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Abstract Background Clinical early warning scoring systems, have improved patient outcomes in a range of specializations and global contexts. These systems are used to predict patient deterioration. A multitude of patient-level physiological decompensation data has been made available through the widespread integration of early warning scoring systems within EHRs across national and international health care organizations. These data can be used to promote secondary research. The diversity of early warning scoring systems and various EHR systems is one barrier to secondary analysis of early warning score data. Given that early warning score parameters are varied, this makes it difficult to query across providers and EHR systems. Moreover, mapping and merging the parameters is challenging. We develop and validate the Early Warning System Scores Ontology (EWSSO), representing three commonly used early warning scores: the National Early Warning Score (NEWS), the six-item modified Early Warning Score (MEWS), and the quick Sequential Organ Failure Assessment (qSOFA) to overcome these problems. Methods We apply the Software Development Lifecycle Framework—conceived by Winston Boyce in 1970—to model the activities involved in organizing, producing, and evaluating the EWSSO. We also follow OBO Foundry Principles and the principles of best practice for domain ontology design, terms, definitions, and classifications to meet BFO requirements for ontology building. Results We developed twenty-nine new classes, reused four classes and four object properties to create the EWSSO. When we queried the data our ontology-based process could differentiate between necessary and unnecessary features for score calculation 100% of the time. Further, our process applied the proper temperature conversions for the early warning score calculator 100% of the time. Conclusions Using synthetic datasets, we demonstrate the EWSSO can be used to generate and query health system data on vital signs and provide input to calculate the NEWS, six-item MEWS, and qSOFA. Future work includes extending the EWSSO by introducing additional early warning scores for adult and pediatric patient populations and creating patient profiles that contain clinical, demographic, and outcomes data regarding the patient.

Abstract Background: The murder of George Floyd created national outcry that echoed down to national institutions, including universities and academic systems to take a hard look at systematic and systemic racism in higher education. This motivated the creation of a fear and tension-minimizing, curricular offering, “Courageous Conversations,” collaboratively engaging students, staff, and faculty in matters of diversity, equity, and inclusion (DEI) in the Department of Health Outcomes and Biomedical Informatics at the University of Florida. Methods: A qualitative design was employed assessing narrative feedback from participants during the Fall semester of 2020. Additionally, the ten-factor model implementation framework was applied and assessed. Data collection included two focus groups and document analysis with member-checking. Thematic analysis (i.e., organizing, coding, synthesizing) was used to analyze a priori themes based on the four agreements of the courageous conversations framework, stay engaged, expect to experience discomfort, speak your truth, and expect and accept non-closure. Results: A total of 41 participants of which 20 (48.78%) were department staff members, 11 (26.83%) were department faculty members, and 10 (24.30%) were graduate students. The thematic analysis revealed 1) that many participants credited their learning experiences to what their peers had said about their own personal lived experiences during group sessions, and 2) several participants said they would either retake the course or recommend it to a colleague. Conclusion: With structured implementation, courageous conversations can be an effective approach to create more diverse, equitable, and inclusive spaces in training programs with similar DEI ecosystems.

BackgroundSocial and behavioral aspects of our lives significantly impact our health, yet minimal social determinants of health (SDOH) data elements are collected in the healthcare system.MethodsIn this proof-of-concept study we developed a repeatable SDOH enrichment and integration process to incorporate dynamically evolving SDOH domain concepts from consumers into clinical data. This process included SDOH mapping, linking compiled consumer data to patient records in Electronic Health Records, data quality analysis and preprocessing, and storage.ResultsConsumer compilers data coverage ranged from ~90 to ~54% and the percentage match rate between compilers was between ~21 and 64%. Our preliminary analysis showed that apart from demographic factors, several SDOH factors like home-ownership, marital-status, presence of children, number of members per household, economic stability and education were significantly different between the COVID-19 positive and negative patient groups while estimated family-income and home market-value were not.ConclusionOur preliminary analysis shows commercial consumer data can be a viable source of SDOH factor at an individual-level for clinical data thus providing a path for clinicians to improve patient treatment and care.