Development and Usage Patterns of a Home-Grown Drug Information Tool.

Drug information tools help avoid medication errors, a common cause of avoidable harm in health care systems. We sought to describe the design, development process and architecture of an electronic drug information tool, as well as its overall use by health professionals. We developed a tool that can be accessed by all health professionals in a tertiary level university hospital. The functionalities of eDrugs are organized into two main parts: Drug Summary sheet, and Prescription Simulator. Most users accessed eDrugs to use the Drug summary sheet. Clinical information and antimicrobial drugs were the most accessed drug information and drug group. The analysis of log data provides insights into the information priorities of health professionals.

User Satisfaction with an AI System for Chest X-Ray Analysis Implemented in a Hospital's Emergency Setting.

The acceptance of artificial intelligence (AI) systems by health professionals is crucial to obtain a positive impact on the diagnosis pathway. We evaluated user satisfaction with an AI system for the automated detection of findings in chest x-rays, after five months of use at the Emergency Department. We collected quantitative and qualitative data to analyze the main aspects of user satisfaction, following the Technology Acceptance Model. We selected the intended users of the system as study participants: radiology residents and emergency physicians. We found that both groups of users shared a high satisfaction with the system's ease of use, while their perception of output quality (i.e., diagnostic performance) differed notably. The perceived usefulness of the application yielded positive evaluations, focusing on its utility to confirm that no findings were omitted, and also presenting distinct patterns across the two groups of users. Our results highlight the importance of clearly differentiating the intended users of AI applications in clinical workflows, to enable the design of specific modifications that better suit their particular needs. This study confirmed that measuring user acceptance and recognizing the perception that professionals have of the AI system after daily use can provide important insights for future implementations.

Health Information Systems Adaptation and Flexibility in Extreme Situations: The COVID Effect.

In 2020, a pandemic forced the entire world to adapt to a new scenario. The objective of this study was to know how Health Information Systems were adapted driven by the pandemic of COVID. 12 CIOS of healthcare organizations were interviewed and the interviews were classified according to the dimensions of a sociotechnical model: Infrastructure, Clinical Content, Human Computer Interface, People, Workflow and Communication, Organizational Characteristics and Internal Policies, Regulations, and Measurement and Monitoring. Adaptation to the Pandemic involved social, organizational and cultural rather than merely technical aspects in private organizations with mature and stable Health Information Systems.