Browsing by Author "Kho, Abel"

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    Evolving availability and standardization of patient attributes for matching
    (Oxford University Press, 2023-10-12) Deng, Yu; Gleason, Lacey P.; Culbertson, Adam; Chen, Xiaotian; Bernstam, Elmer V.; Cullen, Theresa; Gouripeddi, Ramkiran; Harle, Christopher; Hesse, David F.; Kean, Jacob; Lee, John; Magoc, Tanja; Meeker, Daniella; Ong, Toan; Pathak, Jyotishman; Rosenman, Marc; Rusie, Laura K.; Shah, Akash J.; Shi, Lizheng; Thomas, Aaron; Trick, William E.; Grannis, Shaun; Kho, Abel; Health Policy and Management, Richard M. Fairbanks School of Public Health
    Variation in availability, format, and standardization of patient attributes across health care organizations impacts patient-matching performance. We report on the changing nature of patient-matching features available from 2010-2020 across diverse care settings. We asked 38 health care provider organizations about their current patient attribute data-collection practices. All sites collected name, date of birth (DOB), address, and phone number. Name, DOB, current address, social security number (SSN), sex, and phone number were most commonly used for cross-provider patient matching. Electronic health record queries for a subset of 20 participating sites revealed that DOB, first name, last name, city, and postal codes were highly available (>90%) across health care organizations and time. SSN declined slightly in the last years of the study period. Birth sex, gender identity, language, country full name, country abbreviation, health insurance number, ethnicity, cell phone number, email address, and weight increased over 50% from 2010 to 2020. Understanding the wide variation in available patient attributes across care settings in the United States can guide selection and standardization efforts for improved patient matching in the United States.
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    Multihospital Infection Prevention Collaborative: Informatics Challenges and Strategies to Prevent MRSA
    (2013-11) Doebbeling, Bradley N.; Flanagan, Mindy E.; Nall, Glenna; Hoke, Shawn; Rosenman, Marc; Kho, Abel
    We formed a collaborative to spread effective MRSA prevention strategies. We conducted a two-phase, multisite, quasi-experimental study of seven hospital systems (11 hospitals) in IN, MT, ME and Ontario, Canada over six years. Patients with prior MRSA were identified at admission using regional health information exchange data. We developed a system to return an alert message indicating a prior history of MRSA, directed to infection preventionists and admissions. Alerts indicated the prior anatomic site, and the originating institution. The combined approach of training and coaching, implementation of MRSA registries, notifying hospitals on admission of previously infected or colonized patients, and change strategies was effective in reducing MRSA infections over 80%. Further research and development of electronic surveillance tools is needed to better integrate the varied data source and support preventing MRSA infections. Our study supports the importance of hospitals collaborating to share data and implement effective strategies to prevent MRSA.
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    Privacy‐preserving record linkage across disparate institutions and datasets to enable a learning health system: The national COVID cohort collaborative (N3C) experience
    (Wiley, 2024-01-11) Tachinardi, Umberto; Grannis, Shaun J.; Michael, Sam G.; Misquitta, Leonie; Dahlin, Jayme; Sheikh, Usman; Kho, Abel; Phua, Jasmin; Rogovin, Sara S.; Amor, Benjamin; Choudhury, Maya; Sparks, Philip; Mannaa, Amin; Ljazouli, Saad; Saltz, Joel; Prior, Fred; Baghal, Ahmen; Gersing, Kenneth; Embi, Peter J.; Medicine, School of Medicine
    Introduction: Research driven by real-world clinical data is increasingly vital to enabling learning health systems, but integrating such data from across disparate health systems is challenging. As part of the NCATS National COVID Cohort Collaborative (N3C), the N3C Data Enclave was established as a centralized repository of deidentified and harmonized COVID-19 patient data from institutions across the US. However, making this data most useful for research requires linking it with information such as mortality data, images, and viral variants. The objective of this project was to establish privacy-preserving record linkage (PPRL) methods to ensure that patient-level EHR data remains secure and private when governance-approved linkages with other datasets occur. Methods: Separate agreements and approval processes govern N3C data contribution and data access. The Linkage Honest Broker (LHB), an independent neutral party (the Regenstrief Institute), ensures data linkages are robust and secure by adding an extra layer of separation between protected health information and clinical data. The LHB's PPRL methods (including algorithms, processes, and governance) match patient records using "deidentified tokens," which are hashed combinations of identifier fields that define a match across data repositories without using patients' clear-text identifiers. Results: These methods enable three linkage functions: Deduplication, Linking Multiple Datasets, and Cohort Discovery. To date, two external repositories have been cross-linked. As of March 1, 2023, 43 sites have signed the LHB Agreement; 35 sites have sent tokens generated for 9 528 998 patients. In this initial cohort, the LHB identified 135 037 matches and 68 596 duplicates. Conclusion: This large-scale linkage study using deidentified datasets of varying characteristics established secure methods for protecting the privacy of N3C patient data when linked for research purposes. This technology has potential for use with registries for other diseases and conditions.