RESUMO
Artificial intelligence and machine learning have numerous applications in pathology and laboratory medicine. The release of ChatGPT prompted speculation regarding the potentially transformative role of large-language models (LLMs) in academic pathology, laboratory medicine, and pathology education. Because of the potential to improve LLMs over the upcoming years, pathology and laboratory medicine clinicians are encouraged to embrace this technology, identify pathways by which LLMs may support our missions in education, clinical practice, and research, participate in the refinement of AI modalities, and design user-friendly interfaces that integrate these tools into our most important workflows. Challenges regarding the use of LLMs, which have already received considerable attention in a general sense, are also reviewed herein within the context of the pathology field and are important to consider as LLM applications are identified and operationalized.
RESUMO
Direct Secure Messaging (DSM) is a sender-initiated communication technology for exchanging patient-specific information among clinicians and disparate healthcare organizations. As DSM adoption increases it becomes more difficult for clinicians and staff to manage the volume and variety of external data received. This can lead to information hazards that can produce cognitive overload and decrease the ability of clinicians to process patient data when reviewing multiple sources. While DSM is one of many options for electronically exchanging health information, we have found that poor user awareness of DSM features and variable EHR capabilities for sending, receiving, and managing messages and their contents demonstrate that additional work is needed to achieve DSM's potential as a low-barrier, ubiquitous option for clinical interoperability. This paper reviews these problems from end-user perspective and offers best-practices for both senders and recipients of DSM.