Browsing by Author "Visweswaran, Shyam"

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    The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment
    (Oxford University Press, 2021) Haendel, Melissa A.; Chute, Christopher G.; Bennett, Tellen D.; Eichmann, David A.; Guinney, Justin; Kibbe, Warren A.; Payne, Philip R. O.; Pfaff, Emily R.; Robinson, Peter N.; Saltz, Joel H.; Spratt, Heidi; Suver, Christine; Wilbanks, John; Wilcox, Adam B.; Williams, Andrew E.; Wu, Chunlei; Blacketer, Clair; Bradford, Robert L.; Cimino, James J.; Clark, Marshall; Colmenares, Evan W.; Francis, Patricia A.; Gabriel, Davera; Graves, Alexis; Hemadri, Raju; Hong, Stephanie S.; Hripscak, George; Jiao, Dazhi; Klann, Jeffrey G.; Kostka, Kristin; Lee, Adam M.; Lehmann, Harold P.; Lingrey, Lora; Miller, Robert T.; Morris, Michele; Murphy, Shawn N.; Natarajan, Karthik; Palchuk, Matvey B.; Sheikh, Usman; Solbrig, Harold; Visweswaran, Shyam; Walden, Anita; Walters, Kellie M.; Weber, Griffin M.; Zhang, Xiaohan Tanner; Zhu, Richard L.; Amor, Benjamin; Girvin, Andrew T.; Manna, Amin; Qureshi, Nabeel; Kurilla, Michael G.; Michael, Sam G.; Portilla, Lili M.; Rutter, Joni L.; Austin, Christopher P.; Gersing, Ken R.; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Objective: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. Materials and methods: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. Results: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. Conclusions: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.
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    Use, Impact, Weaknesses, and Advanced Features of Search Functions for Clinical Use in Electronic Health Records: A Scoping Review
    (Thieme, 2021-05) Hill, Jordan R.; Visweswaran, Shyam; Ning, Xia; Schleyer, Titus K.; Medicine, School of Medicine
    Objective: Although vast amounts of patient information are captured in electronic health records (EHRs), effective clinical use of this information is challenging due to inadequate and inefficient access to it at the point of care. The purpose of this study was to conduct a scoping review of the literature on the use of EHR search functions within a single patient's record in clinical settings to characterize the current state of research on the topic and identify areas for future study. Methods: We conducted a literature search of four databases to identify articles on within-EHR search functions or the use of EHR search function in the context of clinical tasks. After reviewing titles and abstracts and performing a full-text review of selected articles, we included 17 articles in the analysis. We qualitatively identified themes in those articles and synthesized the literature for each theme. Results: Based on the 17 articles analyzed, we delineated four themes: (1) how clinicians use search functions, (2) impact of search functions on clinical workflow, (3) weaknesses of current search functions, and (4) advanced search features. Our review found that search functions generally facilitate patient information retrieval by clinicians and are positively received by users. However, existing search functions have weaknesses, such as yielding false negatives and false positives, which can decrease trust in the results, and requiring a high cognitive load to perform an inclusive search of a patient's record. Conclusion: Despite the widespread adoption of EHRs, only a limited number of articles describe the use of EHR search functions in a clinical setting, despite evidence that they benefit clinician workflow and productivity. Some of the weaknesses of current search functions may be addressed by enhancing EHR search functions with collaborative filtering.