The evaluation of the reviewed studies provided us with enlightening information with respect to the aims of the research, types of electronic prescribing systems, illness types, and patients. The findings also showed that in several diseases such as cardiovascular disease, high blood pressure, and diabetes, or cases such as simultaneous prescription of drugs, the effect of CDSS was evaluated. Findings from the analyzed studies are presented in the following tables in which * stands for p values and indicates a statistically significant difference. Also, the results of the quest are shown in Figure 1.
The number of studies based on multiple evaluation results and types of studies are shown in Figures 2 and 3, respectively. Figure 4 shows the meta-analysis results for each sub group of medication scope and the total 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 value = 0.0002, I2 = 78.492, Tau2: 0.004). Due to the high heterogeneity of results, sensitivity analysis was performed with excluded Bruxvoort, K. et al [50]; Ackerman, S. et al [35]; Avansino, J. et al [42] and khonsari, S. et al [24]studies. We omitted these trials because of the limited number of patients; they were also the only study of one medication field. The findings indicate that heterogeneity improved considerably after sensitivity analysis (Figure 5). (Q = 164.8, df = 41, p value = 0.0001, I2 = 75.136, Tau2: 0.003). After this change, the overall effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance based on the random effect model was determined to be (std diff in means = 0.84, 95% CI: 0.067 to 0.102) that showed significant difference.
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 from 6 to 24 hours after admission for clinicians allocated to venous thromboembolism reminder CDSS [17]. In another study, differences among physicians over the thromboprophylaxis treatment effect decreased with the help of CDSS providing treatment recommendation (p value = 0.02) [18]. In other studies, alert-based CDSSs have been effective in physician behavior and progressive treatment improvement in anti-inflammatory drugs and lipid-lowering drugs, which has also been statistically significant [19-21]. As stated in another study, by following medical recommendations, doctors in the intervention group were able to improve the prescribing level of secondary preventive medication with the help of a regular CDSS [22]. Also, in the other trials, the short message of the program in patient outcomes had a positive effect on patient adherence to medication, diet, and cardiovascular disease (p value<0.01) [23, 24]. Table 1 shows the result briefly.
Table 1. Data extracted for CDSS trials of cardiovascular diseases
Author/year
|
No. of hospitals/physicians/patients
|
Type of computer system
|
Outcome
|
P value
|
Eckman [18]
2014
|
15 /-/1493
|
CDSS providing treatment recommendation
|
Reducing disagreement among physicians
|
*0/02
|
Beeler [17]
2014
|
-/-/15736
|
Computerized system equipped with reminder to prevent intravenous thromboembolism
|
Increasing the ratio of prescribing prophylaxis 6-24 hours after admission/transfer
|
<0/0001 P value *
|
*0/03
|
Du [61]
2018
|
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 [19]
2016
|
43 /-/14134
|
CDSS equipped with alerts for patients with atrial fibrillation
|
Increasing the prescription of anticoagulation after 12 months
|
*0/01
|
Mazzaglia [20]
2014
|
-/197 /-
|
Alert-based CDSS for patients using cardiovascular drugs
|
Increasing prescription of anti-blocking drugs
|
*P value<0/001
|
Nielsen [22]
2014
|
-/-/191
|
CDSS to regulate the rate of warfarin use
|
Increasing the time outcome in the scope of treatment
|
0/67 Percent
|
Patel [21]
2011
|
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 [23]
2016
|
-/-/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 [24]
2014
|
-/-/62
|
Web-based software equipped with text reminders for patients with chronic coronary syndrome
|
Increasing adherence to drug usage
|
*P value>0/01
|
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 [25]. However, in another study, the patient outcome improved after the implementation of the CDSS [26]. Table 2 shows the result briefly.
Table 2. Data extracted for CDSS trials on hypertension
Author/year
|
Hospitals/physicians/patients
|
Type of computer system
|
Outcome
|
P value
|
Christensen [25]
2009
|
-/-/398
|
Reminder in patient admission and blood pressure control
|
Reducing blood pressure after 12 months
|
0/06
|
Luitjes [26]
2008,
2010
|
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
|
The effect of CDSS on diabetes
In some studies, the Real Time Medication Monitoring (RTMM) system, equipped with a short message reminder, improved precision of patients’ compliance and taking forgotten dosages [27-30]. In another study, HbA1c and group differences were greater in the intervention group using recommendation CDSS than in the control group [31]. The use of statins (p value = 0.03) and the problem areas in diabetes (PAID) (p value=0.01) improved in another study for intervention group that used CDSS [28]. Table 3 shows the result briefly.
Table 3. Data extracted for CDSS trials on diabetes
Author/year
|
Hospitals/physicians/patients
|
Type of computer system
|
Outcome
|
P value
|
Buhse [27]
2019
|
22/-/363
|
ISDM-P program composed of CDSS and sessions
|
Reduction in faulty knowledge causing risk
|
*P value <0/001
|
Perestelo-pérez [28]
2015
|
14/29/168
|
The CDSS selects statin with an estimate of cardiovascular disease risk
|
Increasing satisfaction of decision making
|
*0/009
|
Sáenz [31]
2012
|
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 [29]
2008
|
-/-/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 [30]
2008
|
-/-/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
|
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 [32-34]. The alert-based CDSS was also able to improve the quality of patient care to some degree in the other study [33]. Table 4 shows the result briefly.
Table 4. Data extracted for CDSS trials on digestive diseases
Author/year
|
Hospitals/physicians/patients
|
Type of computer system
|
Outcome
|
P value
|
Geurts [32]
2010
|
-/-/222
|
Recommendation decision support system
|
Increasing the standard use of oral rehydration solution
|
*P value <0/05
|
Gill [33]
2007
|
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 [34]
2013
|
General physicians
|
CDSS equipped with risk notification service
|
Increasing the drug prescription in patients with risk above 5 percent
|
*0/01
|
The effect of CDSS on pulmonary diseases
In some trials, the use of CDSS integrated with electronic health record, learning or prediction rules resulted in a decrease in the prescribing of antibiotics and macrolides; thereby, it helped minimize the inappropriate use of antibiotics (p value = 0.0005), decrease the resistance to antibiotics (p value = 0.04), and enhance primary care [35-39]. The patients had adhered to the reminder message of using their medication in another study; however, the messages did not affect therapy success [40]. Table 5 shows the result briefly.
Table 5. Data extracted for CDSS trials on pulmonary diseases
Author/year
|
Hospitals/physicians/patients
|
Type of computer system
|
Outcome
|
P value
|
Bourgeois [36]
2010
|
-/112/-
|
Chronic obstructive pulmonary disease pattern in electronic health records
|
Reduced antibiotic prescriptions in visits by using templates
|
*0/02
|
Jusuzik [37]
2015
|
-/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 [38]
2014
|
-/103/-
|
DSS and electronic learning
|
Increasing physicians self-efficacy
|
*0/02
|
Mcginn [39]
2010
|
-/-/984
|
A real time and unified CDSS during care combined with integrated clinical prediction rules
|
Reduced antibiotic prescription
|
*0/008
|
Mohammed [40]
2011
|
-/-/2207
|
Short message as a two-way reminder
|
Inability to be effective in treatment success rate
|
0/76
|
Ackerman [35]
2010
|
-/29/33
|
CDSS in Electronic Health Records
|
Reducing excess prescription of antibiotics
|
*0/003
|
The effect of CDSS on AIDS
In the reviewed study, it was shown that the reminder systems for short text messages had a positive effect on the treatment process for antivirus. The length of the messages also required more attention from the physicians, but had no significant effect on patient compliance rates (p value = 0.12) [41]. Table 6 shows the result briefly.
Table 6. Data extracted for CDSS trials on AIDS
Author/year
|
Hospitals/physicians/patients
|
Type of computer system
|
Outcome
|
P value
|
Pop-eleches [41]
2007
|
-/-/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
|
The effect of CDSS on appendicitis
This study showed that the system's systematically developed order set, which used clinical guidelines, increased system usability (p value=0.05) and reduced system-related problems related to unfamiliarity with the system (p value=0.05). This is a result of Computerized Provider Order Entry (CPOE) improved efficiency, quality and safety [42]. Table 7 shows the result briefly.
Table 7. Data extracted for CDSS trials on appendicitis
Author/year
|
Hospitals/physicians/patients
|
Type of computer system
|
Outcome
|
P value
|
Avansino [42]
2009
|
-/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
|
The effect of CDSS on kidney diseases
One study showed the positive effect of multipurpose intervention on creatinine value estimation and dose adjustment to reduce the insufficient dosage of primary care drugs [43]. In the other study, the appropriate prescription rate for kidney problems was rather low, contrary to the results of the former study. Furthermore, the effectiveness of the CDSS, equipped with physician guidelines, has been increased, especially for new versions [44]. Table 8 shows the result briefly.
Table 8. Data extracted for CDSS trials on kidney diseases
Author/year
|
Hospitals/physicians/patients
|
Type of computer system
|
Outcome
|
P value
|
Awdishu [44]
2012
|
-/514/1278
|
DSS Warning
|
An increase in not taking medication or changing dose of inadequate drugs
|
*P value<0/0001
|
Erler [43]
2007
|
-/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
|
The effect of CDSS on taking multiple medications
In one study, 194 hard-alerted CDSSs resulted in delayed drug treatment for four patients requiring immediate treatment, suggesting that adverse events of these systems need to be evaluated and monitored [45]. 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 [46]. Also in another study, the average number of readmission days for each patient and the combination of re-hospitalization and emergency ward visits in the 30 days after hospital discharge was not different in the intervention group using Recommendation CDSS with control groups [47]. In some trials, there was no discrepancy between the outcomes of the dosage rate and the Modified Medication Appropriateness Index (MMAI), improper medication prescribing (p value = 0.48), the Medication Regimen Complexity Index, and the mean pain outcome difference after 6 months (p value = 0.13) and 9 months (p value = 0.78) between the intervention group using alert or reminder CDSS and the control group [48, 49]. Table 9 shows the result briefly.
Table 9. Data extracted for CDSS trials on taking multiple medications
Author/year
|
Hospitals/physicians/patients
|
Type of computer system
|
Outcome
|
P value
|
Cox [46]
2009
|
-/-/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 [48]
2017
|
-/71/465
|
Reminder-based CDSS
|
Ineffectiveness of drug prescriptions after 6 and 9 months
|
0/31, 0/18
|
Strom [45]
2006
|
-/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 [49]
2007
|
-/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 [47]
2016
|
-/-/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
|
The effect of CDSS on Malaria
The use of text messages in the study did not affect the behavior of patients in completing the course of medication for the full duration of treatment. However, if the side effects were low (p value = 0.02), it had effects on the continuous use of the medication. In addition, text messages had an effect on the physicians’ knowledge in using medication with fatty foods (p value<0.0001) [50]. Table 10 shows the result briefly.
Table 10. Data extracted for CDSS trials on Malaria
Author/year
|
Population
|
Type of computer system
|
Outcome
|
P value
|
Bruxvoort [50]
2012
|
82/-/-
|
Text message reminders for Malaria treatment
|
Physicians’ knowledge in using Lumefantrine orthometer
|
*P value <0/0001
|
The effect of CDSS on increasing the level of blood potassium
In one study, there is no statistical difference in terms of following alerts and patient compliance rate between the control and intervention groups. The doctors’ compliance rate improved at the medium potassium level from 3 to 3.9 (mili-equivalents/liter) (p value<0.01) [51]. Due to the rapid response of the physicians to program reminders and alerts for high potassium levels in the intervention group, the positive effect of the system on physician behavior was evident in another study (p value = 0.01) and a high level of potassium (p value = 0.05). Thus, patient safety could be increased [52]. In another study, the time lapse in hyperkalemia monitoring (p value = 0.20) and the incidence rate of hyperkalemia (p value = 0.22) did not differ significantly even with the use of three different kinds of reminder and alert-based CDSSs [53]. Table 11 shows the result briefly.
Table 11. Data extracted for CDSS trials on increasing blood potassium
Author/year
|
Hospitals/physicians/patients
|
Type of computer system
|
Outcome
|
P value
|
Beeler [53]
2014
|
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 [51]
2011
|
-/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 [52]
2014
|
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
|
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 [4, 54]. Also, in the other studies, the overall utilization of system functionalities, system utilization between two time laps (p value<0.0001), number of users (p value<0.0001), and physician compliance regarding drug recommendations given by the CDSS improved drug prescriptions, which eventually resulted in reduced side effects (p value = 0.02) and harm to patients due to the lower number of errors regarding the alert-based CDSS [5, 55]. There was no difference in drug prescription among physicians in one study (p value=0.14); However, the percentage of skill questions answered for the intervention group, equipped with training CDSS (p value = 0.01) improved [56]. In another study, alert-based CDSSs have been effective in identifying evidence-based pharmacotherapies (EBP). Meanwhile, the compliance with treatment by health care managers have had no effect on the outcome of patients [57]. Table 12 shows the result briefly.
Table 12. Data extracted for CDSS trials on medication prescription for the patient
Author/year
|
Hospitals/physicians/patients
|
Type of computer system
|
Outcome
|
P value
|
Curtain [4]
2009
|
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 [5]
2008
|
-/3703/-
|
Hard alert systems to facilitate medication services
|
Increasing overall efficiency of system functionalities prior to admission
|
*P value <0/0001
|
Griffey [55]
2006
|
-/-/1407
|
CDSS for recommending drug dosage
|
Increasing the number of prescriptions by recommending the determined system dose
|
*P value <0/0001
|
Myers [54]
2006
|
-/59/-
|
Computerized alerts for manual or automatic correction of medical abbreviation
|
Reducing the significant number of inappropriate abbreviations
|
*0/02
|
Van Stiphout [56]
2014
|
2/115/1094
|
CDSS integrated with training session
|
More efficient medical summary
|
*0/03
|
Willis [57]
2009
|
-/-/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
|
|
The effect of CDSS on mental disorders
DSS alerts resulted in reduced risk of injury and reduced dose of antipsychotics and anticoagulants (p value = 0.03) from the beginning of the study up to year 1. Therefore, the CDSS reduced the risk of injury (p value = 0.02) [58]. Table 13 shows the result briefly.
Table 13: Data extracted for CDSS trials on mental disorders
Author/year
|
Hospitals/physicians/patients
|
Type of computer system
|
Outcome
|
P value
|
Tamblyn [58]
2008 to 2010
|
-/81/5628
|
DSS equipped with three types of alerts
|
Reduction in dose of drugs after one year for antipsychotics
|
*0/02
|
Subgroup analysis
The subgroup analysis showed a significant difference between CDSS and control groups for medication scopes such 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 disease: (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); pulmonary disease: ( std diff in means = 0.079, 95% CI:0.014 to 0.144). Also, there was no significant difference between the intervention and control group for digestive disease: (std diff in means = 0.182, 95% CI: -0.072 to 0.437). Figure 5 shows the forest plot for subgroup meta-analysis. However, Malaria and appendicitis diseases are eliminated due to the decrease of heterogeneity between studies, they have been described in narrative results.
Evaluation for publication bias
Funnel plot and Egger’s regression were performed to evaluate the publication bias regarding the effect of CDSS on patient outcomes and physician performance [59, 60]. There was no significant difference with respect to publication bias (std diff in means = 0.054, CI 95%: 2.116 to 2.941, p value:0.000001). Figure 6 shows that the X-axis shows std diff in mean in the funnel diagram, and the Y-axis reflects standard error.