ContextDevelopers of health care software have attributed improvements in patient
care to these applications. As with any health care intervention, such claims
require confirmation in clinical trials.ObjectivesTo review controlled trials assessing the effects of computerized clinical
decision support systems (CDSSs) and to identify study characteristics predicting
benefit.Data SourcesWe updated our earlier reviews by searching the MEDLINE, EMBASE, Cochrane
Library, Inspec, and ISI databases and consulting reference lists through
September 2004. Authors of 64 primary studies confirmed data or provided additional
information.Study SelectionWe included randomized and nonrandomized controlled trials that evaluated
the effect of a CDSS compared with care provided without a CDSS on practitioner
performance or patient outcomes.Data ExtractionTeams of 2 reviewers independently abstracted data on methods, setting,
CDSS and patient characteristics, and outcomes.Data SynthesisOne hundred studies met our inclusion criteria. The number and methodologic
quality of studies improved over time. The CDSS improved practitioner performance
in 62 (64%) of the 97 studies assessing this outcome, including 4 (40%) of
10 diagnostic systems, 16 (76%) of 21 reminder systems, 23 (62%) of 37 disease
management systems, and 19 (66%) of 29 drug-dosing or prescribing systems.
Fifty-two trials assessed 1 or more patient outcomes, of which 7 trials (13%)
reported improvements. Improved practitioner performance was associated with
CDSSs that automatically prompted users compared with requiring users to activate
the system (success in 73% of trials vs 47%; P = .02)
and studies in which the authors also developed the CDSS software compared
with studies in which the authors were not the developers (74% success vs
28%; respectively, P = .001).ConclusionsMany CDSSs improve practitioner performance. To date, the effects on
patient outcomes remain understudied and, when studied, inconsistent.

Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review.

Objective To identify features of clinical decision support systems critical for improving clinical practice. Design Systematic review of randomised controlled trials. Data sources Literature searches via Medline, CINAHL, and the Cochrane Controlled Trials Register up to 2003; and searches of reference lists of included studies and relevant reviews. Study selection Studies had to evaluate the ability of decision support systems to improve clinical practice. Data extraction Studies were assessed for statistically and clinically significant improvement in clinical practice and for the presence of 15 decision support system features whose importance had been repeatedly suggested in the literature. Results Seventy studies were included. Decision support systems significantly improved clinical practice in 68% of trials. Univariate analyses revealed that, for five of the system features, interventions possessing the feature were significantly more likely to improve clinical practice than interventions lacking the feature. Multiple logistic regression analysis identified four features as independent predictors of improved clinical practice: automatic provision of decision support as part of clinician workflow (P Conclusions Several features were closely correlated with decision support systems9 ability to improve patient care significantly. Clinicians and other stakeholders should implement clinical decision support systems that incorporate these features whenever feasible and appropriate.

https://www.bmj.com/content/bmj/330/7494/765.full.pdf

Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success

Background Despite increasing emphasis on the role of clinical decision-support systems (CDSSs) for improving care and reducing costs, evidence to support widespread use is lacking. Purpose To evaluate the effect of CDSSs on clinical outcomes, health care processes, workload and efficiency, patient satisfaction, cost, and provider use and implementation. Data sources MEDLINE, CINAHL, PsycINFO, and Web of Science through January 2011. Study selection Investigators independently screened reports to identify randomized trials published in English of electronic CDSSs that were implemented in clinical settings; used by providers to aid decision making at the point of care; and reported clinical, health care process, workload, relationship-centered, economic, or provider use outcomes. Data extraction Investigators extracted data about study design, participant characteristics, interventions, outcomes, and quality. Data synthesis 148 randomized, controlled trials were included. A total of 128 (86%) assessed health care process measures, 29 (20%) assessed clinical outcomes, and 22 (15%) measured costs. Both commercially and locally developed CDSSs improved health care process measures related to performing preventive services (n= 25; odds ratio [OR], 1.42 [95% CI, 1.27 to 1.58]), ordering clinical studies (n= 20; OR, 1.72 [CI, 1.47 to 2.00]), and prescribing therapies (n= 46; OR, 1.57 [CI, 1.35 to 1.82]). Few studies measured potential unintended consequences or adverse effects. Limitations Studies were heterogeneous in interventions, populations, settings, and outcomes. Publication bias and selective reporting cannot be excluded. Conclusion Both commercially and locally developed CDSSs are effective at improving health care process measures across diverse settings, but evidence for clinical, economic, workload, and efficiency outcomes remains sparse. This review expands knowledge in the field by demonstrating the benefits of CDSSs outside of experienced academic centers. Primary funding source Agency for Healthcare Research and Quality.

Effect of Clinical Decision-Support Systems: A Systematic Review

Objectives To catalogue study designs used to assess the clinical effectiveness of CDSSs and KMSs, to identify features that impact the success of CDSSs/KMSs, to document the impact of CDSSs/KMSs on outcomes, and to identify knowledge types that can be integrated into CDSSs/KMSs Data sources MEDLINE(®), CINAHL(®), PsycINFO(®), and Web of Science(®) Review methods We included studies published in English from January 1976 through December 2010 After screening titles and abstracts, full-text versions of articles were reviewed by two independent reviewers Included articles were abstracted to evidence tables by two reviewers Meta-analyses were performed for seven domains in which sufficient studies with common outcomes were included Results We identified 15,176 articles, from which 323 articles describing 311 unique studies including 160 reports on 148 randomized control trials (RCTs) were selected for inclusion RCTs comprised 475 percent of the comparative studies on CDSSs/KMSs Both commercially and locally developed CDSSs effectively improved health care process measures related to performing preventive services (n = 25; OR 142, 95% confidence interval [CI] 127 to 158), ordering clinical studies (n = 20; OR 172, 95% CI 147 to 200), and prescribing therapies (n = 46; OR 157, 95% CI 135 to 182) Fourteen CDSS/KMS features were assessed for correlation with success of CDSSs/KMSs across all endpoints Meta-analyses identified six new success features: Integration with charting or order entry system Promotion of action rather than inaction No need for additional clinician data entry Justification of decision support via research evidence Local user involvement Provision of decision support results to patients as well as providers Three previously identified success features were confirmed: Automatic provision of decision support as part of clinician workflow Provision of decision support at time and location of decisionmaking Provision of a recommendation, not just an assessment Only 29 (196%) RCTs assessed the impact of CDSSs on clinical outcomes, 22 (149%) assessed costs, and 3 assessed KMSs on any outcomes The primary source of knowledge used in CDSSs was derived from structured care protocols Conclusions Strong evidence shows that CDSSs/KMSs are effective in improving health care process measures across diverse settings using both commercially and locally developed systems Evidence for the effectiveness of CDSSs on clinical outcomes and costs and KMSs on any outcomes is minimal Nine features of CDSSs/KMSs that correlate with a successful impact of clinical decision support have been newly identified or confirmed

Enabling health care decisionmaking through clinical decision support and knowledge management

Background
Clinical practice does not always reflect best practice and evidence, partly because of unconscious acts of omission, information overload, or inaccessible information. Reminders may help clinicians overcome these problems by prompting them to recall information that they already know or would be expected to know and by providing information or guidance in a more accessible and relevant format, at a particularly appropriate time. This is an update of a previously published review.

Objectives
To evaluate the effects of reminders automatically generated through a computerized system (computer-generated) and delivered on paper to healthcare professionals on quality of care (outcomes related to healthcare professionals' practice) and patient outcomes (outcomes related to patients' health condition).

Search methods
We searched CENTRAL, MEDLINE, Embase, six other databases and two trials registers up to 21 September 2016 together with reference checking, citation searching and contact with study authors to identify additional studies.

Selection criteria
We included individual- or cluster-randomized and non-randomized trials that evaluated the impact of computer-generated reminders delivered on paper to healthcare professionals, alone (single-component intervention) or in addition to one or more co-interventions (multi-component intervention), compared with usual care or the co-intervention(s) without the reminder component.

Data collection and analysis
Review authors working in pairs independently screened studies for eligibility and abstracted data. For each study, we extracted the primary outcome when it was defined or calculated the median effect size across all reported outcomes. We then calculated the median improvement and interquartile range (IQR) across included studies using the primary outcome or median outcome as representative outcome. We assessed the certainty of the evidence according to the GRADE approach.

Main results
We identified 35 studies (30 randomized trials and five non-randomized trials) and analyzed 34 studies (40 comparisons). Twenty-nine studies took place in the USA and six studies took place in Canada, France, Israel, and Kenya. All studies except two took place in outpatient care. Reminders were aimed at enhancing compliance with preventive guidelines (e.g. cancer screening tests, vaccination) in half the studies and at enhancing compliance with disease management guidelines for acute or chronic conditions (e.g. annual follow-ups, laboratory tests, medication adjustment, counseling) in the other half.

Computer-generated reminders delivered on paper to healthcare professionals, alone or in addition to co-intervention(s), probably improves quality of care slightly compared with usual care or the co-intervention(s) without the reminder component (median improvement 6.8% (IQR: 3.8% to 17.5%); 34 studies (40 comparisons); moderate-certainty evidence).

Computer-generated reminders delivered on paper to healthcare professionals alone (single-component intervention) probably improves quality of care compared with usual care (median improvement 11.0% (IQR 5.4% to 20.0%); 27 studies (27 comparisons); moderate-certainty evidence). Adding computer-generated reminders delivered on paper to healthcare professionals to one or more co-interventions (multi-component intervention) probably improves quality of care slightly compared with the co-intervention(s) without the reminder component (median improvement 4.0% (IQR 3.0% to 6.0%); 11 studies (13 comparisons); moderate-certainty evidence).

We are uncertain whether reminders, alone or in addition to co-intervention(s), improve patient outcomes as the certainty of the evidence is very low (n = 6 studies (seven comparisons)). None of the included studies reported outcomes related to harms or adverse effects of the intervention.

Authors' conclusions
There is moderate-certainty evidence that computer-generated reminders delivered on paper to healthcare professionals probably slightly improves quality of care, in terms of compliance with preventive guidelines and compliance with disease management guidelines. It is uncertain whether reminders improve patient outcomes because the certainty of the evidence is very low. The heterogeneity of the reminder interventions included in this review also suggests that reminders can probably improve quality of care in various settings under various conditions.

/pdf/computer-generated-reminders-delivered-on-paper-to-7wng4f9shs.pdf

Computer-generated reminders delivered on paper to healthcare professionals: effects on professional practice and healthcare outcomes

Background Identifying the appropriate long-term anti-thrombotic therapy following acute ischaemic stroke is a challenging area in which computer-based decision support may provide assistance. Aim To evaluate the influence on prescribing practice of a computer-based decision support system (CDSS) that provided patient-specific estimates of the expected ischaemic and haemorrhagic vascular event rates under each potential anti-thrombotic therapy. Design Cluster-randomized controlled trial. Methods We recruited patients who presented for a first investigation of ischaemic stroke or TIA symptoms, excluding those with a poor prognosis or major contraindication to anticoagulation. After observation of routine prescribing practice (6 months) in each hospital, centres were randomized for 6 months to either control (routine practice observed) or intervention (practice observed while the CDSS provided patient-specific information). We compared, between control and intervention centres, the risk reduction (estimated by the CDSS) in ischaemic and haemorrhagic vascular events achieved by long-term anti-thrombotic therapy, and the proportions of subjects prescribed the optimal therapy identified by the CDSS. Results Sixteen hospitals recruited 1952 subjects. When the CDSS provided information, the mean relative risk reduction attained by prescribing increased by 2.7 percentage units (95%CI -0.3 to 5.7) and the odds ratio for the optimal therapy being prescribed was 1.32 (0.83 to 1.80). Some 55% (5/9) of clinicians believed the CDSS had influenced their prescribing. Conclusions Cluster-randomized trials provide excellent frameworks for evaluating novel clinical management methods. Our CDSS was feasible to implement and acceptable to clinicians, but did not substantially influence prescribing practice for anti-thrombotic drugs after acute ischaemic stroke.

/pdf/cluster-randomized-controlled-trial-of-computer-based-1uahrcjaio.pdf

Cluster-randomized, controlled trial of computer-based decision support for selecting long-term anti-thrombotic therapy after acute ischaemic stroke.

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Cluster-randomized, controlled trial of computer-based decision support for selecting long-term anti-thrombotic therapy after acute ischaemic stroke.