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Studies in big data

Quantum Computing for Health Care: A Review on Implementation Trends and Recent Advances

Avinash Kumar, Bharat Bhushan, Sonal Shriti, Parma Nand

Published Jan 1, 2022• 28 citations
Publisher
DOI
10.1007/978-981-19-0924-5_2

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Drug Utilization Review (DUR) is needed within health care to assess prescribing, dispensing, and medication practices, and to determine if those practices are safe, effective, and affordable.The scale of DUR is currently growing, and will continue to grow with the availability of electronic health records (EHR), claims and real world evidence (RWE).As this scale increases, so do the challenges surrounding contracting with high dimensional data and identifying intricate relationships (e.g.polypharmacy, drug-disease interactions).These challenges are in part due to the current paradigm of classical computers.Quantum Computing (QC) creates the opportunity to advanced these challenges due to its ability to work through massive amounts of information and solve problems on the fly.Under this premise, this work explores the potential role of QC, specifically hybrid quantum-classical models, in the advancement of large scale DUR.This includes the current state of the QC drug discovery and computational chemistry DUR, the application QC could provide to several key DUR challenges, and the creation of a conceptual model which incorporates QC into the current state of DUR.The work also discusses the current state technical, regulatory, and practical impediments to the QC use case within health care, the foundational research needed to closed these gaps, and the use case benefits the work seeks to enable.Ultimately, this work creates a path to the integration of QC and the optimization of drug utilization practices to improve health systems cost effectiveness and patient outcomes.
Quantum computing has heralded a new era of computational capabilities, enabling the resolution of complex problems that are intractable for classical computers. The health care sector, renowned for its data-rich and computationally intensive challenges, stands to benefit significantly from quantum-based algorithms. This research paper surveys the current landscape of quantum algorithm applications in health care, emphasizing their potential in simulation modeling, decision support, adversarial robustness, and cross-domain recommendations. By integrating findings from recent literature, this survey critically examines both the opportunities and limitations inherent to quantum-driven approaches in health care. The paper provides an in-depth literature review, methodology for categorizing quantum algorithmic applications, a discussion of empirical and simulated results, and concludes with insights for future research. This work aims to serve as a comprehensive reference for researchers and practitioners seeking to understand and develop quantum-based solutions in health care.