Browsing by Author "Gouripeddi, Ramkiran"

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    EHR-based cohort assessment for multicenter RCTs: a fast and flexible model for identifying potential study sites
    (Oxford University Press, 2022) Nelson, Sarah J.; Drury, Bethany; Hood, Daniel; Harper, Jeremy; Bernard, Tiffany; Weng, Chunhua; Kennedy, Nan; LaSalle, Bernie; Gouripeddi, Ramkiran; Wilkins, Consuelo H.; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Objective: The Recruitment Innovation Center (RIC), partnering with the Trial Innovation Network and institutions in the National Institutes of Health-sponsored Clinical and Translational Science Awards (CTSA) Program, aimed to develop a service line to retrieve study population estimates from electronic health record (EHR) systems for use in selecting enrollment sites for multicenter clinical trials. Our goal was to create and field-test a low burden, low tech, and high-yield method. Materials and methods: In building this service line, the RIC strove to complement, rather than replace, CTSA hubs' existing cohort assessment tools. For each new EHR cohort request, we work with the investigator to develop a computable phenotype algorithm that targets the desired population. CTSA hubs run the phenotype query and return results using a standardized survey. We provide a comprehensive report to the investigator to assist in study site selection. Results: From 2017 to 2020, the RIC developed and socialized 36 phenotype-dependent cohort requests on behalf of investigators. The average response rate to these requests was 73%. Discussion: Achieving enrollment goals in a multicenter clinical trial requires that researchers identify study sites that will provide sufficient enrollment. The fast and flexible method the RIC has developed, with CTSA feedback, allows hubs to query their EHR using a generalizable, vetted phenotype algorithm to produce reliable counts of potentially eligible study participants. Conclusion: The RIC's EHR cohort assessment process for evaluating sites for multicenter trials has been shown to be efficient and helpful. The model may be replicated for use by other programs.
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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.