The Probabilistic Sentinel System: A Formally Specified Scoring Engine for Privacy-Safe Member Identity Resolution in Healthcare MDM
Keywords:
Fellegi-Sunter Model; Healthcare Identity Resolution; HIPAA Privacy Risk; Master Data Management; Probabilistic Record LinkageAbstract
Healthcare payers routinely fail to unify a single member's history across enrollment, claims, pharmacy, and care management systems, and the operational cost of that failure falls into two opposite failure modes: fragmentation that hides a member's full history, and over-consolidation that can expose Protected Health Information (PHI) to the wrong record. This article presents the Probabilistic Sentinel System (PSS), a practitioner-informed scoring and governance framework for managing that tradeoff in healthcare payer master data management (MDM). PSS extends the Fellegi-Sunter probabilistic linkage model with differential attribute weighting across eight member identifiers, an explicit negative penalty for Social Security Number (SSN) conflicts distinct from a missing SSN, calibrated decision thresholds separating auto-link, manual-review, and auto-reject zones, and an override layer that routes behavioral health, HIV, and substance-use records to human review regardless of numeric score. The framework is motivated by MDM defect patterns drawn from payer operations, but all quantitative results in this paper come from a fully synthetic payer-style benchmark containing 101,000 labeled candidate pairs. The synthetic evaluation supports reviewer inspection through confidence intervals, threshold tables, subgroup fairness results, candidate-pair examples, and audit-log examples without exposing PHI, member-level records, steward notes, proprietary matching rules, or internal governance artifacts.
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References
A. Haug, A. M. Staskiewicz, and L. Hvam, "Strategies for master data management: A case study of an international hearing healthcare company," Information Systems Frontiers, vol. 25, no. 5, pp. 1903-1923, 2023.
U.S. Department of Health and Human Services, "The HIPAA Privacy Rule," HHS, Washington, DC, USA, 2024.
U.S. Department of Health and Human Services, Office for Civil Rights, "Annual report to Congress on HIPAA Privacy, Security, and Breach Notification Rule compliance for calendar year 2022," HHS, Washington, DC, USA, submitted Feb. 14, 2024.
U.S. Government Accountability Office, "Electronic health information: HHS needs to improve communications for breach reporting," GAO, Washington, DC, USA, Rep. GAO-22-105425, 2022.
I. P. Fellegi and A. B. Sunter, "A theory for record linkage," Journal of the American Statistical Association, vol. 64, no. 328, pp. 1183-1210, 1969.
B. Williams, "Record linkage in statistical sampling: Past, present, and future," in Recent Advances on Sampling Methods and Educational Statistics, Cham, Switzerland: Springer, 2022, ch. 9.
M. A. Jaro, "Advances in record-linkage methodology as applied to matching the 1985 census of Tampa, Florida," Journal of the American Statistical Association, vol. 84, no. 406, pp. 414-420, 1989.
W. E. Winkler, "String comparator metrics and enhanced decision rules in the Fellegi-Sunter model of record linkage," in Proc. Section on Survey Research Methods, American Statistical Association, 1990, pp. 354-359.
V. Christophides, V. Efthymiou, T. Palpanas, G. Papadakis, and K. Stefanidis, "An overview of end-to-end entity resolution for big data," ACM Computing Surveys, vol. 53, no. 6, Art. no. 127, 2020.
A. Gkoulalas-Divanis, D. Vatsalan, D. Karapiperis, and M. Kantarcioglu, "Modern privacy-preserving record linkage techniques: An overview," IEEE Transactions on Information Forensics and Security, vol. 16, pp. 4966-4987, 2021.
X. Li, H. Xu, and S. Grannis, "The data-adaptive Fellegi-Sunter model for probabilistic record linkage: Algorithm development and validation for incorporating missing data and field selection," Journal of Medical Internet Research, vol. 24, no. 9, e33775, 2022.
W. Nelson, N. Khanna, M. Ibrahim, J. Fyfe, M. Geiger, K. Edwards, and J. Petch, "Optimizing patient record linkage in a master patient index using machine learning: Algorithm development and validation," JMIR Formative Research, vol. 7, e44331, 2023.
H. Blake, L. Sharples, K. Harron, J. van der Meulen, and K. Walker, "Linkage of national clinical datasets without patient identifiers using probabilistic methods," International Journal of Population Data Science, vol. 7, no. 3, 2022.
N. Wu, D. Vatsalan, S. Verma, and M. A. Kaafar, "Fairness and cost constrained privacy-aware record linkage," IEEE Transactions on Information Forensics and Security, vol. 17, pp. 2644-2656, 2022.
V. Efthymiou, K. Stefanidis, E. Pitoura, and V. Christophides, "FairER: Entity resolution with fairness constraints," in Proc. 30th ACM Int. Conf. Information and Knowledge Management (CIKM 2021), 2021.
C. Makri, A. Karakasidis, and E. Pitoura, "Towards a more accurate and fair SVM-based record linkage," in Proc. 2022 IEEE Int. Conf. Big Data, 2022, pp. 4691-4699.
Y. Deng, L. P. Gleason, A. Culbertson et al., "Evolving availability and standardization of patient attributes for matching," Health Affairs Scholar, vol. 1, no. 4, qxad047, 2023.
U.S. Department of Health and Human Services, "The HIPAA Security Rule," HHS, Washington, DC, USA, 2024.
A. Vidanage, T. Ranbaduge, P. Christen, and R. Schnell, "A taxonomy of attacks on privacy-preserving record linkage," Journal of Privacy and Confidentiality, vol. 12, no. 1, 2022.
IEEE Standards Association, "IEEE recommended practice for the quality management of datasets for medical artificial intelligence," IEEE Std 2801-2022, 2022.
R. Miller, H. Whelan, M. Chrubasik, D. Whittaker, P. Duncan, and J. Gregorio, "A framework for current and new data quality dimensions: An overview," Data, vol. 9, no. 12, p. 151, 2024.
A. A. Mamun, S. Azam, and C. Gritti, "Blockchain-based electronic health records management: A comprehensive review and future research direction," IEEE Access, vol. 10, pp. 5768-5789, 2022.
A. Ahmad, M. Saad, M. Al Ghamdi, D. Nyang, and D. Mohaisen, "BlockTrail: A service for secure and transparent blockchain-driven audit trails," IEEE Systems Journal, vol. 16, no. 1, pp. 1367-1378, 2022.
A. Haddad, M. H. Habaebi, M. R. Islam, N. F. Hasbullah, and S. A. Zabidi, "Systematic review on AI-blockchain based e-healthcare records management systems," IEEE Access, vol. 10, 2022.
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