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.

Although more than 10 years have passed since the implementation of FPP in Iran, and despite its positive effects on health, there are still challenges in implementing this policy, which makes it difficult to achieve its objectives. Health decision- and policy-makers in Iran should address these challenges and use all available capacities to face them.

Background: Todays, smartphone technologies and applications are used in the medical and nursing fields. Medical and nursing students are among the groups in which the use of these tools is observed. Objectives: To investigate their views on the barriers and facilitators of the use of these technologies. Methods: Four hundred people (200 people from each group) were invited randomly. A questionnaire was used to collect data. To collect data, hospitals were referred and the questionnaires were provided to students and after completion, they were collected. Data analysis was performed using SPSS software and descriptive and inferential statistics. Results: The most important barriers from the students’ view included internet problems in the university or hospital, lack of technical support for this technology in the hospital or university, the quality of existing applications, lack of appropriate Apps in the local language, non-introduction of appropriate Apps and not knowing the right Apps. The most important facilitators were the appropriate support services in the university or hospital, placing the appropriate Apps on the department or university website, designing native Apps with the participation of experts, and introducing the appropriate Apps by professors or universities. Conclusion: The use of Apps is increasing among medical and nursing students. In this regard, attention to the existing barriers and their elimination and strengthening the facilitators, and providing the necessary technical and educational infrastructure to facilitate the useful applications should be considered by decision-makers.

BackgroundDiabetes mellitus is one of the most prevalent diseases worldwide. Diabetes is a chronic disease associated with micro- and macro-vascular complications and deterioration in general health status. Therefore, the aim of this study was to estimate general health status among Iranian diabetic patients through a systematic review and meta-analysis of study utilizing the Short-Form-36 questionnaire.MethodsSearching the EMBASE, PubMed, ISI/Web of Sciences (WOS), MEDLINE via Ovid, PsycoINFO, as well as Iranian databases (MagIran, Iranmedex, and SID) from January 2000 to December 2017. The methodological quality of the studies was evaluated using the “A Cochrane Risk of Bias Assessment Tool: for Non-Randomized Studies of Interventions” (ACROBAT-NRSI). Random-effect model was used and the means were reported with their 95% confidence interval (CI). To evaluate the heterogeneity between studies, I2 test was used. Egger’s regression test was used to assess the publication bias.ResultsFourteen studies were retained in the final analysis. The mean general health status using SF-36 in diabetic patients of Iran was 51.9 (95% CI: 48.64 to 53.54). The mean physical component summary was 52.92 [95% CI: 49.46–56.38], while the mean mental component summary was 51.02 [95% CI: 46.87–55.16].ConclusionThe findings of this study showed that general health status in Iranian diabetic patients is low. Health policymakers should work to improve the health status in these patients and take appropriate interventions.Electronic supplementary materialThe online version of this article (10.1186/s12902-018-0262-2) contains supplementary material, which is available to authorized users.

Background: Patient safety classifications are used to collect, classify and analyze patient safety data. Objective: This review was conducted to identify and compare the subject and coverage of existing patient safety classifications for Health Information Technology (HIT) and medical devices in which HIT may cover. Methods: All studies in patient safety that developed or extended any type of classification in HIT and medical devices were included. We identified and classified the covered concepts in these systems. Results: We identified 7 articles that met all of the inclusion criteria, resulting in 6 classifications. The most common patient safety subjects included adverse events and medication errors. Incident types and contributing factors/hazards were the most frequently covered concepts. Conclusions: Patient safety classifications in HIT cover more concepts and classes than medical device classifications. It is therefore recommended to improve existing classification systems in terms of covered concepts and classes.

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Background The clinical decision support systems(CDSSs) for prescription medications is one of the technologies aimed at improving physician practice behavior and patient outcomes by reducing drug prescription errors. This study was conducted to investigate the effect of various CDSSs on physician practice behavior and patient outcomes.Methods This systematic review was conducted by searching in PubMed, EMBASE, Web of Science, Scopus and Cochrane Library from January 2005 to November 2019. Two researchers independently evaluated the studies. Any discrepancies over the eligibility of the studies between the two researchers were then resolved by consulting a third researcher. Finally, data were extracted from the articles; however, we could not able to conduct meta-analysis due to the heterogeneity of the studies and the narrative form of the findings.Results Based on the inclusion criteria, 46 studies were considered eligible for the analysis in this review. The CDSS for prescription medications had been used for various diseases, namely cardiovascular diseases, hypertension, diabetes, gastrointestinal and respiratory diseases, AIDS, appendicitis, kidney disease, malaria, high blood potassium, mental disease. Meanwhile, other cases such as the concurrent prescription of multiple drugs for the patient and its effects on the above-mentioned outcomes were evaluated. The analysis shows that in some cases the application of CDSS provides positive effects on patient outcomes and physician practice behaviors. No significant difference was observed in comparison between some other cases and conventional methods. We think that this could be due to the disease type, the quantity and type of CDSS requirements that influence the comparison.Conclusions Our findings suggest that the positive effects of the CDSS are due to factors such as user-friendliness, compliance with clinical guidelines, patient and doctor cooperation, integration of electronic health records, CDSS and pharmaceutical systems, consideration of the views of physicians in assessing the importance of CDSS alerts and their real-time alerts in the prescription.Registration number on PROSPERO CRD42018079936.

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.