DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing

The meaningful use of electronic health records (EHR) continues to progress in the digital era with clinical decision support systems augmented by artificial intelligence. A priority in improving provider experience is to overcome information overload and reduce the cognitive burden so fewer medical errors and cognitive biases are introduced during patient care. One major type of medical error is diagnostic error due to systematic or predictable errors in judgment that rely on heuristics. The potential for clinical natural language processing (cNLP) to model diagnostic reasoning in humans with forward reasoning from data to diagnosis and potentially reduce the cognitive burden and medical error has not been investigated. Existing tasks to advance the science in cNLP have largely focused on information extraction and named entity recognition through classification tasks. We introduce a novel suite of tasks coined as Diagnostic Reasoning Benchmarks, DR.BENCH, as a new benchmark for developing and evaluating cNLP models with clinical diagnostic reasoning ability. The suite includes six tasks from ten publicly available datasets addressing clinical text understanding, medical knowledge reasoning, and diagnosis generation. DR.BENCH is the first clinical suite of tasks designed to be a natural language generation framework to evaluate pre-trained language models. Experiments with state-of-the-art pre-trained generative language models using large general domain models and models that were continually trained on a medical corpus demonstrate opportunities for improvement when evaluated in DR. BENCH. We share DR. BENCH as a publicly available GitLab repository with a systematic approach to load and evaluate models for the cNLP community.Comment: Under revie

The Role of the Physicians\u27 Assistant in Trinidad and Tobago\u27s Healthcare System

The profession of physicians\u27 assistant was introduced in the 1960s to assist with physician shortages in the United States of America. Since then, some countries have introduced this profession to fill the gaps that exist in the physician shortages problem in their health care system. Yet, in many countries like Trinidad and Tobago, this role remains absent from the health care system. The objective of this study was to assess how professionalization supports the introduction of the physicians\u27 assistant role in Trinidad and Tobago. Using the theory of profession as a theoretical framework, and through an evaluation of institutional, regulatory, and cultural norms and barriers associated with the health care system of Trinidad and Tobago, the role of jurisdiction, societal factors, professional competition, and legitimization was assessed using a qualitative, ethnographic design, with 22 participants. The data collection tools included a questionnaire and structured interview and content analysis of relevant documents to yield the data from which conclusions may be drawn. The results showed that jurisdiction, societal changes, interprofessional competition and legitimization can all influence the introduction of physicians\u27 assistants. Evidence from this research may provide health care administrators with important information to assess the feasibility of the introduction of this vital role to improve patient care on the islands