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Abstract Objective This article describes a scalable, performant, sustainable global network of electronic health record data for biomedical and clinical research. Materials and Methods TriNetX has created a technology platform characterized by a conservative security and governance model that facilitates collaboration and cooperation between industry participants, such as pharmaceutical companies and contract research organizations, and academic and community-based healthcare organizations (HCOs). HCOs participate on the network in return for access to a suite of analytics capabilities, large networks of de-identified data, and more sponsored trial opportunities. Industry participants provide the financial resources to support, expand, and improve the technology platform in return for access to network data, which provides increased efficiencies in clinical trial design and deployment. Results TriNetX is a growing global network, expanding from 55 HCOs and 7 countries in 2017 to over 220 HCOs and 30 countries in 2022. Over 19 000 sponsored clinical trial opportunities have been initiated through the TriNetX network. There have been over 350 peer-reviewed scientific publications based on the network’s data. Conclusions The continued growth of the TriNetX network and its yield of clinical trial collaborations and published studies indicates that this academic-industry structure is a safe, proven, sustainable path for building and maintaining research-centric data networks. Lay Summary This article describes a network—a series of interconnected data repositories—where clinical data about patients is stored after being extracted from electronic health record systems. The data on this network are meant to be used by researchers working in healthcare institutions as well as the life sciences industry. This network aims to make it easier, faster, and cheaper to find patients for recruitment into clinical trials and to conduct research using the clinical data. This network is being developed and maintained by a commercial company TriNetX, LLC. It is growing rapidly, expanding from 55 healthcare organizations and 7 countries in 2017 to over 220 healthcare organizations and 30 countries in 2022. The privacy and security of patient as well as member organizations’ data are of paramount concern. TriNetX takes a very conservative stand with respect to privacy protection and data governance. The data on this network have been used extensively for research and there’s currently over 350 peer-reviewed scientific publications based on the network’s data. The continued growth of the TriNetX network demonstrates that this approach to clinical data sharing is a safe, proven, and sustainable path for supporting the data needs of healthcare and life sciences researchers.

BackgroundPatient-Generated Health Data (PGHD) in remote monitoring programs is a promising source of precise, personalized data, encouraged by expanding growth in the health technologies market. However, PGHD utilization in clinical settings is low. One of the critical challenges that impedes confident clinical use of PGHD is that these data are not managed according to any recognized approach for data quality assurance.ObjectiveThis article aims to identify the PGHD management and quality challenges that such an approach must address, as these are expressed by key PGHD stakeholder groups.Materials and MethodsIn-depth interviews were conducted with 20 experts who have experience in the use of PGHD in remote patient monitoring, including: healthcare providers, health information professionals within clinical settings, and commercial providers of remote monitoring solutions. Participants were asked to describe PGHD management processes in the remote monitoring programs in which they are involved, and to express their perspectives on PGHD quality challenges during the data management stages.ResultsThe remote monitoring programs in the study did not follow clear PGHD management or quality assurance approach. Participants were not fully aware of all the considerations of PGHD quality. Digital health literacy, wearable accuracy, difficulty in data interpretation, and lack of PGHD integration with electronic medical record systems were among the key challenges identified that impact PGHD quality.ConclusionCo-development of PGHD quality guidelines with relevant stakeholders, including patients, is needed to ensure that quality remote monitoring data from wearables is available for use in more precise and personalized patient care.

Activity trackers hold the promise to support people in managing their health through quantified measurements about their daily physical activities. Monitoring personal health with quantified activity tracker-generated data provides patients with an opportunity to self-manage their health. Many have been conducted within short-time frames; makes it difficult to discover the impact of the activity tracker's novelty effect or the reasons for the device's long-term use. This study explores the impact of novelty effect on activity tracker adoption and the motivation for sustained use beyond the novelty period. This study uses a mixed-methods approach that combines both quantitative activity tracker log analysis and qualitative one-on-one interviews to develop a deeper behavioral understanding of 23 Fitbit device users who used their trackers for at least 2 months (range of use = 69-1073 days). Log data from users' Fitbit devices revealed 2 stages: the novelty period and the long-term use period. The novelty period for Fitbit users in this study was approximately 3 months, during which they might have discontinued using their devices. The qualitative interview data identified various factors that users to continuously use the Fitbit devices in different stages. The discussion of these results provides design implications to guide future development of activity tracking technology. This study reveals important dynamics emerging over long-term activity tracker use, contributes new knowledge to consumer health informatics and human-computer interaction, and offers design implications to guide future development of similar health-monitoring technologies that better account for long-term use in support of patient care and health self-management.

Video telehealth technology has the potential to enhance access for patients with clinical, social, and geographic barriers to care. We evaluated the implementation of a US Department of Veterans Affairs (VA) initiative to distribute tablets to high-need Veterans with access barriers. In this mixed methods implementation study, we examined tablet adoption (ie, facility-level tablet distribution rates and patient-level tablet utilization rates) and reach (ie, sociodemographic and clinical characteristics of tablet recipients) between 5/1/16 and 9/30/17. Concurrently, we surveyed 68 facility telehealth coordinators to determine the most common implementation barriers and facilitators, and then conducted interviews with telehealth coordinators and regional leadership to identify strategies that facilitated tablet distribution and use. 86 VA facilities spanning all 18 geographic regions, distributed tablets to 6 745 patients. Recipients had an average age of 56 years, 53% lived in rural areas, 75% had a diagnosed mental illness, and they had a mean (SD) of 5 (3) chronic conditions. Approximately 4 in 5 tablet recipients used the tablet during the evaluation period. In multivariate logistic regression, tablet recipients were more likely to use their tablets if they were older and had fewer chronic conditions. Implementation barriers included insufficient training, staffing shortages, and provider disinterest (described as barriers by 59%, 55%, and 33% of respondents, respectively). Site readiness assessments, local champions, licensure modifications, and use of mandates and incentives were identified as strategies that may influence widespread implementation of home-based video telehealth. VA's initiative to distribute video telehealth tablets to high-need patients appears to have successfully reached individuals with social and clinical access barriers. Implementation strategies that address staffing constraints and provider engagement may enhance the impact of such efforts.

Abstract Background In a recent survey, medical students expressed eagerness to acquire competencies in the use of artificial intelligence (AI) in medicine. It is time that undergraduate medical education takes the lead in helping students develop these competencies. We propose a solution that integrates competency-driven AI instruction in medical school curriculum. Methods We applied constructivist and backwards design principles to design online learning assignments simulating the real-world work done in the healthcare industry. Our innovative approach assumed no technical background for students, yet addressed the need for training clinicians to be ready to practice in the new digital patient care environment. This modular 4-week AI course was implemented in 2019, integrating AI with evidence-based medicine, pathology, pharmacology, tele-monitoring, quality improvement, value-based care, and patient safety. Results This educational innovation was tested in 2 cohorts of fourth year medical students who demonstrated an improvement in knowledge with an average quiz score of 97% and in skills with an average application assignment score of 89%. Weekly reflections revealed how students learned to transition from theory to practice of AI and how these concepts might apply to their upcoming residency training programs and future medical practice. Conclusions We present an innovative product that achieves the objective of competency-based education of students regarding the role of AI in medicine. This course can be integrated in the preclinical years with a focus on foundational knowledge, vocabulary, and concepts, and in clinical years with a focus on application of core knowledge to real-world scenarios. Lay Summary Despite headwinds in usability and outcomes, clinical analytics and artificial intelligence (AI) play an increasingly important role in medical practice. Medical professionals find themselves within a paradigm shift in the healthcare delivery models that rely on technology, yet AI remains a gap in standard medical education. It is generally perceived that medical students are academically unprepared to study technology topics in school and do not require technology skills in practice. Yet, students desire technology topics and identify AI among their knowledge needs. There is no development model or standard for analytics and AI curriculum in medical schools, while interest in the topic among students, faculty, and practitioners is growing. To address the gap in AI education and the challenge of working around mathematics and computer science preparation among medical students, we created and piloted new curriculum with 2 medical student cohorts, by focusing on the role of clinicians in the processes of analytics/AI innovation and practice in the digitally enabled workplace. This online curriculum assumes no prior exposure to computer science topics and fits into multiple modes of delivery to students. It received positive response from the pilot cohorts. We report on methods, content, and results of this academic endeavor.

Background Coix lacryma jobi L., a member of the Poaceae family, is a traditional Chinese medicine ingredient with a long history. In recent years, research and clinical treatment have shown that Coix lacryma jobi L. seed oil exhibits significant anti-cancer effects and enhances the efficacy of chemotherapy. As Diacylglycerol Acyltransferase is a key rate-limiting enzyme in lipid synthesis, a thorough understanding of the Diacylglycerol Acyltransferase gene family in Coix lacryma jobi L. could pave the way for increasing its lipid content. Results This study used the website NCBI to search for the CDS sequences of the Diacylglycerol Acyltransferase family genes in different plants and find the homologous genes in Coix lacryma jobi L. genome by blast screening. A total of ten Diacylglycerol Acyltransferase family genes were identified in Coix lacryma jobi L., which were named ClDGAT1_1, ClDGAT1_2 , ClDGAT1_3 , ClDGAT2_1 , ClDGAT2-2 , ClDGAT3 , ClWS/DGAT_1 , ClWS/DGAT_2 , ClWS/DGAT_3 , and ClWS/DGAT_4 . We analyzed the protein physicochemical properties, gene structure, gene homology and evolutionary analysis of them to elaborate the information of ClDGAT family genes. Meanwhile, functional assays revealed significant differences in oil and fatty acid synthesis among the ClDGAT s. By expressing ten ClDGAT genes in H1246 yeast and comparing the differences in oil and fatty acid content in these yeasts, we found that ClDGAT3 and ClDGAT1_2 had the best oil and triglyceride synthesis ability. This study advanced research on the Diacylglycerol Acyltransferase gene family and expanded the understanding of lipid synthesis-related genes in Coix lacryma jobi L.. Conclusions In this study, we systematically identified and characterized ten DGAT family genes in the Coix lacryma jobi L. genome. Functional validation in the H1246 yeast demonstrated significant divergence in lipid synthesis capacity of ClDGAT isoforms. And ClDGAT3 and ClDGAT1_2 had the best oil and triglyceride synthesis ability.

Abstract Objectives Determine the specific aspects of health information and communications technologies (HICT), including electronic health records (EHRs), most associated with physician burnout, and identify effective coping strategies. Materials and methods We performed a qualitative analysis of transcripts from 2 focus groups and a burnout assessment of ambulatory physicians-each at 3 different health care institutions with 3 different EHRs. Results Of the 41 clinicians, 71% were women, 98% were physicians, and 73% worked in primary care for an average of 11 years. Only 22% indicated sufficient time for documentation. Fifty-six percent noted \"a great deal of stress\" because of their job. Forty-two percent reported \"poor\" or \"marginal\" control over workload. Even though 90% reported EHR proficiency, 56% indicated EHR time at home was \"excessive\" or \"moderately high.\" Focus group themes included HICT \"successes\" where all patients' information is accessible from multiple locations. HICT \"stressors\" included inefficient user interfaces, unpredictable system response times, poor interoperability between systems and excessive data entry. \"Adverse outcomes\" included ergonomic problems (eg, eye strain and hand, wrist, and back pain) and decreased attractiveness of primary care. Suggested \"organizational changes\" included EHR training, improved HICT usability, and scribes. \"Personal/resilience\" strategies focused on self-care (eg, exercise, maintaining work-life boundaries, and positive thinking). Discussion and conclusion HICT use, while beneficial in many ways for patients and providers, has also increased the burden of ambulatory practice with personal and professional consequences. HICT and clinic architectural and process redesign are likely necessary to make significant overall improvements.

Background Conventional management (CM), substantial fertilization and flooding irrigation, has led to soil acidification, the decrease in soil bacterial diversity in bamboo forests. Integration of water and fertilizer management (IWF) can effectively improve the efficiency of water and fertilizer use, but its effect on soil environment, especially on microbial community, is still unclear. Methods Here, we used next-generation high-throughput sequencing to compare soil properties and bacterial communities through different fertilization and irrigation methods under IWF and CM. Results Compared to the control group, CM significantly reduced soil pH and bacterial diversity, while IWF improved soil nutrition status, increased soil bacterial diversity and soil pH to a level similar to the control group. Compared with CM, IWF also improved the relative abundance of beneficial bacteria and copiotrophic bacteria community in the soil, and the bacterial community in IWF was similar to CK. The structure of the bacterial community was also significantly correlated with soil organic matter, total nitrogen, hydrolyzable nitrogen, and available potassium, while soil bacterial diversity was mainly associated with soil hydrolyzable nitrogen. Conclusions IWF can play an important role in preventing soil acidification, the loss of soil bacterial diversity, and improving the structure of the bacterial community under specific conditions.

Objectives Artificial intelligence (AI) holds great promise for transforming the healthcare industry. However, despite its potential, AI is yet to see widespread deployment in clinical settings in significant part due to the lack of publicly available clinical data and the lack of transparency in the published AI algorithms. There are few clinical data repositories publicly accessible to researchers to train and test AI algorithms, and even fewer that contain specialized data from the perioperative setting. To address this gap, we present and release the Medical Informatics Operating Room Vitals and Events Repository (MOVER). Materials and Methods This first release of MOVER includes adult patients who underwent surgery at the University of California, Irvine Medical Center from 2015 to 2022. Data for patients who underwent surgery were captured from 2 different sources: High-fidelity physiological waveforms from all of the operating rooms were captured in real time and matched with electronic medical record data. Results MOVER includes data from 58 799 unique patients and 83 468 surgeries. MOVER is available for download at https://doi.org/10.24432/C5VS5G, it can be downloaded by anyone who signs a data usage agreement (DUA), to restrict traffic to legitimate researchers. Discussion To the best of our knowledge MOVER is the only freely available public data repository that contains electronic health record and high-fidelity physiological waveforms data for patients undergoing surgery. Conclusion MOVER is freely available to all researchers who sign a DUA, and we hope that it will accelerate the integration of AI into healthcare settings, ultimately leading to improved patient outcomes. Lay Summary Despite many publications showing artificial intelligence algorithms to be successful in retrospective healthcare studies, there is a very limited amount of freely and publicly available medical data for researchers to work with, to develop and benchmark predictive and other methods in a reproducible manner. This is even more significant in the perioperative setting for patients undergoing surgery and anesthesia. In this article, we present and release a new repository we have constructed called MOVER: Medical Informatics Operating Room Vitals and Events Repository. This repository contains data (electronic medical record data and high-fidelity physiological waveforms data obtained from the bedside physiological monitors) associated with hospital visits for patients undergoing surgery and anesthesia. MOVER is freely available for download for all researchers who sign a data usage agreement: https://doi.org/10.24432/C5VS5G. MOVER is intended to advance a wide variety of healthcare research and serve as a resource to evaluate new clinical decision support and monitoring algorithms.

ObjectivesScalable informatics solutions that provide molecularly tailored treatment recommendations to clinicians are needed to streamline precision oncology in care settings.Materials and MethodsWe developed a cloud-based virtual molecular tumor board (VMTB) platform that included a knowledgebase, scoring model, rules engine, an asynchronous virtual chat room and a reporting tool that generated a treatment plan for each of the 1725 patients based on their molecular profile, previous treatment history, structured trial eligibility criteria, clinically relevant cancer gene-variant assertions, biomarker-treatment associations, and current treatment guidelines. The VMTB systematically allows clinician users to combine expert-curated data and structured data from clinical charts along with molecular testing data to develop consensus on treatments, especially those that require off-label and clinical trial considerations.ResultsThe VMTB was used as part of the cancer care process for a focused subset of 1725 patients referred by advocacy organizations wherein resultant personalized reports were successfully delivered to treating oncologists. Median turnaround time from data receipt to report delivery decreased from 14 days to 4 days over 4 years while the volume of cases increased nearly 2-fold each year. Using a novel scoring model for ranking therapy options, oncologists chose to implement the VMTB-derived therapies over others, except when pursuing immunotherapy options without molecular support.DiscussionVMTBs will play an increasingly critical role in precision oncology as the compendium of biomarkers and associated therapy options available to a patient continues to expand.ConclusionFurther development of such clinical augmentation tools that systematically combine patient-derived molecular data, real-world evidence from electronic health records and expert curated knowledgebases on biomarkers with computational tools for ranking best treatments can support care pathways at point of care.