Machine learning models to detect and predict patient safety events using electronic health records: A systematic review.

Introduction: Identifying patient safety events using electronic health records (EHRs) and automated machine learning-based detection methods can help improve the efficiency and quality of healthcare service provision.

Objective: This study aimed to systematically review machine learning-based methods and techniques, as well as their results for patient safety event management using EHRs.

Methods: We reviewed the studies that focused on machine learning techniques, including automatic prediction and detection of patient safety events and medical errors through EHR analysis to manage patient safety events.

Health information technology in ambulatory care in a developing country.

Background: Physicians need to apply new technologies in ambulatory care. At present, with regard to the extended use of information technology in other departments in Iran it has yet to be considerably developed by physicians and clinical technicians in the health department.

Objective: To determine the rate of use of health information technology in the clinics of specialist- and subspecialist physicians in Semnan city, Iran.

Pharmacy Information Systems in Teaching Hospitals: A Multi-dimensional Evaluation Study.

Objectives: In hospitals, the pharmacy information system (PIS) is usually a sub-system of the hospital information system (HIS). The PIS supports the distribution and management of drugs, shows drug and medical device inventory, and facilitates preparing needed reports. In this study, pharmacy information systems implemented in general teaching hospitals affiliated to medical universities in Tehran (Iran) were evaluated using a multi-dimensional tool.