William W. Stead

Objective: Clarify disciplinary foundations and internal structure of biomedical informatics. Methods: We analyze BMI’s emergence at disciplinary intersections and map its internal structure across 4 domains: theory and practice of knowledge discovery, knowledge representation and reasoning, knowledge architecture, and knowledge-driven transformation. We compare BMI with mathematics, computer science, biostatistics, and biomedical engineering, and illustrate emergent characteristics through a precision medicine example. Results: BMI’s distinctive contribution—elucidating the structure of biomedical knowledge and developing methods to discover, preserve, and make knowledge actionable—requires strength across all 4 domains. BMI developed these domains pragmatically: building systems, extracting principles, and formalizing theories. The discipline must now complement empirical approaches with rigorous theoretical work: assessing adequacy of existing theories, identifying gaps, and orchestrating collaborative development. Conclusions: BMI creates emergent capabilities across disciplines. As biomedicine becomes increasingly complex, BMI must strengthen its theoretical foundations while demonstrating transformative potential of knowledge spanning biological scales and time.

The rising popularity of artificial intelligence in healthcare is highlighting the problem that a computational model achieving super-human clinical performance at its training sites may perform substantially worse at new sites. In this perspective, we argue that we should typically expect this failure to transport, and we present common sources for it, divided into those under the control of the experimenter and those inherent to the clinical data-generating process. Of the inherent sources we look a little deeper into site-specific clinical practices that can affect the data distribution, and propose a potential solution intended to isolate the imprint of those practices on the data from the patterns of disease cause and effect that are the usual target of probabilistic clinical models.

In this editorial, the first 4 Editors-in-Chief of the Journal of the American Medical Informatics Association (JAMIA) reflect on its history and future. The origins and characterization of each Editor’s era represent the “lived experience” of each Editor rather than a comparison of common metrics over time. We also qualitatively assess JAMIA’s progress in meeting its original vision and goals, and posit considerations for its future. Table 1 summarizes key JAMIA-related events. The summary of origins was compiled from: Report of the AAMSI Publication Committee May 8, 1985; Minutes of the AAMSI Board of Directors May 19, 1985; Minutes of the Meeting of ACMI May 22, 1985; Action items recommended by the AMIA Publication Committee to the AMIA Board of Directors March 31, 1992; and Motions approved by the AMIA Board of Directors May 8, 1992. In 1985, the Board of Directors of the American Association for Medical Systems and Informatics (AAMSI) asked the Publications Committee to develop a proposal to include a subscription to a professional journal as a benefit funded by membership dues. The Committee reviewed 46 publications and recommended affiliating with 2 journals considering the varied interests of AAMSI’s membership. The Committee recommended Computers and Biomedical Research as the scientific journal and MD Computing for state-of-the-art reviews. The Board decided to make these journals “official journals” of the Association and to increase dues by about a third to cover the subscriptions. These decisions were controversial and the votes were not unanimous. Later that year, the American College of Medical Informatics (ACMI) endorsed Computers and Biomedical Research and included the subscription in its dues. MD Computing was already broadening its content to represent all health professions and its editorial board included 10 AAMSI members. Computers and Biomedical Research restructured its editorial board to include 5 individuals who were both AAMSI members and ACMI fellows to recruit multidisciplinary papers.

Motivation: Phecodes are widely used and easily adapted phenotypes based on International Classification of Diseases codes. The current version of phecodes (v1.2) was designed primarily to study common/complex diseases diagnosed in adults; however, there are numerous limitations in the codes and their structure. Results: Here, we present phecodeX, an expanded version of phecodes with a revised structure and 1,761 new codes. PhecodeX adds granularity to phenotypes in key disease domains that are under-represented in the current phecode structure—including infectious disease, pregnancy, congenital anomalies, and neonatology—and is a more robust representation of the medical phenome for global use in discovery research. Availability and implementation: phecodeX is available at https://github.com/PheWAS/phecodeX.

Suzanne (Sue) Bakken, RN, PhD and Editor-in-Chief of the Journal of the American Medical Informatics Association (JAMIA), has been selected to receive the 2023 Morris F. Collen Award from the American College of Medical Informatics to honor her lifetime contributions to the field of biomedical informatics. Bakken has advanced the field by integrating leading-edge research with training successive generations of informaticians and by working with underserved communities to bring the results of research to people’s lives. We have created an on-line collection of selected papers from among Bakken’s publications in JAMIA that is available at academic.oup.com/jamia/pages/morris-collen-award-2023. The first paper in the collection appeared in the inaugural issue of JAMIA and reported her analysis of terms used by nurses to describe patient problems as a test of the feasibility of using SNOMED III as a nursing data standard. ¹ This article exhibits the hallmarks of her work: rigorous grounding in existing frameworks, in this case, a comparative review of healthcare classification schemes and published evaluation studies related to their use to represent clinical data; explicit research questions; appropriate use of both qualitative and quantitative analysis; and discussion including an actionable path forward for the field. The collection provides a glimpse of her progression from first author researcher ^1–6 to senior author with a mentee as first author, ^7–15 as well as the evolution of her research foci.

Objective: Knowledgebases are needed to clarify correlations observed in real-world electronic health record (EHR) data. We posit design principles, present a unifying framework, and report a test of concept. Materials and Methods: We structured a knowledge framework along 3 axes: condition of interest, knowledge source, and taxonomy. In our test of concept, we used hypertension as our condition of interest, literature and VanderbiltDDx knowledgebase as sources, and phecodes as our taxonomy. In a cohort of 832 566 deidentified EHRs, we modeled blood pressure and heart rate by sex and age, classified individuals by hypertensive status, and ran a Phenome-wide Association Study (PheWAS) for hypertension. We compared the correlations from PheWAS to the associations in our knowledgebase. Results: We produced PhecodeKbHtn: a knowledgebase comprising 167 hypertension-associated diseases, 15 of which were also negatively associated with blood pressure (pos+neg). Our hypertension PheWAS included 1914 phecodes, 129 of which were in the PhecodeKbHtn. Among the PheWAS association results, phecodes that were in PhecodeKbHtn had larger effect sizes compared with those phecodes not in the knowledgebase. Discussion: Each source contributed unique and additive associations. Models of blood pressure and heart rate by age and sex were consistent with prior cohort studies. All but 4 PheWAS positive and negative correlations for phecodes in PhecodeKbHtn may be explained by knowledgebase associations, hypertensive cardiac complications, or causes of hypertension independently associated with hypotension. Conclusion: It is feasible to assemble a knowledgebase that is compatible with EHR data to aid interpretation of clinical correlation research.

This Viewpoint discusses ways in which artificial intelligence (AI) methods can enhance the use of health care data in detection, prognostication, and predictio