Abstract
Healthcare policy evaluation is a time-consuming, challenging process due to the complexity of the US healthcare system which is comprised of both public and private payers; a variety of healthcare suppliers including doctors, medical device companies, and pharmacies; and patients from different insurance coverages and socioeconomic backgrounds. Systems engineering processes are intended for complex systems and are ideal for addressing healthcare policy. Specifically, model-based systems engineering (MBSE) is used to increase traceability with its model-centric approach and can be used to increase understanding of the healthcare system. In this paper, we attempt to exploit digital twin philosophy of MBSE to understand a US healthcare system as a complex system. We focus our efforts in building a digital doppelgänger which reflects most aspects of the healthcare systems digitally, but is not an exact digital twin. The doppelgänger helps navigate around the medical privacy laws of the US healthcare system and runs some analysis on healthcare policy.
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Acknowledgments
We thank our colleagues at MITRE Corporation for their expertise in healthcare and Synthea, especially to Jason Walonoski and Rob Lieberthal. Additional thanks to Robi Scalfani for generating datasets from unreleased branches of Synthea.
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Legaspi, J., Bhada, S.V. (2022). Introducing Digital Doppelgängers for Healthcare Policy Analysis. In: Madni, A.M., Boehm, B., Erwin, D., Moghaddam, M., Sievers, M., Wheaton, M. (eds) Recent Trends and Advances in Model Based Systems Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-82083-1_3
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DOI: https://doi.org/10.1007/978-3-030-82083-1_3
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