OBJECTIVES: We report on findings from a meeting convened by the American College of Medical Informatics (ACMI) to characterize aspects of the patient experience that could be improved using informatics. MATERIALS AND METHODS: The American College of Medical Informatics fellows were invited to share their experiences as patients and suggest informatics approaches that may improve the patient experience. RESULTS: We identified 4 themes: (1) getting the right care, (2) data sharing and data interoperability, (3) guiding low-cost evaluations, and (4) predictive analytics. DISCUSSION: Despite widespread adoption of health IT, patient experiences remain far from optimal. CONCLUSION: The American College of Medical Informatics fellows identified informatics approaches, applications, and research areas that have the potential to improve patient experiences with health care systems.

High-quality real-world datasets are essential for advancing data-driven approaches in type 1 diabetes (T1D) management, including personalized therapy design, digital twin systems, and glucose prediction models. However, progress in this area has been limited by the scarcity of publicly available datasets that offer detailed and comprehensive patient data. To address this gap, we present$A Z T 1 D$, a dataset containing data collected from 25 individuals with T1D on automated insulin delivery (AID) systems. AZT1D includes continuous glucose monitoring (CGM) data, insulin pump and insulin administration data, carbohydrate intake, and device mode (regular, sleep, and exercise) obtained over 6-8 weeks for each patient. Notably, the dataset provides granular details on bolus insulin delivery (i.e., total dose, bolus type, correction-specific amounts) features that are rarely found in existing datasets. By offering rich, naturalistic data, AZT1D supports a wide range of artificial intelligence and machine learning applications aimed at improving clinical decision-making and individualized care in T1D.

The objective is to report on the design and evaluation of the inaugural Women in AMIA Leadership Program. A year-long leadership curriculum was developed. Survey responses were summarized with descriptive statistics and quotes selected. Twenty-four scholars participated in the program. There was a significant increase in perceived achievement of learning objectives after the program (P < .0001). The largest improvement was in leadership confidence and presence in work interactions (modal answer Neutral in presurvey from 21 responses rose to Agree in postsurvey from 24 responses). Most (92% of 13) scholars clarified leadership vision and goals and (83% of 18) would be Very Likely to recommend the program to others. The goals of the program-developing women's leader identity, increasing networks, and accumulating experience for future programs-were achieved. The second leadership program is on its way in the United States and Australia. This study may benefit organizations seeking to develop leadership programs for women in informatics and digital health.

A Tribute to

We review recently proposed notions of healthcare patterns for collaborative work and show how these can be cast in terms of composition of processes. The approach uses a purely diagrammatic language to drive a logic-based verification engine, resulting in fully-verified workflows that capture the information flow in these patterns.

The objective of this paper is to try to fill the gap between: argumentation, electronic institutions and protocols by using a combination of automated synthesis and model checking methods. More precisely, this paper proposes a means of moving rapidly from argument specification to protocol implementation, using an extension of the Argument Interchange Format as the specification language and the Lightweight Coordination Calculus as an implementation language.