Evaluating the success of Iran Electronic Health Record System (SEPAS) based on the DeLone and McLean model: a cross-sectional descriptive study.

BACKGROUND: Quality dimensions are the most important criteria for predicting the success of an information system. The current study aims to evaluate the success of the Iran Electronic Health Record System (SEPAS) based on the DeLone and McLean model for information system success. METHOD: This nationwide cross-sectional study was conducted in 2021. Participants were 468 health information management personnel who had working experience with SEPAS. Data were collected using a questionnaire based on the DeLone and McLean model. The validity and reliability of the questionnaire were confirmed. Data were analyzed using SPSS 22 through descriptive and analytic analysis including t-test and ANOVA. RESULTS: Most participants were female (70.9%) and almost half of the participants mean age was between 30 and 40 years old (49.6%). The total mean of SEPAS success was 3.42 ± 0.53. According to the participants' perspectives "system quality" was the most influencing factor on SEPAS success. The least influencing factor was SEPAS "benefits". There was a significant relationship between the mean score of SEPAS success and age (p value = 0.001), Education level (p value = 0.01), and Work experience (p value < 0.001). CONCLUSION: The total mean of system success was not acceptable. SEPAS has not been much successful in providing net benefits like provision of electronic services which locate patients in the center and improve the delivery of care to them. It sounds that SEPAS is not stable enough that means crashes sometimes. Hence, considering the required infrastructures for quick response and stability is more critical, especially when healthcare providers are supposed to use the SEPAS.

Supervised Machine Learning Approach to COVID-19 Detection Based on Clinical Data.

Background: The new coronavirus has been spreading since the beginning of 2020, and many efforts have been made to develop vaccines to help patients recover. It is now clear that the world needs a rapid solution to curb the spread of COVID-19 worldwide with non-clinical approaches such as artificial intelligence techniques. These approaches can be effective in reducing the burden on the health care system to provide the best possible way to diagnose the COVID-19 epidemic. This study was conducted to use Machine Learning (ML) algorithms for the early detection of COVID-19 in patients. Methods: This retrospective study used data from hospitals affiliated with Shiraz University of Medical Sciences in Iran. This dataset was collected in the period March to October 2020 andcontained 10055 cases with 63 features. We selected and compared six algorithms: C4.5, support vector machine (SVM), Naive Bayes, logistic Regression (LR), Random Forest, and K-Nearest Neighbor algorithm using Rapid Miner software. The performance of algorithms was measured using evaluation metrics, such as precision, recall, accuracy, and f-measure. Results: The results of the study show that among the various used classification methods in the diagnosis of coronavirus, SVM (93.41% accuracy) and C4.5 (91.87% accuracy) achieved the highest performance. According to the C4.5 decision tree, "contact with a person who has COVID-19" was considered the most important diagnostic criterion based on the Gini index. Conclusion: We found that ML approaches enable a reasonable level of accuracy in the diagnosis of COVID-19.

Laboratory test ordering in inpatient hospitals: a systematic review on the effects and features of clinical decision support systems.

BACKGROUND: Studies have revealed inappropriate laboratory testing as a source of waste. This review aimed at evaluating the effects and features of CDSSs on physicians' appropriate laboratory test ordering in inpatient hospitals. METHOD: Medline through PubMed, SCOPUS, Web of Science, and Cochrane were queried without any time period restriction. Studies using CDSSs as an intervention to improve laboratory test ordering as the primary aim were included. The study populations in the included studies were laboratory tests, physicians ordering laboratory tests, or the patients for whom laboratory tests were ordered. The included papers were evaluated for their outcomes related to the effect of CDSSs which were categorized based on the outcomes related to tests, physician, and patients. The primary outcome measures were the number and cost of the ordered laboratory tests. The instrument from The National Heart Lung and Blood Institute (NIH) was used to assess the quality of the included studies. Moreover, we applied a checklist for assessing the quality and features of the CDSSs presented in the included studies. A narrative synthesis was used to describe and compare the designs and the results of included studies. RESULT: Sixteen studies met the inclusion criteria. Most studies were conducted based on a quasi-experimental design. The results showed improvement in laboratory test-related outcomes (e.g. proportion and cost of tests) and also physician-related outcomes (e.g. guideline adherence and orders cancellation). Patient-related outcomes (e.g. length of stay and mortality rate) were not well investigated in the included studies. In addition, the evidence about applying CDSS as a decision aid for interpreting laboratory results was rare. CONCLUSION: CDSSs increase appropriate test ordering in hospitals through eliminating redundant test orders and enhancing evidence-based practice. Appropriate testing and cost saving were both affected by the CDSSs. However, the evidence is limited about the effects of laboratory test CDSSs on patient-related outcomes.

Moving toward community based telehealth services using mhealth for hypertensive patients.

BACKGROUND: Although self-care can control and prevent complications in hypertensive patients, self-care adherence is relatively low among these patients. Community-based telehealth services through mhealth can be an effective solution. OBJECTIVE: This study aimed to evaluate the effect and acceptance of an mhealth application as a community-based telehealth intervention on self-care behavior adherence. METHOD: This clinical trial included sixty hypertensive patients and their matched controls from two heart clinics affiliated to Shiraz University of Medical Sciences (SUMS). Self-care behaviors were assessed using Hill-Bone questionnaire before and after the intervention. Acceptability was evaluated in the intervention group at the end of the study period. The data were analyzed via SPSS 18 software using descriptive and inferential statistics. RESULT: The results showed a significant difference between the intervention and control groups regarding the mean score of self-care behaviors (4.13 ± 0.23 versus 3.18 ± 0.27, p < .001). Additionally, a significant difference was observed between the two groups concerning the mean scores of the two subscales of self-care behaviors, including "medication taking" and "proper diet". However, no significant difference was observed between the two groups regarding the mean score of "appointment keeping" (p = .075). Overall, the intervention group participants were satisfied (4.27 ± 0.34) with this approach for managing hypertension. CONCLUSION: Community-based telehealth services through mhealth had the potential to improve self-care behaviors in hypertensive patients and seemed to be accepted by the patients in the intervention group.

Usability Assessment of an Electronic Medical Record-Embedded Clinical Decision Support System for Arterial Blood Gas Interpretation.

Since usability is considered a significant success factor for Clinical decision support systems (CDSSs), this study seeks to assess the usability of an electronic medical records-embedded CDSS for arterial blood gas (ABG) interpretation and ordering. The current study was conducted in the general ICU of a teaching hospital, using the System Usability Scale (SUS) and interviews with all anesthesiology residents and intensive care fellows in two rounds of CDSS usability testing. The feedback from the participants was discussed with the research team across a series of meetings, and the second version of CDSS was designed and tailored to participants' feedbacks. Subsequently, the CDSS usability score increased from 67.22±4.58 to 80.00±4.84 (P-value<0.001) through participatory, iterative design and the users' usability testing feedbacks.

Presenting a Model to Evaluate Factors Affecting Outsourcing of Health Information Technology Services.

INTRODUCTION: Outsourcing of health information technology services (OHITS) is an important process for healthcare organizations due to the lack of expert staff to respond rapid advance in IT and the security of patient's information. This study aimed at presenting a model to evaluate factors affecting OHITS. METHOD: This is a descriptive-analytic study, conducted in 2017. Participants were experts of IT and accounting field. This research was performed in four general steps: identifying the factors affecting OHITS through literature review; determining suitable indicators by Delphi technique; prioritizing the factors using Analytical Hierarchical Process (AHP), measuring the accuracy of research hypotheses by Partial Least Square (PLS) and calculating the Goodness Of Fit (GOF) criteria for the model. FINDINGS: the most and the least important factors affecting OHITS were "motivation" and "selection of a provider" respectively. GOF criteria was 0.697, suggesting powerful model fitting. CONCLUSION: Using the model presented in this research, the healthcare managers and chief officers of IT will be able to decide consciously about outsourcing projects, and also manage the project better.