Hachesu PR, et al. Healthc Inform Res. 2013 Jun;19(2):121-129. http://dx.doi.org/10.4258/hir.2013.19.2.121

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 I²were 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.

Internet of Things (IoT), which provides smart services and remote monitoring across healthcare systems according to a set of interconnected networks and devices, is a revolutionary technology in this domain. Due to its nature to sensitive and confidential information of patients, ensuring security is a critical issue in the development of IoT-based healthcare system. Our purpose was to identify the features and concepts associated with security requirements of IoT in healthcare system. A survey study on security requirements of IoT in healthcare system was conducted. Four digital databases (Web of Science, Scopus, PubMed and IEEE) were searched from 2005 to September 2019. Moreover, we followed international standards and accredited guidelines containing security requirements in cyber space. We identified two main groups of security requirements including cyber security and cyber resiliency. Cyber security requirements are divided into two parts: CIA Triad (three features) and non-CIA (seven features). Six major features for cyber resiliency requirements including reliability, safety, maintainability, survivability, performability and information security (cover CIA triad such as availability, confidentiality and integrity) were identified. Both conventional (cyber security) and novel (cyber resiliency) requirements should be taken into consideration in order to achieve the trustworthiness level in IoT-based healthcare system.

Context: Medication non-adherence is a commonly observed problem in the self-administration of treatment, regardless of the disease type. Text messaging reminders, as electronic reminders, provide an opportunity to improve medication adherence. In this study, we aimed to provide evidence addressing the question of whether text message reminders were effective in improving patients’ adherence to medication. Evidence Acquisition: We carried out a systematic literature search, using the five electronic bibliographic databases: PubMed, Embase, PsycINFO, CINAHL, and the Cochrane central register of controlled trials. Studies were included on the basis of whether they examined the benefits and effects of short-message service (SMS) interventions on medication adherence. Results: The results of this systematic review indicated that text messaging interventions have improved patients’ medication adherence rate (85%, 29.34). Included in the review, those who had problems with adherence, or those whom text messaging was most helpful had HIV, asthma, diabetes, schizophrenia and heart disease (73.5%). The period of intervention varied from 1 week to 14 months. The most common study design was randomized controlled trials (RCTs) (66%) carried out in the developed countries. Conclusions: This study demonstrated the potential of mobile phone text messaging for medication non-adherence problem solving. Keywords: Cellular Phones; Text Messaging; Medication Adherence; Chronic Disease; Review