Table 2.
Data extracted for CDSS trials
| References | Disease type | No. of hospitals/physicians/patients | Type of computer system | Outcome | p value |
|---|---|---|---|---|---|
| Beeler et al. [25] | Cardiovascular | –/–/15,736 | Computerized system equipped with reminder to prevent intravenous thromboembolism | Increasing the ratio of prescribing prophylaxis 6–24 h after admission/transfer | *p value < 0/0001* |
| *0/03 | |||||
| Eckman et al. [26] | Cardiovascular | 15/–/1493 | CDSS providing treatment recommendation | Reducing disagreement among physicians | *0/02 |
| Du et al. [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. [28] | Cardiovascular | 43/–/14,134 | CDSS equipped with alerts for patients with atrial fibrillation | Increasing the prescription of anticoagulation after 12 months | *0/01 |
| Mazzaglia et al. [29] | Cardiovascular | –/197/– | Alert-based CDSS for patients using cardiovascular drugs | Increasing prescription of anti-blocking drugs | *p value < 0/001 |
| Nielsen et al. [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. [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. [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. [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. [34] | Hypertension | –/–/398 | Reminder in patient admission and blood pressure control | Reducing blood pressure after 12 months | 0/06 |
| Luitjes et al. [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 | *p value < 0/0001 |
| Buhse et al. [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. [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. [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. [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. [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. [41] | Digestive diseases | –/–/222 | Recommendation decision support system | Increasing the standard use of oral rehydration solution | *p value < 0/05 |
| Gill et al. [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. [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. [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. [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. [46] | Pulmonary diseases | –/103/– | DSS and electronic learning | Increasing physicians self-efficacy | *0/02 |
| Mcginn et al. [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. [48] | Pulmonary diseases | –/–/2207 | Short message as a two-way reminder | Inability to be effective in treatment success rate | 0/76 |
| Ackerman et al. [49] | Pulmonary diseases | –/29/33 | CDSS in Electronic Health Records | Reducing excess prescription of antibiotics | *0/003 |
| Pop-eleches et al. [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. [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. [52] | Kidney diseases | –/514/1278 | DSS WarninDSS Warnin | An increase in not taking medication or changing dose of inadequate drugs | *p value < 0/0001 |
| Erler et al. [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. [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 < 0/00,001 | ||||
| Muth et al. [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. [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 versus 13/5 |
| Strom et al. [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. [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. [59] | Malaria | 82/–/– | Text message reminders for Malaria treatment | Physicians’ knowledge in using Lumefantrine orthometer | *p value < 0/0001 |
| Beeler et al. [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. [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. [62] | Increasing blood potassium | 15/–/37,000 | 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. [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. [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. [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. [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. [65] | Medication prescription for the patient | 2/115/1094 | CDSS integrated with training session | More efficient medical summary | *0/03 |
| Willis et al. [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. [67] | Mental disorders | –/81/5628 | DSS equipped with three types of alerts | Reduction in dose of drugs after one year for antipsychotics | *0/02 |