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a systematic review of trials to identify features critical to success
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.
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
Recent research has shown that health care delivered in industrialised nations often falls short of optimal, evidence based care. A nationwide audit assessing 439 quality indicators found that US adults receive only about half of recommended care,1 and the US Institute of Medicine has estimated that up to 98 000 US residents die each year as the result of preventable medical errors.2 Similarly a retrospective analysis at two London hospitals found that 11% of admitted patients experienced adverse events, of which 48% were judged to be preventable and of which 8% led to death.3
To address these deficiencies in care, healthcare organisations are increasingly turning to clinical decision support systems, which provide clinicians with patient specific assessments or recommendations to aid clinical decision making.4 Examples include manual or computer based systems that attach care reminders to the charts of patients needing specific preventive care services and computerised physician order entry systems that provide patient specific recommendations as part of the order entry process. Such systems have been shown to improve prescribing practices,5 7 reduce serious medication errors,8 9 enhance the delivery of preventive care services,10 11 and improve adherence to recommended care standards.4 12 Compared with other approaches to improve practice, these systems have also generally been shown to be more effective and more likely to result in lasting improvements in clinical practice.13 22
Clinical decision support systems do not always improve clinical practice, however. In a recent systematic review of computer based systems, most (66%) significantly improved clinical practice, but 34% did not.4 Relatively little sound scientific evidence is available to explain why systems succeed or fail.23 24 Although some investigators have tried to identify the system features most important for improving clinical practice,12 25 34 they have typically relied on the opinion of a limited number of experts, and none has combined a systematic literature search with quantitative meta analysis. We therefore systematically reviewed the literature to identify the specific features of clinical decision support systems most crucial for improving clinical practice.
Data sourcesInclusion and exclusion criteria
We defined a clinical decision support system as any electronic or non electronic system designed to aid directly in clinical decision making, in which characteristics of individual patients are used to generate patient specific assessments or recommendations that are then presented to clinicians for consideration.4 We included both electronic and non electronic systems because we felt the use of a computer represented only one of many potentially important factors. Our inclusion criteria were any randomised controlled trial evaluating the ability of a clinical decision support system to improve an important clinical practice in a real clinical setting; use of the system by clinicians (physicians, physician assistants, or nurse practitioners) directly involved in patient care; and assessment of improvements in clinical practice through patient outcomes or process measures. Disagreements between reviewers were resolved by discussion, and we measured inter rater agreement using Cohen's unweighted statistic.35A study may include several trial arms, so that multiple comparisons may exist within the single study. For each relevant comparison,fake rolex submariner date, two reviewers independently assessed whether the clinical decision support system resulted in an improvement in clinical practice that was both statistically and clinically significant. We considered effect size as an alternative outcome measure but concluded that the use of effect size would have been misleading given the significant heterogeneity among the outcome measures reported by the included studies. We also anticipated that the use of effect size would have led to the exclusion of many relevant trials, as many studies fail to report all of the statistical elements necessary to accurately reconstruct effect sizes.
Next, two reviewers independently determined the presence or absence of specific features of decision support systems that could potentially explain why a system succeeded or failed. To construct a set of potential explanatory features, we systematically examined all relevant reviews and primary studies identified by our search strategy and recorded any factors suggested to be important for system effectiveness. Both technical and non technical factors were eligible for inclusion. Also, if a factor was designated as a barrier to effectiveness (such as "the need for clinician data entry limits system effectiveness") we treated the logically opposite concept as a potential success factor (such as "removing the need for clinician data entry enhances system effectiveness"). Next,submariner rolex imitation, we limited our consideration to features that were identified as being potentially important by at least three sources, which left us with 22 potential explanatory features, including general system features, system clinician interaction features, communication content features, and auxiliary features (tables 1 and 2). Of these 22 features, 15 could be included into our analysis (table 1) because their presence or absence could be reliably abstracted from most studies, whereas the remaining seven could not (table 2).
Table 1 Descriptions of the 15 features of clinical decision support systems (CDSS) included in statistical analyses
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We used three methods to identify clinical decision support system features important for improving clinical practice.
Univariate analyses For each of the 15 selected features we individually determined whether interventions possessing the feature were significantly more likely to succeed (result in a statistically and clinically significant improvement in clinical practice) than interventions lacking the feature. We used StatXact55 to calculate 95% confidence intervals for individual success rates56 and for differences in success rates,womens rolex submariner imitation.57
Multiple logistic regression analyses For these analyses, the presence or absence of a statistically and clinically significant improvement in clinical practice constituted the binary outcome variable, and the presence or absence of specific decision support system features constituted binary explanatory variables. We included only cases in which the clinical decision support system was compared against a true control group. For the primary meta regression analysis, we pooled the results from all included studies, so as to maximise the power of the analysis while decreasing the risk of false positive findings from over fitting of the model.58 We also conducted separate secondary regression analyses for computer based systems and for non electronic systems. For all analyses, we included one indicator for the decision support subject matter (acute care v non acute care) and two indicators for the study setting (academic v non academic, outpatient v inpatient) to assess the role of potential confounding factors related to the study environment. With the 15 system features and the three environmental factors constituting the potential explanatory variables,ladies rolex submariner imitation, we conducted logistic regression analyses using LogXact 5.59 Independent predictor variables were included into the final models using forward selection and a significance level of 0.05.
Direct experimental evidence We systematically identified studies in which the effectiveness of a given decision support system was directly compared with the effectiveness of the same system with additional features. We considered a feature to have direct experimental evidence supporting its importance if its addition resulted in a statistically and clinically significant improvement in clinical practice.
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.
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
Recent research has shown that health care delivered in industrialised nations often falls short of optimal, evidence based care. A nationwide audit assessing 439 quality indicators found that US adults receive only about half of recommended care,1 and the US Institute of Medicine has estimated that up to 98 000 US residents die each year as the result of preventable medical errors.2 Similarly a retrospective analysis at two London hospitals found that 11% of admitted patients experienced adverse events, of which 48% were judged to be preventable and of which 8% led to death.3
To address these deficiencies in care, healthcare organisations are increasingly turning to clinical decision support systems, which provide clinicians with patient specific assessments or recommendations to aid clinical decision making.4 Examples include manual or computer based systems that attach care reminders to the charts of patients needing specific preventive care services and computerised physician order entry systems that provide patient specific recommendations as part of the order entry process. Such systems have been shown to improve prescribing practices,5 7 reduce serious medication errors,8 9 enhance the delivery of preventive care services,10 11 and improve adherence to recommended care standards.4 12 Compared with other approaches to improve practice, these systems have also generally been shown to be more effective and more likely to result in lasting improvements in clinical practice.13 22
Clinical decision support systems do not always improve clinical practice, however. In a recent systematic review of computer based systems, most (66%) significantly improved clinical practice, but 34% did not.4 Relatively little sound scientific evidence is available to explain why systems succeed or fail.23 24 Although some investigators have tried to identify the system features most important for improving clinical practice,12 25 34 they have typically relied on the opinion of a limited number of experts, and none has combined a systematic literature search with quantitative meta analysis. We therefore systematically reviewed the literature to identify the specific features of clinical decision support systems most crucial for improving clinical practice.
Data sourcesInclusion and exclusion criteria
We defined a clinical decision support system as any electronic or non electronic system designed to aid directly in clinical decision making, in which characteristics of individual patients are used to generate patient specific assessments or recommendations that are then presented to clinicians for consideration.4 We included both electronic and non electronic systems because we felt the use of a computer represented only one of many potentially important factors. Our inclusion criteria were any randomised controlled trial evaluating the ability of a clinical decision support system to improve an important clinical practice in a real clinical setting; use of the system by clinicians (physicians, physician assistants, or nurse practitioners) directly involved in patient care; and assessment of improvements in clinical practice through patient outcomes or process measures. Disagreements between reviewers were resolved by discussion, and we measured inter rater agreement using Cohen's unweighted statistic.35A study may include several trial arms, so that multiple comparisons may exist within the single study. For each relevant comparison,fake rolex submariner date, two reviewers independently assessed whether the clinical decision support system resulted in an improvement in clinical practice that was both statistically and clinically significant. We considered effect size as an alternative outcome measure but concluded that the use of effect size would have been misleading given the significant heterogeneity among the outcome measures reported by the included studies. We also anticipated that the use of effect size would have led to the exclusion of many relevant trials, as many studies fail to report all of the statistical elements necessary to accurately reconstruct effect sizes.
Next, two reviewers independently determined the presence or absence of specific features of decision support systems that could potentially explain why a system succeeded or failed. To construct a set of potential explanatory features, we systematically examined all relevant reviews and primary studies identified by our search strategy and recorded any factors suggested to be important for system effectiveness. Both technical and non technical factors were eligible for inclusion. Also, if a factor was designated as a barrier to effectiveness (such as "the need for clinician data entry limits system effectiveness") we treated the logically opposite concept as a potential success factor (such as "removing the need for clinician data entry enhances system effectiveness"). Next,submariner rolex imitation, we limited our consideration to features that were identified as being potentially important by at least three sources, which left us with 22 potential explanatory features, including general system features, system clinician interaction features, communication content features, and auxiliary features (tables 1 and 2). Of these 22 features, 15 could be included into our analysis (table 1) because their presence or absence could be reliably abstracted from most studies, whereas the remaining seven could not (table 2).
Table 1 Descriptions of the 15 features of clinical decision support systems (CDSS) included in statistical analyses
View this table:View popupView inline
We used three methods to identify clinical decision support system features important for improving clinical practice.
Univariate analyses For each of the 15 selected features we individually determined whether interventions possessing the feature were significantly more likely to succeed (result in a statistically and clinically significant improvement in clinical practice) than interventions lacking the feature. We used StatXact55 to calculate 95% confidence intervals for individual success rates56 and for differences in success rates,womens rolex submariner imitation.57
Multiple logistic regression analyses For these analyses, the presence or absence of a statistically and clinically significant improvement in clinical practice constituted the binary outcome variable, and the presence or absence of specific decision support system features constituted binary explanatory variables. We included only cases in which the clinical decision support system was compared against a true control group. For the primary meta regression analysis, we pooled the results from all included studies, so as to maximise the power of the analysis while decreasing the risk of false positive findings from over fitting of the model.58 We also conducted separate secondary regression analyses for computer based systems and for non electronic systems. For all analyses, we included one indicator for the decision support subject matter (acute care v non acute care) and two indicators for the study setting (academic v non academic, outpatient v inpatient) to assess the role of potential confounding factors related to the study environment. With the 15 system features and the three environmental factors constituting the potential explanatory variables,ladies rolex submariner imitation, we conducted logistic regression analyses using LogXact 5.59 Independent predictor variables were included into the final models using forward selection and a significance level of 0.05.
Direct experimental evidence We systematically identified studies in which the effectiveness of a given decision support system was directly compared with the effectiveness of the same system with additional features. We considered a feature to have direct experimental evidence supporting its importance if its addition resulted in a statistically and clinically significant improvement in clinical practice.
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