Browsing by Subject "Patient matching"

Now showing 1 - 5 of 5
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    A framework for a consistent and reproducible evaluation of manual review for patient matching algorithms
    (Oxford University Press, 2022) Gupta, Agrayan K.; Kasthurirathne, Suranga N.; Xu, Huiping; Li, Xiaochun; Ruppert, Matthew M.; Harle, Christopher A.; Grannis, Shaun J.; Medicine, School of Medicine
    Healthcare systems are hampered by incomplete and fragmented patient health records. Record linkage is widely accepted as a solution to improve the quality and completeness of patient records. However, there does not exist a systematic approach for manually reviewing patient records to create gold standard record linkage data sets. We propose a robust framework for creating and evaluating manually reviewed gold standard data sets for measuring the performance of patient matching algorithms. Our 8-point approach covers data preprocessing, blocking, record adjudication, linkage evaluation, and reviewer characteristics. This framework can help record linkage method developers provide necessary transparency when creating and validating gold standard reference matching data sets. In turn, this transparency will support both the internal and external validity of recording linkage studies and improve the robustness of new record linkage strategies.
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    Evaluating Two Approaches for Parameterizing the Fellegi-Sunter Patient Matching Algorithm to Optimize Accuracy
    (Medinfo conference proceedings, 2019-08-25) Grannis, Shaun; Kasthurirathne, Suranga; Bo, Na; Huiping, Xu
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    Evaluation of real-world referential and probabilistic patient matching to advance patient identification strategy
    (Oxford University Press, 2022) Grannis, Shaun J.; Williams, Jennifer L.; Kasthuri, Suranga; Murray, Molly; Xu, Huiping; Medicine, School of Medicine
    Objective: This study sought both to support evidence-based patient identity policy development by illustrating an approach for formally evaluating operational matching methods, and also to characterize the performance of both referential and probabilistic patient matching algorithms using real-world demographic data. Materials and methods: We assessed matching accuracy for referential and probabilistic matching algorithms using a manually reviewed 30 000 record gold standard reference dataset derived from a large health information exchange containing over 47 million patient registrations. We applied referential and probabilistic algorithms to this dataset and compared the outputs to the gold standard. We computed performance metrics including sensitivity (recall), positive predictive value (precision), and F-score for each algorithm. Results: The probabilistic algorithm exhibited sensitivity, positive predictive value (PPV), and F-score of .6366, 0.9995, and 0.7778, respectively. The referential algorithm exhibited corresponding sensitivity, PPV, and F-score values of 0.9351, 0.9996, and 0.9663, respectively. Treating discordant and limited-data records as nonmatches increased referential match sensitivity to 0.9578. Compared to the more traditional probabilistic approach, referential matching exhibits greater accuracy. Conclusions: Referential patient matching, an increasingly popular method among health IT vendors, demonstrated notably greater accuracy than a more traditional probabilistic approach without the adaptation of the algorithm to the data that the traditional probabilistic approach usually requires. Health IT policymakers, including the Office of the National Coordinator for Health Information Technology (ONC), should explore strategies to expand the evidence base for real-world matching system performance, given the need for an evidence-based patient identity strategy.
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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, 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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    Patient-Centered Data Home: A Path Towards National Interoperability
    (Frontiers Media, 2022-07-13) Williams, Karmen S.; Grannis, Shaun J.; Medicine, School of Medicine
    Objective: National interoperability is an agenda that has gained momentum in health care. Although several attempts to reach national interoperability, an alerting system through interconnected network of Health Information Exchange (HIE) organizations, Patient-Centered Data Home (PCDH), has seen preliminary success. The aim was to characterize the PCDH initiative through the Indiana Health Information Exchange's participation in the Heartland Region Pilot, which includes HIEs in Indiana, Ohio, Michigan, Kentucky, and Tennessee. Materials and methods: Admission, Discharge, and Transfer (ADT) transactions were collected between December 2016 and December 2017 among the seven HIEs in the Heartland Region. ADTs were parsed and summarized. Overlap analyses and patient matching software were used to characterize the PCDH patients. R software and Microsoft Excel were used to populate descriptive statistics and visualization. Results: Approximately 1.5 million ADT transactions were captured. Majority of patients were female, ages 56-75 years, and were outpatient visits. Top noted reasons for visit were labs, screening, and abdominal pain. Based on the overlap analysis, Eastern Tennessee HIE was the only HIE with no duplicate service areas. An estimated 80 percent of the records were able to be matched with other records. Discussion: The high volume of exchange in the Heartland Region Pilot established that PCDH is practical and feasible to exchange data. PCDH has the posture to build better comprehensive medical histories and continuity of care in real time. Conclusion: The value of the data gained extends beyond clinical practitioners to public health workforce for improved interventions, increased surveillance, and greater awareness of gaps in health for needs assessments. This existing interconnection of HIEs has an opportunity to be a sustainable path toward national interoperability.