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Knowledge graphs have become a common approach for knowledge representation. Yet, the application of graph methodology is elusive due to the sheer number and complexity of knowledge sources. In addition, semantic incompatibilities hinder efforts to harmonize and integrate across these diverse sources. As part of The Biomedical Translator Consortium, we have developed a knowledge graph-based question-answering system designed to augment human reasoning and accelerate translational scientific discovery: the Translator system. We have applied the Translator system to answer biomedical questions in the context of a broad array of diseases and syndromes, including Fanconi anemia, primary ciliary dyskinesia, multiple sclerosis, and others. A variety of collaborative approaches have been used to research and develop the Translator system. One recent approach involved the establishment of a monthly "Question-of-the-Month (QotM) Challenge" series. Herein, we describe the structure of the QotM Challenge; the six challenges that have been conducted to date on drug-induced liver injury, cannabidiol toxicity, coronavirus infection, diabetes, psoriatic arthritis, and ATP1A3-related phenotypes; the scientific insights that have been gleaned during the challenges; and the technical issues that were identified over the course of the challenges and that can now be addressed to foster further development of the prototype Translator system. We close with a discussion on Large Language Models such as ChatGPT and highlight differences between those models and the Translator system.

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based "Translator" system capable of integrating existing biomedical data sets and "translating" those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system's architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.

PURPOSE Many individuals with behavioral health challenges receive services in primary care, and integrated behavioral health (IBH) programs can help increase access to evidence-based interventions. IBH programs can benefit substantially from integrating standardized tracking databases that allow for the implementation of measurement-based care to evaluate patient-, clinician-, and practice-level outcomes. We describe the development and integration of Mayo Clinic's pediatric and adult primary care psychotherapy tracking database. METHODS IBH practice leaders directed the development of a large psychotherapy tracking database that continuously populates from Mayo Clinic's electronic health record system. The database captures numerous patient variables including demographics, behavioral health and substance use issues, psychotherapy principles used, and self-reported symptoms. We retrieved current data for patients empaneled in Mayo Clinic's pediatric and adult primary care psychotherapy programs from June 2014 to June 2022. RESULTS The tracking database contained data for 16,923 adult patients and 6,298 pediatric patients. The mean age of adult patients was 43.2 years (SD 18.3), 88.1% were non-Latine White, and 66.7% identified as female. The mean age of pediatric patients was 11.6 years (SD 4.2), 82.5% were non-Latine White, and 56.9% identified as female. We provide examples of practical applications of the database across clinical, educational, research, and administrative domains. CONCLUSIONS The development and integration of a psychotherapy tracking database supports clinician communication, examination of patient outcomes, practice quality improvement efforts, and clinically relevant research. Our description of Mayo Clinic's IBH database may serve as a model for other IBH practices. Key words: integrated behavioral health; measurement-based care; electronic health records; health informatics; quality improvement; health metrics; mental health; psychotherapy; primary care; multidisciplinary research; patient care team; population health https://doi.org/10.1370/afm.2927, INTRODUCTION Behavioral health disorders in both children and adults are prevalent (1-4) and burdensome, resulting in a marked degree of functional impairment. (5,6) Although evidence-based interventions exist, many individuals needing [...]

BackgroundPatient-centered registries are essential in population-based clinical care for patient identification and monitoring of outcomes. Although registry data may be used in real time for patient care, the same data may further be used for secondary analysis to assess disease burden, evaluation of disease management and health care services, and research. The design of a registry has major implications for the ability to effectively use these clinical data in research. ObjectiveThis study aims to develop a systematic framework to address the data and methodological issues involved in analyzing data in clinically designed patient-centered registries. MethodsThe systematic framework was composed of 3 major components: visualizing the multifaceted and heterogeneous patient-centered registries using a data flow diagram, assessing and managing data quality issues, and identifying patient cohorts for addressing specific research questions. ResultsUsing a clinical registry designed as a part of a collaborative care program for adults with depression at Mayo Clinic, we were able to demonstrate the impact of the proposed framework on data integrity. By following the data cleaning and refining procedures of the framework, we were able to generate high-quality data that were available for research questions about the coordination and management of depression in a primary care setting. We describe the steps involved in converting clinically collected data into a viable research data set using registry cohorts of depressed adults to assess the impact on high-cost service use. ConclusionsThe systematic framework discussed in this study sheds light on the existing inconsistency and data quality issues in patient-centered registries. This study provided a step-by-step procedure for addressing these challenges and for generating high-quality data for both quality improvement and research that may enhance care and outcomes for patients. International Registered Report Identifier (IRRID)DERR1-10.2196/18366

Objectives: There is a lack of knowledge about the challenges of researchers who continued in-person research during the early phases of the COVID-19 pandemic. Design: Electronic survey assessing work-related exposure to COVID-19, logistical challenges, and procedural changes during the first year of the COVID-19 pandemic on clinical research. Setting: National Heart, Lung, and Blood Institute-sponsored Prevention and Early Treatment of Acute Lung Injury Clinical Trial Network Centers. Subjects: Research staff at research Network Sites. Measurements and Main Results: The 37-question survey was completed by 277 individuals from 24 states between 29 September 2020, and 12 December 2020, yielding a response rate of 37.7%. Most respondents (91.5%) indicated that non-COVID-19 research was affected by COVID-19 research studies. In response to the COVID-19 pandemic, 20% of respondents were reassigned to different roles at their institution. Many survey takers were exposed to COVID-19 (56%), with more than 50% of researchers requiring a COVID-19 test and 8% testing positive. The fear of infection was 2.7-times higher compared to pre-COVID-19 times. Shortages of personal protective equipment were encountered by 34% of respondents, primarily due to lack of access to N95 masks, followed by gowns and protective eyewear. Personal protective equipment reallocation from research to clinical use was reported by 31% of respondents. Most of the respondents (88.5%), despite these logistical challenges, indicated their willingness to enroll COVID-19 patients. Conclusions: During the first year of the COVID-19 pandemic, members of the research network were engaged in COVID-19 research despite logistical challenges, limited access to personal protective equipment, and fear of exposure. The research network's survey experience can inform ongoing policy discussions to create research enterprises that can dexterously refocus research to address the knowledge gaps associated with novel public health emergencies while mitigating the effect of pandemics on existing research projects and research personnel. [ABSTRACT FROM AUTHOR]