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Electronic pharmacovigilance reporting systems are being implemented in many developing countries in an effort to improve reporting rates. This study sought to establish the factors that acted as barriers to the success of an electronic pharmacovigilance reporting system in Kenya 3 years after its implementation.

To reduce the risk of wrong-patient errors, safety experts recommend allowing only one patient chart to be open at a time. Due to the lack of empirical evidence, the number of allowable open charts is often based on anecdotal evidence or institutional preference, and hence varies across institutions. Using an interrupted time series analysis of intercepted wrong-patient medication orders in an emergency department during 2010-2016 (83.6 intercepted wrong-patient events per [...]

(1) To describe the usage of medication data from the Health Information Exchange (HIE) at the health care system level in the province of Quebec; (2) To assess the accuracy of the medication list obtained from the HIE.

Ensuring the ability to exchange patient information among disparate electronic health records systems is a top priority and a domain of substantial public investment across countries. However, we know little about the extent to which current capabilities meet the needs of frontline clinicians.

Research on the implementation of health information exchange (HIE) organizations has identified both positive and negative effects of laws relating to governance, incentives, mandates, sustainability, stakeholder participation, patient engagement, privacy, confidentiality, and security. We fill a substantial research gap by describing whether comprehensive state and territorial HIE legal frameworks address identified legal facilitators and barriers.

To describe the development, as part of the European Union MOSAIC (Models and Simulation Techniques for Discovering Diabetes Influence Factors) project, of a dashboard-based system for the management of type 2 diabetes and assess its impact on clinical practice.

Investigate the accuracy of 2 different medication reconciliation tools integrated into electronic health record systems (EHRs) using a cognitively demanding scenario and complex medication history.

Incorrect imaging protocol selection can lead to important clinical findings being missed, contributing to both wasted health care resources and patient harm. We present a machine learning method for analyzing the unstructured text of clinical indications and patient demographics from magnetic resonance imaging (MRI) orders to automatically protocol MRI procedures at the sequence level. We compared 3 machine learning models - support vector machine, gradient boosting machine, and random forest [...]