On the basis of the inclusion criteria, we selected 45 qualified articles (Fig.1). The assessment of the studies provided us with valuable information on the research goals, the types of electronic prescription systems, the types of diseases, and patients. Table 1 indicates that the findings of the quality evaluation of the studies were appropriate.
Table 1. Quality assessment for trials.
Author (year of publication)
|
Was research described as randomized?
|
Was approach of randomization appropriate?
|
Was research described as blinding?
|
Was approach of blinding appropriate?
|
Was there a presentation of withdrawal and dropouts?
|
Was there a presentation of the inclusion / exclusion criteria?
|
Was approach used to assess outcome?
|
Was the approach of statistical analysis described?
|
Total
|
Beeler et al. )2014( [25]
|
1
|
0
|
0
|
0
|
1
|
0
|
1
|
1
|
4
|
Eckman et al. )2016( [26]
|
1
|
1
|
0
|
0
|
1
|
1
|
1
|
1
|
6
|
Du et al. )2018( [27]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
Karlsson et al. )2018( [28]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
Mazzaglia et al.) 2016( [29]
|
1
|
1
|
1
|
1
|
1
|
0
|
1
|
1
|
7
|
Nielsen et al. )2017( [30]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
Patel et al. )2018( [31]
|
1
|
1
|
0
|
0
|
1
|
1
|
1
|
1
|
6
|
Akhu-zaheya et al. )2017( [32]
|
1
|
1
|
0
|
0
|
0
|
1
|
1
|
1
|
5
|
Khonsari et al. )2015( [33]
|
1
|
1
|
0
|
0
|
0
|
0
|
1
|
1
|
4
|
Christensen et al .) 2010( [34]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
Luitjes et al . )2018( [35]
|
1
|
1
|
1
|
1
|
0
|
1
|
1
|
1
|
7
|
Buhse et al. )2018( [36]
|
1
|
1
|
0
|
0
|
1
|
0
|
1
|
1
|
5
|
Perestelo-pérez et al. )2016( [37]
|
1
|
1
|
0
|
0
|
1
|
1
|
1
|
1
|
6
|
Sáenz et al. )2012( [38]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
Vervloet et al. )2014( [39]
|
1
|
1
|
1
|
1
|
0
|
0
|
1
|
1
|
6
|
Vervloet et al. )2012( [40]
|
1
|
1
|
1
|
1
|
0
|
1
|
1
|
1
|
7
|
Geurts et al. )2017( [41]
|
1
|
1
|
0
|
0
|
1
|
0
|
1
|
1
|
5
|
Gill et al. )2011( [42]
|
1
|
1
|
0
|
0
|
1
|
1
|
1
|
1
|
6
|
Petersen et al. )2017( [43]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
Bourgeois et al. )2010( [44]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
Juszczyk et al. )2017( [45]
|
1
|
1
|
1
|
1
|
1
|
0
|
1
|
1
|
7
|
Mcdermott et al. )2014( [46]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
Mcginn et al. )2013( [47]
|
1
|
1
|
0
|
0
|
1
|
1
|
1
|
1
|
6
|
Mohammed et al. )2016 ([48]
|
1
|
1
|
1
|
1
|
0
|
1
|
1
|
1
|
7
|
Ackerman et al. )2013( [49]
|
1
|
1
|
0
|
0
|
1
|
0
|
1
|
1
|
5
|
Pop-eleches et al. )2011( [50]
|
1
|
1
|
0
|
0
|
1
|
1
|
1
|
1
|
6
|
Avansino et al. )2012( [51]
|
1
|
1
|
1
|
1
|
1
|
0
|
1
|
1
|
7
|
Awdishu et al. )2016( [52]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
Erler et al. )2012( [53]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
Cox et al. )2011( [54]
|
1
|
1
|
0
|
0
|
1
|
0
|
1
|
1
|
5
|
Muth et al. )2018( [55]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
Strom et al. )2010( [56]
|
1
|
1
|
0
|
0
|
1
|
1
|
1
|
1
|
6
|
Strom et al. )2010( [57]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
Elliott et al. )2017( [58]
|
1
|
1
|
1
|
1
|
0
|
0
|
1
|
1
|
6
|
Bruxvoort et al. )2014( [59]
|
1
|
1
|
1
|
1
|
0
|
1
|
1
|
1
|
7
|
Beeler et al. )2019( [60]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
Duke et al. )2013( [61]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
Eschmann et al. )2015( [62]
|
1
|
1
|
1
|
1
|
1
|
0
|
1
|
1
|
7
|
Curtain et al. )2011( [5]
|
1
|
1
|
0
|
0
|
1
|
1
|
1
|
1
|
6
|
Turchin et al. )2011( [6]
|
1
|
1
|
0
|
0
|
1
|
1
|
1
|
1
|
6
|
Griffey et al. )2012( [63]
|
1
|
1
|
0
|
0
|
1
|
1
|
1
|
1
|
6
|
Myers et al. )2011( [64]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
Van Stiphout et al. )2018( [65]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
Willis et al. )2013( [66]
|
1
|
1
|
1
|
1
|
0
|
1
|
1
|
1
|
7
|
Tamblyn et al. )2012( [67]
|
1
|
1
|
1
|
1
|
0
|
1
|
1
|
1
|
7
|
- Total point earned
- Quality Score
|
303
82.34
|
1 stands for the answer “yes”, and 0 stands for the answer “no”.
The findings also demonstrated the effectiveness of CDSS in many diseases such as cardiovascular disease, high blood pressure, and diabetes, or cases such as simultaneous prescription of drugs. Findings from the analyzed studies are presented in Table 2 in which * stands for p-values indicating a statistically significant difference.
The number of studies based on multiple evaluation results and types of studies is also shown in Fig. 2 and Fig. 3, respectively. Table 2 shows the variety of outcomes for several medication scopes (for example, the outcome "Increasing the ratio of prescribing prophylaxis" is specific for cardiovascular domain, or the outcome "Reducing blood pressure" is related to hypertension disorders). Meanwhile, Table 2 shows various kinds of CDSSs for prescribing classified according to alerts, reminders, recommendations, instruction, and a combination of these types. Table 2 also briefly presents the outcome of the thirteen medication scopes involved.
Table 2. Data extracted for CDSS trials
Author (year of publication)
|
Disease type
|
No. of hospitals/physicians/patients
|
Type of computer system
|
Outcome
|
P-value
|
Beeler et al. )2014( [25]
|
cardiovascular
|
-/-/15736
|
Computerized system equipped with reminder to prevent intravenous thromboembolism
|
Increasing the ratio of prescribing prophylaxis 6-24 hours after admission/transfer
|
*P-value <0/0001 *
|
*0/03
|
Eckman et al. )2016 ([26]
|
cardiovascular
|
15 /-/1493
|
CDSS providing treatment recommendation
|
Reducing disagreement among physicians
|
*0/02
|
Du et al. )2018( [27]
|
cardiovascular
|
58/-/patients
|
CDSS in mobile devices
|
Increasing secondary preventive prescriptions after 15 months in the intervention group
|
From 73/7 to 86/8 percent
|
Karlsson et al. )2018( [28]
|
cardiovascular
|
43 /-/14134
|
CDSS equipped with alerts for patients with atrial fibrillation
|
Increasing the prescription of anticoagulation after 12 months
|
*0/01
|
Mazzaglia et al. )2016( [29]
|
cardiovascular
|
-/197 /-
|
Alert-based CDSS for patients using cardiovascular drugs
|
Increasing prescription of anti-blocking drugs
|
*P-value<0/001
|
Nielsen et al. )2017( [30]
|
cardiovascular
|
-/-/191
|
CDSS to regulate the rate of warfarin use
|
Increasing the time outcome in the scope of treatment
|
0/67 Percent
|
Patel et al. )2018( [31]
|
cardiovascular
|
23 /178/-
|
Framework for the UK Medical Research Council (MRC)
|
Increasing the number of anti-inflammatory/lipid-lowering drugs
|
*P-value <0/001
|
Akhu-zaheya et al. )2017( [32]
|
cardiovascular
|
-/-/160
|
Short message reminder system in adherence to a healthy nutritional diet, drugs, cessation of smoking
|
Increasing prescriptions in the short message group
|
*0/001
|
Khonsari et al. )2015( [33]
|
cardiovascular
|
-/-/62
|
Web-based software equipped with text reminders for patients with chronic coronary syndrome
|
Increasing adherence to drug usage
|
*P-value>0/01
|
Christensen et al.) 2010( [34]
|
hypertension
|
-/-/398
|
Reminder in patient admission and blood pressure control
|
Reducing blood pressure after 12 months
|
0/06
|
Luitjes et al . 2018 [35]
|
hypertension
|
16/-/532 at pre implementation phase,-/-/1762 at post implementation phase
|
Innovative strategy including decision support system, audit and feedback
|
For the control group, reducing the secondary outcome of infant morbidity after implementation
|
<0/0001 *P-value
|
Buhse et al. )2018( [36]
|
diabetes
|
22/-/363
|
ISDM-P program composed of CDSS and sessions
|
Reduction in faulty knowledge causing risk
|
*P-value <0/001
|
Perestelo-pérez et al. )2016( [37]
|
diabetes
|
14/29/168
|
The CDSS selects statin with an estimate of cardiovascular disease risk
|
Increasing satisfaction of decision making
|
*0/009
|
Sáenz et al. )2012( [38]
|
diabetes
|
66/-/697
|
The CDSS including patient data, glucose profile and recommendation for physician
|
Increasing long-term blood sugar using between group differences
|
*0/01
|
Vervloet et al. )2014( [39]
|
diabetes
|
-/-/161
|
Real-time monitoring system for drug use by applying short message for diabetic patients
|
Increasing adherence in the group receiving short messages
|
*P-value <0/001
|
Vervloet et al. )2012( [40]
|
diabetes
|
-/-/104
|
Real-time medication monitoring system equipped with short message reminder for patients with type two diabetes
|
Increasing the drug dosage in one hour during a six month period
|
*0/003
|
Geurts et al. )2017( [41]
|
digestive diseases
|
-/-/222
|
Recommendation decision support system
|
Increasing the standard use of oral rehydration solution
|
*P-value <0/05
|
Gill et al. )2011( [42]
|
digestive diseases
|
27/119/5234
|
CDSS equipped with alert functionality and integrated with electronic health record and clinical guidelines
|
Increasing the receiving care on the basis of instructions for patients with low-dose aspirin use (25%)
|
1/30
|
Petersen et al. )2017( [43]
|
digestive diseases
|
General physicians
|
CDSS equipped with risk notification service
|
Increasing the drug prescription in patients with risk above 5 percent
|
*0/01
|
Bourgeois et al. )2010( [44]
|
pulmonary diseases
|
-/112/-
|
Chronic obstructive pulmonary disease pattern in electronic health records
|
Reduced antibiotic prescriptions in visits by using templates
|
*0/02
|
Juszczyk et al. )2017( [45]
|
pulmonary diseases
|
-/79/-
|
Electronic health records combined with databases of Electronic medical records such as links to clinical practice research data
|
Reducing unnecessary prescription of antibiotics
|
*0/04
|
Mcdermott et al. )2014( [46]
|
pulmonary diseases
|
-/103/-
|
DSS and electronic learning
|
Increasing physicians self-efficacy
|
*0/02
|
Mcginn et al. )2013( [47]
|
pulmonary diseases
|
-/-/984
|
A real time and unified CDSS during care combined with integrated clinical prediction rules
|
Reduced antibiotic prescription
|
*0/008
|
Mohammed et al. )2016( [48]
|
pulmonary diseases
|
-/-/2207
|
Short message as a two-way reminder
|
Inability to be effective in treatment success rate
|
0/76
|
Ackerman et al. )2013( [49]
|
pulmonary diseases
|
-/29/33
|
CDSS in Electronic Health Records
|
Reducing excess prescription of antibiotics
|
*0/003
|
Pop-eleches et al. )2011( [50]
|
AIDS
|
-/-/428
|
Short-message reminder systems (daily and weekly) in the antivirus treatment process
|
Reducing the number of treatment interruptions in both groups receiving weekly messages
|
*0/02
|
Avansino et al. )2012( [51]
|
appendicitis
|
-/7/-
|
Systematically developed order set for using the decision support system
|
Increasing the follow-up clinical guidelines for systematic prescriptions compared to case prescriptions
|
*0/003
|
Awdishu et al. )2016( [52]
|
kidney diseases
|
-/514/1278
|
DSS Warning
|
An increase in not taking medication or changing dose of inadequate drugs
|
*P-value<0/0001
|
Erler et al. )2012( [53]
|
kidney diseases
|
-/44/404
|
Software including a database in coronary resection
|
Reduction in the amount of medication received in the intervention group in excess of the prescribed dose
|
*0/04
|
Cox et al. )2011( [54]
|
taking multiple medications
|
-/-/216
|
The CDSS with medication order entry in order to determine the initial drug dosage
|
An increase in the number of prescriptions for initial drug use
|
*P-value <0/0001
|
An increase in the conformity of prescribed medication percentage with the suggested medication
|
*P-value <0/00001
|
Muth et al. )2018( [55]
|
taking multiple medications
|
-/71/465
|
Reminder-based CDSS
|
Ineffectiveness of drug prescriptions after 6 and 9 months
|
0/31, 0/18
|
Strom et al. )2010( [56]
|
taking multiple medications
|
-/1981/-
|
Computerized drug prescribing systems equipped with hard-alerted CDSSs
|
Increasing the percentage of appropriate alerts that have been responded to by physicians in the intervention group compared to the control group
|
57/2 vs. 13/5
|
Strom et al. )2010( [57]
|
taking multiple medications
|
-/1963/-
|
Computerized medication order entry system equipped with various alerts
|
Reduction in the appropriate response of physicians to alerts during 17 months
|
*0/007
|
Elliott et al. )2017( [58]
|
taking multiple medications
|
-/-/110
|
Prescribing CDSS for creating drug treatment recommendations such as drug-drug and drug-gene interaction
|
Reducing the average number of days re-hospitalized 60 days after discharge
|
*0/007
|
Reducing the combination of re-hospitalizations, emergency ward visits and morbidity 60 days after discharge
|
*0/005
|
Bruxvoort et al. )2014( [59]
|
Malaria
|
82/-/-
|
Text message reminders for Malaria treatment
|
Physicians’ knowledge in using Lumefantrine orthometer
|
*P-value <0/0001
|
Beeler et al. )2019( [60]
|
increasing blood potassium
|
29/-/4861
|
Three types of CDSSs including reminder, high potassium and calcium alerts
|
An increase in the average monitoring time of potassium level
|
*P-value <0/001
|
Duke et al. )2013( [61]
|
increasing blood potassium
|
-/1029/-
|
Drug-drug interaction alerts for patients in danger of high potassium level
|
A decrease in the conformity rate in normal risk patients for increased potassium
|
*P-value <0/01
|
Eschmann et al. )2015( [62]
|
increasing blood potassium
|
15/-/37000
|
Electronic health records equipped with alerts and reminders systems
|
A decrease in the reaction time of reminders for physicians monitoring alerts of potassium level
|
*0/04
|
Curtain et al. )2011( [5]
|
medication prescription for the patient
|
185/-/-
|
CDSS for drug distribution in treatment with proton pump
|
Reduction in the approved percentage of inhibitor intervention proton pump which is registered by the pharmacologist
|
*P-value <0/001
|
Turchin et al. )2011( [6]
|
medication prescription for the patient
|
-/3703/-
|
Hard alert systems to facilitate medication services
|
Increasing overall efficiency of system functionalities prior to admission
|
*P-value <0/0001
|
Griffey et al. )2012( [63]
|
medication prescription for the patient
|
-/-/1407
|
CDSS for recommending drug dosage
|
Increasing the number of prescriptions by recommending the determined system dose
|
*P-value <0/0001
|
Myers et al. )2011( [64]
|
medication prescription for the patient
|
-/59/-
|
Computerized alerts for manual or automatic correction of medical abbreviation
|
Reducing the significant number of inappropriate abbreviations
|
*0/02
|
Van Stiphout et al. )2018( [65]
|
medication prescription for the patient
|
2/115/1094
|
CDSS integrated with training session
|
More efficient medical summary
|
*0/03
|
Willis et al. )2013( [66]
|
medication prescription for the patient
|
-/-/2219
|
CDSS alerts for the primary care clinic
|
A lack of difference in the rate of patient adherence to treatment, drug treatment significance, economic and clinical outcomes in three groups
|
*0/01
|
Tamblyn et al. )2012( [67]
|
mental disorders
|
-/81/5628
|
DSS equipped with three types of alerts
|
Reduction in dose of drugs after one year for antipsychotics
|
*0/02
|
The effect of CDSS on cardiovascular diseases
For patients admitted to the hospital, the level of venous thromboembolism prophylaxis, and the proportion of prescribed prophylaxis increased during 6-24 hours after admission [25]. In another study, the discrepancies among physicians over the thromboprophylaxis treatment decreased with the aid of CDSS by offering treatment recommendations (p= 0.02) [26]. In other studies, alert-based CDSSs have positive effects on physician performance and treatment improvement in anti-inflammatory and lipid-lowering drugs [28, 29, 31]. By following medical recommendations in another study, physicians in the intervention group were able to improve the prescribing level of secondary preventive medication through using a regular CDSS [30]. Also, in other trials, the short messages of the program had a positive effect on patient adherence to medication and diet (p<0.01) [32, 33].
The effect of CDSS on hypertension
In one study, the electronic monitoring and recall program had no effect on blood pressure reduction and the admission of patients [34]. However, in another study, the patient outcome improved following the implementation of the CDSS [35].
The effect of CDSS on diabetes
In some studies, the Real-Time Medication Monitoring (RTMM) system, equipped with a short message reminder, improved the precision of patients’ compliance and missed dose [36, 37, 39, 40]. In another study, HbA1c and group differences were greater in the intervention group using recommendation CDSS than that of the control group [38]. The use of statins (p = 0.03) and the problem areas in diabetes (PAID) (p=0.01) improved in another study for the intervention group that used CDSS [37].
The effect of CDSS on digestive diseases
In all studies, the CDSS had an effect on prescribing non-steroidal anti-inflammatory drugs, proton pump inhibitors, and increasing the standard use of oral rehydration solution without any difference in other results [41-43]. Also, alert-based CDSS improved the quality of patient care in another study [42].
The effect of CDSS on pulmonary diseases
In some trials, the use of CDSS which was integrated with electronic health record or prediction rules resulted in a decrease in the prescribing of antibiotics and macrolides; therefore, it helped minimize the inappropriate use of antibiotics (p= 0.0005), reduce the resistance to antibiotics (p= 0.04), and enhance primary care [44-47, 49]. The patients adhered to the reminder message in another study; however, the messages did not affect the success rate of therapy [48].
The effect of CDSS on AIDS
Results of this study showed that the reminder system for short text messages had a positive effect on the treatment process. Also, the number of messages did not have a significant effect on patients’ compliance rates (p = 0.12) [50].
The effect of CDSS on appendicitis
This study showed that the system's systematically developed order set, which used clinical guidelines, improved system usability (p=0.05), and reduced system-related problems (p=0.05). This is the result of Computerized Provider Order Entry (CPOE) which improved efficiency, quality, and safety [51].
The effect of CDSS on kidney diseases
One study showed the positive effect of the multipurpose intervention on creatinine value estimation and dose adjustment to reduce the insufficient dosage of primary care drugs [53]. In another study, the appropriate prescription rate for kidney problems was low, as opposed to the results of the former study. Also, the effectiveness of the CDSS with physician guidelines has been improved [52].
The effect of CDSS on taking multiple medications
In one study, CDSSs resulted in delayed drug treatment for four patients needing urgent treatment. This suggests that the adverse effects of these systems need to be evaluated and monitored [56]. In another study, the CDSS improved the primary dose of medication, time intervals for drug use, and drug concentration which is to be injected intravenously compared to standard doses [54]. Also in another study, the average number of readmission days for each patient and the combination of re-hospitalization and emergency ward visits within 30 days after hospital discharge did not vary between the intervention group using recommendation CDSS and control groups [58]. In some trials, there was no discrepancy between the outcomes of the dosage rate and the Modified Medication Appropriateness Index (MMAI).
In the meanwhile, no discrepancy was seen among improper medication prescribing (p= 0.48), the Medication Regimen Complexity Index, and the mean pain outcome difference after 6 months (p= 0.13) and 9 months (p= 0.78) between the intervention group using alert or reminder CDSS and the control group [55, 57].
The effect of CDSS on Malaria
The use of text-messaging in one study did not affect the patients’ behavior in completing the course of medication for the full duration of treatment. However, when the side effects were low (p= 0.02), it had some effects on the continuous use of the medication. In addition, text messages had an effect on physicians’ knowledge about the use of medications with fatty foods. (p <0.0001) [59].
The effect of CDSS on increasing the level of blood potassium
In one study, there is no statistical difference between the control and intervention groups in terms of following alerts and patients’ compliance rate. However, the physicians’ compliance rate improved at the medium potassium level from 3 to 3.9 (mili-equivalents/liter) (p<0.01) [61]. Due to the rapid response of physicians to program alerts for high potassium levels in the intervention group, the positive effect of the system on physician performance was evident in another study (p= 0.01) [62]. However, in another study in this section, the time-lapse in hyperkalemia monitoring (p= 0.20) and the incidence rate of hyperkalemia (p= 0.22) did not vary significantly even with the use of three different kinds of reminder and alert-based CDSSs [60].
The effect of CDSS on medication prescription for patients
Based on the results of some studies, the regular or alert based CDSSs resulted in better drug prescriptions for the proton pump inhibitor and a reduction in abbreviation prescriptions [5, 64]. Also, in other studies, the overall utilization of system functionalities, system utilization between two-time laps (p<0.0001), number of users (p<0.0001), and physicians’ compliance with the medication recommendations provided by the CDSS improved medication prescriptions which eventually resulted in reduced side effects (p= 0.02) [6, 63]. There was no difference in prescribing among physicians in one study (p=0.14); however, the percentage of skilled questions for the intervention group equipped with training CDSS (p= 0.01) improved [65]. In another study, alert-based CDSSs have been effective in identifying evidence-based pharmacotherapies (EBP). In the meantime, compliance with treatment by health care managers has had no effect on patient outcome [66].
The effect of CDSS on mental disorders
CDSS alerts resulted in reduced risk of injury and reduced dose of antipsychotics and anticoagulants (p= 0.03) over a one-year period. Therefore, CDSS reduced the risk of injury (p= 0.02) [67].
Statistical and sensitivity analysis
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 to 0.109, standard error = 0.010). 95% CI for the effectiveness was drawn for each study in the horizontal line format (Q = 209.2, df = 45, p= 0.0002, I2 = 78.492, Tau2: 0.004) (Fig. 4). Due to the high heterogeneity of results, a sensitivity analysis was performed. In doing so, we excluded the following studies: khonsari et al [33]; Ackerman et al. [49]; Avansino et al. [51], and Bruxvoort et al. [59]. Because of the limited number of patients in these trials, we decided to exclude them from our meta-analysis. In Tables 2 and 3, the characteristics of these studies are presented in narrative results. The findings indicate that heterogeneity improved considerably after sensitivity analysis (Fig. 5). (Q = 164.8, df = 41, p= 0.0001, I2 = 75.136, Tau2: 0.003). The overall effect of CDSS for prescribing medications on patient outcomes and physician practice performance based on the random-effects model was statistically significant (std diff in means = 0.84, 95% CI: 0.067 to 0.102).
Subgroup analysis for medication scope
Fig. 5 shows the results of the meta-analysis for each subgroup of medication scope and the total analysis. Subgroup analysis is performed on different medication groups because there have been common outcomes in related similar medication scope studies. The subgroup analysis showed a significant difference between CDSS and control groups for medication scopes namely as hypertension: (std diff in means = 0.187, 95% CI: 0.102 to 0.272); increasing blood potassium: (std diff in means = 0.036, 95% CI: 0.006 to 0.066), multiple medications: (std diff in means = 0.208, 95% CI:0.084 to 0.332), AIDs: (std diff in means = 0.241, 95% CI:0.038 to 0.444), kidney disorders: (std diff in means = 0.133, 95% CI:0.073 to 0.193), diabetes: (std diff in means = 0.381, 95% CI: 0.223 to 0.539), cardiac: (std diff in means = 0.073, 95% CI:0.035 to 0.111), mental diseases: (std diff in means = 0.062, 95% CI:0.010 to 0.114), medication prescription for patients: (std diff in means = 0.157, 95% CI:0.094 to 0.219), and pulmonary diseases: ( std diff in means = 0.079, 95% CI:0.014 to 0.144). However, there was no significant difference between the intervention and control group for digestive diseases: (std diff in means = 0.182, 95% CI: -0.072 to 0.437). Fig. 5 shows the forest plot for subgroup meta-analysis. However, we eliminated malaria and appendicitis diseases due to the decrease of heterogeneity among studies. We then described malaria and appendicitis diseases in narrative results. Also, Fig. 6 and Fig. 7 show the number of studies associated with each country and type of CDSS.
Categorization of Outcomes
Physician practice performance and patient outcomes are presented in Table 3 as primary outcomes are categorized based on the summary of the outcome concept and the impact of CDSS. Improvement or neutrality in outcomes is shown by plus or zero in Table 3. We categorized outcomes because similar outcomes may have different impacts on various diseases. For instance, the outcome “decrease prescribing” may have a positive effect on some diseases and no effect on other medication domains.
Table 3. Outcome classification for trials
Author (year of publication)
|
Primary Outcome
|
Outcome summarization
|
Outcome impact
|
Outcome Category
|
Beeler et al. )2014( [25]
|
Increasing the ratio of prescribing prophylaxis 6-24 hours after admission/transfer
|
Increasing prescribing
|
+
|
physician practice performance improved
|
Eckman et al. )2016( [26]
|
Reducing disagreement among physicians
|
Reducing disagreement among physicians
|
+
|
Du et al. )2018( [27]
|
Increasing secondary preventive prescriptions after 15 months in the intervention group
|
Increasing prescribing
|
+
|
Karlsson et al. (2018) [28]
|
Increasing the prescription of anticoagulation after 12 months
|
Increasing prescribing
|
+
|
Mazzaglia et al. (2016) [29]
|
Increasing prescription of anti-blocking drugs
|
Increasing prescribing
|
+
|
Patel et al. (2018) [31]
|
Increasing the number of anti-inflammatory/lipid-lowering drugs
|
Increasing prescribing
|
+
|
Perestelo-pérez et al. )2016( [37]
|
Increasing satisfaction of decision making
|
Increasing satisfaction of decision making
|
+
|
Sáenz et al. )2012( [38]
|
Increasing long-term blood sugar using between group differences
|
Increasing prescribing
|
+
|
Geurts et al. )2017( [41]
|
Increase in standard use of oral rehydration solution
|
Increasing prescribing
|
+
|
Petersen et al. (2017) [43]
|
Increase in drug prescription in patients with risk above 5 percent
|
Increasing prescribing
|
+
|
Bourgeois et al. (2010) [44]
|
Reduced antibiotic prescriptions in visits by using templates
|
Reducing prescribing
|
+
|
Juszczyk et al. (2017) [45]
|
Reducing unnecessary prescription of antibiotics
|
Reducing prescribing
|
+
|
Mcdermott et al. (2014) [46]
|
Increasing physicians self-efficacy
|
Increasing physicians efficacy
|
+
|
Mcginn et al. (2013) [47]
|
Reduced antibiotic prescription
|
Reducing prescribing
|
+
|
Avansino et al. (2012) [51]
|
Increase in following clinical guidelines for systematic prescriptions compared to case prescriptions
|
Increase in following clinical guidelines
|
+
|
Awdishu et al. )2016( [52]
|
Increase in not taking medication or changing dose of inadequate drugs
|
Reducing prescribing
|
+
|
Erler et al. )2012( [53]
|
Reduction in the amount of medication received in the intervention group in excess of the prescribed dose
|
Reducing prescribing
|
+
|
Cox et al. )2011( [54]
|
Increase in the number of prescriptions for initial drug use
|
Increasing prescribing
|
+
|
Strom et al. (2010) [56]
|
Increasing the percentage of appropriate alerts that have been responded to by physicians in the intervention group compared to the control group
|
Increasing the percentage of appropriate alerts
|
+
|
Beeler et al. (2019) [60]
|
Increase in the average monitoring time of potassium level
|
Increase in the average monitoring time of potassium level
|
+
|
Eschmann et al. )2015( [62]
|
Decrease in the reaction time to reminders in physicians for monitoring alerts for potassium level
|
Decrease in the reaction time to reminders
|
+
|
Curtain et al. )2011( [5]
|
Reduction in the approved percentage of inhibitor intervention proton pump which is registered by the pharmacologist
|
Reduction in the approved percentage of inhibitor intervention proton pump which is registered by the pharmacologist
|
+
|
Turchin et al. (2011) [6]
|
Increasing overall efficiency of system functionalities prior to admission
|
Increasing overall efficiency of system functionalities
|
0
|
Griffey et al.( 2012) [63]
|
Increasing the number of prescriptions by recommending the determined system dose
|
Increasing prescribing
|
+
|
Myers et al. (2011) [64]
|
Reducing the significant number of inappropriate abbreviations
|
Reducing prescribing
|
+
|
Van Stiphout et al. (2018) [65]
|
More efficient medical summary
|
More efficient medical summary
|
+
|
Akhu-zaheya et al. (2017) [32]
|
Increasing prescriptions in the short message group
|
Increasing prescribing
|
+
|
patient outcome improved
|
Khonsari et al. (2015) [33]
|
Increasing adherence to drug usage
|
Increasing adherence
|
+
|
Vervloet et al. (2014) [39]
|
Increasing adherence in the group receiving short messages
|
Increasing adherence
|
+
|
ervloet et al. (2012) [40]
|
Increasing the drug dosage in one hour during a six month period
|
Increasing prescribing
|
+
|
Elliott et al.( 2017) [58]
|
Reducing the average number of days re-hospitalized 60 days after discharge
|
Reducing the average number of days re-hospitalized
|
+
|
Bruxvoort et al. (2014) [59]
|
Knowledge of the physician in using Lumefantrine or thometer
|
increased Knowledge of the physician
|
+
|
Tamblyn et al. (2012) [67]
|
Reduction in dose of drugs after one year for antipsychotics
|
Reducing prescribing
|
+
|
Luitjes et al. (2018) [35]
|
For the control group, reducing the secondary outcome of infant morbidity after implementation
|
reducing morbidity
|
+
|
physician practice performance and patient outcome improved
|
Ackerman et al. (2013) [49]
|
Reducing excess prescription of antibiotics
|
Reducing prescribing
|
+
|
Pop-eleches et al. (2011) [50]
|
Reducing the number of treatment interruptions in both groups receiving weekly messages
|
effective in process of care
|
+
|
Christensen et al.)2010 ([34]
|
Reducing blood pressure after 12 months
|
Reducing morbidity
|
0
|
physician practice performance not improved
|
Nielsen et al. )2017( [30]
|
Increasing the time outcome in the scope of treatment
|
Increasing the time outcome
|
0
|
Buhse et al. )2018( [36]
|
Reduction in faulty knowledge causing risk
|
Reducing risk
|
0
|
Gill et al. )2011( [42]
|
Increase in receiving care on the basis of instructions for patients with low-dose aspirin use (25%)
|
Increase in receiving care
|
0
|
Muth et al. )2018( [55]
|
Ineffectiveness of drug prescriptions after 6 and 9 months
|
ineffectiveness in process of care
|
0
|
Strom et al. )2010( [57]
|
Reduction in the appropriate response of physicians to alerts during 17 months
|
Reduction in the appropriate response of physicians to alerts
|
0
|
Duke et al. )2013( [61]
|
Decrease in the conformity rate in normal risk patients for increased potassium
|
Decrease in the conformity rate in normal risk patients
|
0
|
Willis et al. (2013) [66]
|
Lack of difference in the rate of patient adherence to treatment, drug treatment significance, economic and clinical outcomes in three groups
|
no difference in process of care outcomes
|
+
|
patient outcome not improved
|
Mohammed et al. (2016) [48]
|
Inability to be effective in treatment success rate
|
ineffectiveness in process of care
|
0
|
Evaluation for publication bias
We conducted a funnel plot and Egger’s regression to evaluate the publication bias regarding the effect of CDSS on patient outcomes and physician performance [68, 69]. There was no significant difference with respect to publication bias (std diff in means = 0.054, CI 95%: 2.116 to 2.941, p=0.000001). Fig. 8 depicts that the X-axis shows std diff in mean in the funnel diagram, and the Y-axis reflects standard error.