OBJECTIVES: In 2023, AMIA's Inclusive Language and Context Style Guidelines (the "Guidelines") were approved by the Board of Directors and made a publicly available resource. This work began in 2021 through AMIA's DEI Task Force and subsequent DEI Committee; many members provided input, feedback, and time to create the Guidelines. In this paper, the authors provide a transparent account of the origin, development, contents, and dissemination of the Guidelines and share plans for their future development and use. MATERIALS AND METHODS: Our approach to drafting, refining, and distributing the Guidelines included consulting existing language guides, AMIA member reviews, external expert reviews, webinars, and workshops. Through an iterative approach to drafting and refining the Guidelines, the authors consulted relevant language guidelines and many experts throughout and beyond the AMIA community. RESULTS: The Inclusive Language Context Guidelines were formally approved by the AMIA Board of Directors on February 15, 2023. The Guidelines included four principles to be considered in scientific communications: Plurality, Precision, Transparency, and Destigmatization. DISCUSSION: A moment of vulnerability where an AMIA member raised concerns about the use of harmful language during a presentation resulted in the creation of a principled approach to support inclusive language within biomedical and health informatics communications. We envision that the Guidelines will support health equity by challenging dominant public narratives around health, fostering stronger interdisciplinary collaboration and critical thinking about the impact of language, and creating a more welcoming environment for the broader AMIA community. This work could not have been completed without the support of many AMIA members and other researchers in biomedical and health informatics. The Guidelines are a living document that will continue to be updated with input and feedback from the AMIA community into the future.

InterMed is a collaboration among research groups from Stanford, Harvard, and Columbia Universities. The primary goal of InterMed has been to develop a sharable language that could serve as a standard for modeling computer-interpretable guidelines (CIGs). This language, called GuideLine Interchange Format (GLIF), has been developed in a collaborative manner and in an open process that has welcomed input from the larger community. The goals and experiences of the InterMed project and lessons that the authors have learned may contribute to the work of other researchers who are developing medical knowledge-based tools. The lessons described include (1) a work process for multi-institutional research and development that considers different viewpoints, (2) an evolutionary lifecycle process for developing medical knowledge representation formats, (3) the role of cognitive methodology to evaluate and assist in the evolutionary development process, (4) development of an architecture and (5) design principles for sharable medical knowledge representation formats, and (6) a process for standardization of a CIG modeling language.

OBJECTIVE: To ascertain whether three-dimensional geometric and probabilistic reasoning methods can be successfully combined for computer-based assessment of conditions arising from ballistic penetrating trauma to the chest and abdomen. DESIGN: The authors created a computer system (TraumaSCAN) that integrates three-dimensional geometric reasoning about anatomic likelihood of injury with probabilistic reasoning about injury consequences using Bayesian networks. Preliminary evaluation of TraumaSCAN was performed via a retrospective study testing performance of the system on data from 26 cases of actual gunshot wounds. MEASUREMENTS: Areas under the receiver operating characteristics (ROC) curve were calculated for each condition modeled in TraumaSCAN that was present in the 26 cases. The comprehensiveness and relevance of the TraumaSCAN diagnosis for the 26 cases were used to assess the overall performance of the system. To test the ability of TraumaSCAN to handle limited findings, these measurements were calculated both with and without input of observed findings into the Bayesian network. RESULTS: For the 11 conditions assessed, the worst area under the ROC curve with no observed findings input into the Bayesian network was 0.542 (95% CI, 0.146-0.937), the median was 0.883 (95% CI, 0.713-1.000), and the best was 1.00 (95% CI, 1.000-1.000). The worst area under the ROC curve with all observed findings input into the Bayesian network was 0.835 (95% CI, 0.602-1.000), the median was 0.941 (95% CI, 0.827-1.000), and the best was 0.992 (95% CI, 0.965-1.000). A comparison of the areas under the curve obtained with and without input of observed findings into the Bayesian network showed that there were significant differences for 2 of the 11 conditions assessed. CONCLUSION: A computer-based method that combines geometric and probabilistic reasoning shows promise as a tool for assessing ballistic penetrating trauma to the chest and abdomen.

Describes a method for diagnostic reasoning under uncertainty that is used in TraumaSCAN, a computer-based system for assessing penetrating trauma. Uncertainty in assessing penetrating injuries arises from two different sources: the actual extent of damage associated with a particular injury mechanism may not be easily discernable, and there may be incomplete information about patient findings (signs, symptoms and test results) which provide clues about the extent of the injury. Bayesian networks are used in TraumaSCAN for diagnostic reasoning because they provide a mathematically sound means of making probabilistic inferences about the injury in the face of uncertainty. We also present a comparison of TraumaSCAN's results in assessing 26 actual gunshot wound cases with those of TraumAID, a validated rule-based expert system for the diagnosis and treatment of penetrating trauma.

Examines an approach for integrating 3D structural reasoning, using computer models of the human anatomy, with diagnostic reasoning based on Bayesian networks in order to probabilistically predict injuries to anatomic structures from gunshot wounds. An interactive 3D graphical system has been created which allows the user to visualize different bullet path hypotheses and computes the probability that an anatomical structure associated with a given penetration path is injured. The probabilities derived are essential for mediating between structural reasoning and diagnostic reasoning.