@inproceedings{abf5ae430d3f45b89bf7b8cf1fe494cb,
title = "Privacy-Preserving Record Linkage to Identify Fragmented Electronic Medical Records in the All of Us Research Program",
abstract = "As part of a national study in the United States to recruit one million Americans (All of Us Research Program) and their Electronic Health Record data, we set out to determine the degree to which care is fragmented across a sample of participating health provider organizations (HPOs). We distributed a previously validated Privacy-Preserving Record Linkage (PPRL) tool to participating sites to generate a unique set of keyed encrypted hashes for seven participating institutions across three States in the Upper Midwest of the U.S. An honest broker received the resulting encrypted hashes to identify patients with the same encrypted hashes shared across any combination of more than one institution as a proxy for patients receiving care across institutions. Out of 5,831,238 individuals, we identified 458,680 patients with data at more than one institution. Care fragmentation varied significantly by State and by Institution ranging from 6.1\% up to 32.7\%. Patients with fragmented care were more likely to be black (11.8\% vs 10.8\%), and slightly older (Median birth year 1968 vs 1969) compared with patients receiving care at only one participating institution. In contrast, patients who maintained an address in a warmer state (“snowbirds”) were the least likely to be black (7.5\%) of all study groups. We identified conflicting or inconsistent demographic information in 49.1\% of patients with care fragmentation compared with 5.6\% of patients without care fragmentation. Privacy-preserving record linkage can be an effective means to identify populations with care fragmentation and poor data quality for focused clinical and data improvement efforts.",
keywords = "Ecology of care, Privacy preservation, Record linkage",
author = "Kho, \{Abel N.\} and Jingzhi Yu and Bryan, \{Molly Scannell\} and Charon Gladfelter and Gordon, \{Howard S.\} and Shaun Grannis and Margaret Madden and Eneida Mendonca and Vesna Mitrovic and Raj Shah and Umberto Tachinardi and Bradley Taylor",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 19th Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019 ; Conference date: 16-09-2019 Through 20-09-2019",
year = "2020",
doi = "10.1007/978-3-030-43887-6\_7",
language = "English (UK)",
isbn = "9783030438869",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "79--87",
editor = "Peggy Cellier and Kurt Driessens",
booktitle = "Machine Learning and Knowledge Discovery in Databases - International Workshops of ECML PKDD 2019, Proceedings",
address = "Germany",
}