Governance of Big Data in Pharmacy Patient Profiling

Published Paper PDF: View PDF

Confirmation Letter: View

Feng Li

Independent Researcher

Haidian District, Beijing, China (CN) – 100871

Abstract— The growing availability of pharmacy data—from e-prescriptions, dispensing logs, medication therapy management (MTM) notes, claims, wearables, and patient-reported outcomes—has enabled increasingly granular patient profiling to predict adherence risk, optimize therapy, and support pharmacovigilance. Yet these capabilities introduce substantial governance challenges: privacy and consent, lawful and ethical purposes, data quality and provenance, algorithmic fairness and explainability, security and access control, and continuous accountability. This manuscript proposes a comprehensive governance framework tailored to big-data-driven pharmacy patient profiling and outlines a pragmatic study protocol for multi-site implementation using privacy-preserving analytics.

We synthesize the literature on health data governance, model risk management, and fairness-aware machine learning to articulate clear roles, processes, and technical controls across the data lifecycle. The methodology integrates stakeholder mapping, data inventory and classification, risk assessment, consent design, de-identification, federated learning with differential privacy, model cards, bias audits, and independent oversight. A pilot protocol for a regional pharmacy network is presented, covering eligibility, data schema, federated training, evaluation metrics (AUC, calibration, demographic parity difference), incident response, and audit procedures. Anticipated results include improved prediction performance with reduced re-identification risk, higher data quality scores, transparent model documentation, and narrowed disparity gaps across demographic groups. The paper concludes with recommendations for scaling governance, aligning with international standards and national regulations, and establishing measurable key performance indicators (KPIs) so that pharmacy innovations remain trustworthy, equitable, and compliant while delivering clinical value.

Keywords— big data; pharmacy; patient profiling; data governance; privacy; fairness; federated learning; differential privacy; model risk management; pharmacovigilance

References

Allen, J., Hutchinson, A. M., Brown, R., Livingston, P. M., & Usera, R. (2020). Quality and safety of big data in healthcare: A systematic review. BMC Medical Informatics and Decision Making, 20(1), 90. https://doi.org/10.1186/s12911-020-01168-5
Belle, A., Thiagarajan, R., Soroushmehr, S. M. R., Navidi, F., Beard, D. A., & Najarian, K. (2015). Big data analytics in healthcare. BioMed Research International, 2015, 370194. https://doi.org/10.1155/2015/370194
Benjamens, S., Dhunnoo, P., & Mesko, B. (2020). The state of artificial intelligence-based FDA-approved medical devices and algorithms: An online database. NPJ Digital Medicine, 3(1), 118. https://doi.org/10.1038/s41746-020-00324-0
Bietz, M. J., Bloss, C. S., Calvert, S., Godino, J. G., Gregory, J., Claffey, M. P., … & Patrick, K. (2016). Opportunities and challenges in the use of personal health data for health research. Journal of the American Medical Informatics Association, 23(e1), e42–e48. https://doi.org/10.1093/jamia/ocv118
Boumans, R., & Trappenburg, M. (2021). Patient profiling and data-driven medicine: Ethical concerns and governance needs. Medicine, Health Care and Philosophy, 24(4), 657–667. https://doi.org/10.1007/s11019-021-10033-5
Chen, M., Hao, Y., Cai, Y., Wang, Y., & Zhang, K. (2017). Privacy-preserving data access for big data in healthcare. IEEE Network, 31(1), 46–50. https://doi.org/10.1109/MNET.2017.1500180NM
Costa, F. F. (2014). Big data in biomedicine. Drug Discovery Today, 19(4), 433–440. https://doi.org/10.1016/j.drudis.2013.10.012
El Emam, K., Rodgers, S., & Malin, B. (2015). Anonymising and sharing individual patient data. BMJ, 350, h1139. https://doi.org/10.1136/bmj.h1139
Floridi, L., & Taddeo, M. (2016). What is data ethics? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2083), 20160360. https://doi.org/10.1098/rsta.2016.0360
Froomkin, A. M., Kerr, I., & Pineau, J. (2019). When AIs outperform doctors: Confronting the challenges of a tort-induced over-reliance on machine learning. Arizona Law Review, 61(1), 33–99.
Habli, I., Lawton, T., Porter, Z., & Batalden, P. (2020). Artificial intelligence in health care: Accountability and safety. Bulletin of the World Health Organization, 98(4), 251–256. https://doi.org/10.2471/BLT.19.237487
Haug, C. J., Drazen, J. M., & Morrissey, S. (2019). Artificial intelligence and machine learning in clinical medicine, 2020. New England Journal of Medicine, 382(9), 791–793. https://doi.org/10.1056/NEJMe1916485
Krittanawong, C., Johnson, K. W., Rosenson, R. S., Wang, Z., Aydar, M., & Halperin, J. L. (2019). Deep learning for cardiovascular medicine: A practical primer. European Heart Journal, 40(25), 2058–2073. https://doi.org/10.1093/eurheartj/ehz056
Lazer, D., Pentland, A. S., Adamic, L., Aral, S., Barabási, A. L., Brewer, D., … & Van Alstyne, M. (2009). Social science: Computational social science. Science, 323(5915), 721–723. https://doi.org/10.1126/science.1167742
Mittelstadt, B. D. (2019). Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, 1(11), 501–507. https://doi.org/10.1038/s42256-019-0114-4
Mooney, S. J., & Pejaver, V. (2018). Big data in public health: Terminology, machine learning, and privacy. Annual Review of Public Health, 39, 95–112. https://doi.org/10.1146/annurev-publhealth-040617-014208
Rumbold, J. M. M., & Pierscionek, B. K. (2017). The effect of the general data protection regulation on medical research. Journal of Medical Internet Research, 19(2), e47. https://doi.org/10.2196/jmir.7108
Shabani, M. (2019). Blockchain-based platforms for genomic data sharing: A de-centralized approach in response to the governance problems? Journal of the American Medical Informatics Association, 26(1), 76–80. https://doi.org/10.1093/jamia/ocy149
Vayena, E., Dzenowagis, J., Brownstein, J. S., & Sheikh, A. (2018). Policy implications of big data in the health sector. Bulletin of the World Health Organization, 96(1), 66–68. https://doi.org/10.2471/BLT.17.197426
Zhang, Y., Qian, C., & Zhao, J. (2021). Big data governance in healthcare: Framework, challenges, and future directions. Health Information Science and Systems, 9(1), 14. https://doi.org/10.1007/s13755-021-00141-3