The potential of artificial intelligence (AI) to reduce health care disparities and inequities is recognized, but it can also exacerbate these issues if not implemented in an equitable manner. This perspective identifies potential biases in each stage of the AI life cycle, including data collection, annotation, machine learning model development, evaluation, deployment, operationalization, monitoring, and feedback integration. To mitigate these biases, we suggest involving a diverse group of stakeholders, using human-centered AI principles. Human-centered AI can help ensure that AI systems are designed and used in a way that benefits patients and society, which can reduce health disparities and inequities. By recognizing and addressing biases at each stage of the AI life cycle, AI can achieve its potential in health care.

Collaborative information systems (CISs) are deployed within a diverse array of environments that manage sensitive information. Current security mechanisms detect insider threats, but they are ill-suited to monitor systems in which users function in dynamic teams. In this paper, we introduce the community anomaly detection system (CADS), an unsupervised learning framework to detect insider threats based on the access logs of collaborative environments. The framework is based on the observation that typical CIS users tend to form community structures based on the subjects accessed (e.g., patients' records viewed by healthcare providers). CADS consists of two components: 1) relational pattern extraction, which derives community structures and 2) anomaly prediction, which leverages a statistical model to determine when users have sufficiently deviated from communities. We further extend CADS into MetaCADS to account for the semantics of subjects (e.g., patients' diagnoses). To empirically evaluate the framework, we perform an assessment with three months of access logs from a real electronic health record (EHR) system in a large medical center. The results illustrate our models exhibit significant performance gains over state-of-the-art competitors. When the number of illicit users is low, MetaCADS is the best model, but as the number grows, commonly accessed semantics lead to hiding in a crowd, such that CADS is more prudent.

The integration of digital technologies into health care has significantly enhanced the efficiency and effectiveness of care coordination. Our perspective paper explores the digital information ecosystems in modern care coordination, focusing on the processes of information generation, updating, transmission, and exchange along a patient’s care pathway. We identify several challenges within this ecosystem, including interoperability issues, information silos, hard-to-map patient care journeys, increased workload on health care professionals, coordination and communication gaps, and compliance with privacy regulations. These challenges are often associated with inefficiencies and diminished care quality. We also examine how emerging artificial intelligence (AI) tools have the potential to enhance the management of patient information flow. Specifically, AI can boost interoperability across diverse health systems; optimize and monitor patient care pathways; improve information retrieval and care transitions; humanize health care by integrating patients’ desired outcomes and patient-reported outcome measures; and optimize clinical workflows, resource allocation, and digital tool usability and user experiences. By strategically leveraging AI, health care systems can establish a more robust and responsive digital information ecosystem, improving care coordination and patient outcomes. This perspective underscores the importance of continued research and investment in AI technologies in patient care pathways. We advocate for a thoughtful integration of AI into health care practices to fully realize its potential in revolutionizing care coordination.