The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: a systematic review and meta-analysis

Abstract

Background: Clinical Decision Support Systems (CDSSs) for Prescribing are one of the innovations designed to improve physician practice performance and patient outcomes by reducing prescription errors. This study was therefore conducted to examine the effects of various CDSSs on physician practice performance and patient outcomes.

Methods: This systematic review was carried out by searching PubMed, Embase, Web of Science, Scopus, and Cochrane Library from 2005 to 2019. The studies were independently reviewed by two researchers. Any discrepancies in the eligibility of the studies between the two researchers were then resolved by consulting the third researcher. In the next step, we performed a meta-analysis based on medication subgroups, CDSS-type subgroups, and outcome categories. Also, we provided the narrative style of the findings. In the meantime, we used a random-effects model to estimate the effects of CDSS on patient outcomes and physician practice performance with a 95% confidence interval. Q statistics and I² were then used to calculate heterogeneity.

Results: On the basis of the inclusion criteria, 45 studies were qualified for analysis in this study. CDSS for prescription drugs/COPE has been used for various diseases such as cardiovascular diseases, hypertension, diabetes, gastrointestinal and respiratory diseases, AIDS, appendicitis, kidney disease, malaria, high blood potassium, and mental diseases. In the meantime, other cases such as concurrent prescribing of multiple medications for patients and their effects on the above-mentioned results have been analyzed. The study shows that in some cases the use of CDSS has beneficial effects on patient outcomes and physician practice performance (std diff in means = 0.084, 95% CI 0.067 to 0.102). It was also statistically significant for outcome categories such as those demonstrating better results for physician practice performance and patient outcomes or both. However, there was no significant difference between some other cases and traditional approaches. We assume that this may be due to the disease type, the quantity, and the type of CDSS criteria that affected the comparison. Overall, the results of this study show positive effects on performance for all forms of CDSSs.

Conclusions: Our results indicate that the positive effects of the CDSS can be due to factors such as user-friendliness, compliance with clinical guidelines, patient and physician cooperation, integration of electronic health records, CDSS, and pharmaceutical systems, consideration of the views of physicians in assessing the importance of CDSS alerts, and the real-time alerts in the prescription.

Keywords: Computerized clinical decision support systems; Medication prescription; Systematic review.

Fig. 1

The PRISMA flow diagram of selected studies. The number of records for each database is specified. The PRISMA theory approach is also displayed in the blue rectangles

Fig. 2

The number of studies based on several evaluating outcomes. The number of studies that assessed different kinds of outcomes based on patient outcomes, physician performance, or both outcomes is identified

Fig. 3

The number of studies based on the type of included studies. The number of studies focused on different types of randomized controlled trials has been established

Fig. 4

Forest plot of the overall effect of CDSS for prescribing on physician practice performance and patient outcome based on medication subgroup analysis. Meta-analysis is conducted using Comprehensive Meta-Analysis (CMA) statistical tools. The pooled std diff in means of p values showed a significant difference between the CDSS and the control group (std diff in means = 0.091, 95% CI 0.072–0.109, standard error = 0.010). Confidence Interval (CI) represents for the linear area between lower and upper limits

Fig. 5

Forest plot of the overall effect of CDSS for prescribing on physician practice performance and patient outcome based on medication subgroup analysis after sensitivity analysis. After sensitivity analysis, heterogeneity improved considerably, excluding khonsari et al. [33]; Ackerman et al. [49]; Avansino et al. [51], and Bruxvoort et al. [59]. The pooled std diff in means of p values was used for evaluating the overall and subgroup effects of CDSS which were significantly different (std diff in means = 0.084, 95% CI 0.067–0.102) as a whole. Meta-analysis results for each subgroup of medication scope showed a significant difference between CDSS and control groups for medication scopes namely as hypertension (CI 0.102–0.272); increasing blood potassium (CI 0.006–0.066); multiple medications (CI 0.084–0.332); AIDs (CI 0.038–0.444); kidney disorders (CI 0.073–0.193); diabetes (CI 0.223–0.539); cardiac (CI 0.035–0.111); mental disease (CI 0.010–0.114); medication prescription (CI 0.094–0.219); and pulmonary disease (CI 0.014–0.144)

Fig. 6

The number of studies associated with each country. The number of studies carried out in different countries is identified

Fig.7

The number of studies associated with each CDSS type. The number of studies that were performed on various types of CDSS such as reminders and alarms is listed

Fig. 8

Funnel plot of standard error by std diff in means. There was no significant difference for publication bias for the included studies (p value = 0.000001). X-axis shows std diff in mean in the funnel diagram and the Y-axis reflects standard error. Dispersion of studies in the funnel plot showed that there was no bias in publication

Fig. 9

Forest plot of the effect of CDSS for prescribing on physician practice performance and patient outcome based on subgroup analysis for CDSS types. The subgroup analysis for various CDSS types showed a significant difference for alerts (CI 0.082–0.0187); combination types of CDSSs (CI 0.022–0.372); recommendation CDSSs (CI 0.063–0.166); reminders (CI 0.072–0.189); and instructional CDSSs (0.081 to 0.178). The results are assessed following the exclusion of khonsari et al. [33]; Ackerman et al. [49]; Avansino et al. [51] and Bruxvoort et al. [59] studies

Fig. 10

Forest plot of the overall effect of CDSS for prescribing on physician practice performance and patient outcome based on outcome categorization. The pooled std diff in the mean p values did not indicate a significant difference between the CDSS and the control group before the sensitivity analysis was performed (std diff in means = 0.0110, 95% CI 0.086–0.138, standard error = 0.013)

Fig. 11

Forest plot of the overall effect of CDSS for prescribing on physician practice performance and patient outcome based on outcome categorization. The overall effects of prescribing CDSS on patient outcomes and physician practice performance after performing sensitivity analysis were significantly different: (std diff in means = 0.114, 95% CI 0.090–0.138). The outcome analysis showed a significant difference between CDSS and the control group for outcome categories such as patient outcomes improved (CI 0.122–0.747); physician practice performance improved (CI 0.78–0.133); physician practice performance and patient outcomes improved (CI 0.111–0.281); and physician practice performance didn’t improve (CI 0.040–0.222). There was not a significant difference in the category of ‘not improved’ for patient outcomes (CI − 0.038 to 0.165). The results are assessed following the exclusion of khonsari et al. [33]; Ackerman et al. [49]; Avansino et al. [51] and Bruxvoort et al. [59] studies