decision making · 2014
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  14. Computerized advice on drug dosage to improve prescribing practice.The Cochrane database of systematic reviews · 2013
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  17. Computerized clinical decision support for prescribing: provision does not guarantee uptake.Journal of the American Medical Informatics Association : JAMIA
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30 more citing papers are in PubMed but not listed here.

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.

Sallie-Anne PearsonUNSW Cancer Research Centre, University of New South Wales and Prince of Wales Clinical School, Sydney, Australia. sallie.pearson@unsw.edu.au
Annette Moxey
Jane Robertson
Isla Hains
Margaret Williamson
James Reeve
David Newby

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundComputerised clinical decision support systems (CDSSs) are used widely to improve quality of care and patient outcomes. This systematic review evaluated the impact of CDSSs in targeting specific aspects of prescribing, namely initiating, monitoring and stopping therapy. We also examined the influence of clinical setting (institutional vs ambulatory care), system- or user-initiation of CDSS, multi-faceted vs stand alone CDSS interventions and clinical target on practice changes in line with the intent of the CDSS.

methodsWe searched Medline, Embase and PsychINFO for publications from 1990-2007 detailing CDSS prescribing interventions. Pairs of independent reviewers extracted the key features and prescribing outcomes of methodologically adequate studies (experiments and strong quasi-experiments).

results56 studies met our inclusion criteria, 38 addressing initiating, 23 monitoring and three stopping therapy. At the time of initiating therapy, CDSSs appear to be somewhat more effective after, rather than before, drug selection has occurred (7/12 versus 12/26 studies reporting statistically significant improvements in favour of CDSSs on = 50% of prescribing outcomes reported). CDSSs also appeared to be effective for monitoring therapy, particularly using laboratory test reminders (4/7 studies reporting significant improvements in favour of CDSSs on the majority of prescribing outcomes). None of the studies addressing stopping therapy demonstrated impacts in favour of CDSSs over comparators. The most consistently effective approaches used system-initiated advice to fine-tune existing therapy by making recommendations to improve patient safety, adjust the dose, duration or form of prescribed drugs or increase the laboratory testing rates for patients on long-term therapy. CDSSs appeared to perform better in institutional compared to ambulatory settings and when decision support was initiated automatically by the system as opposed to user initiation. CDSSs implemented with other strategies such as education were no more successful in improving prescribing than stand alone interventions. Cardiovascular disease was the most studied clinical target but few studies demonstrated significant improvements on the majority of prescribing outcomes.

conclusionOur understanding of CDSS impacts on specific aspects of the prescribing process remains relatively limited. Future implementation should build on effective approaches including the use of system-initiated advice to address safety issues and improve the monitoring of therapy.

Indexed as

Decision Support Systems, ClinicalDrug PrescriptionsPractice Patterns, Physicians'User-Computer InterfaceHumans

Identifiers

PMID19715591
PMCPMC2744674

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.