Machine Learning as a Catalyst for Value-Based Health Care
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SYSTEMS-LEVEL QUALITY IMPROVEMENT
Machine Learning as a Catalyst for Value-Based Health Care Matthew G. Crowson 1,2
&
Timothy C. Y. Chan 2
Received: 23 March 2020 / Accepted: 15 July 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Recent estimates of waste in health care spending in the United States have reached a threshold of one trillion dollars [1], equivalent to the gross domestic product of the 17th largest economy in the world. The major contributors to wasteful spending span failures of care delivery, over-treatment and low-quality care, fraud, administrative complexity and redundancies, inadequate care coordination, and widely variable resource pricing [1]. Determining the factors that drive wasteful spending is a complex problem since waste can arise anywhere from daily individual decisions made by clinicians and their allies to decisions made by health authorities and legislators at the regional or national systems-level. Considering only individual clinical encounters between clinicians and patients, high quality care demands seamless integration of rapidly advancing clinical knowledge, compassionate care, and electronic data all within a fifteen-minute visit. Efficiently securing a diagnosis, work-up, and treatment plan consistent with today’s gold standard of care in this continuously evolving and demanding environment increases the probability of error. An error in this pathway such as a missed diagnosis, an inappropriate imaging study, or an ineffective treatment adds to the waste tally. Ultimately, the complexity of modern medicine leads to error resulting in waste, which leads to poor health care value. In consideration of the journal reviewing and editing my submission, the authors undersigned transfers, assigns and otherwise conveys all copyright ownership in the event that such work is published. This article is part of the Topical Collection on Systems-Level Quality Improvement * Matthew G. Crowson [email protected] 1
Department of Otolaryngology-Head & Neck Surgery, Sunnybrook Health Sciences Center, University of Toronto, Toronto, Ontario, Canada
2
Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
The complexity of modern health care delivery and the dire consequences of persistent wasteful care have led to the rising popularity of value-based care models, which are centered on the principle that optimal health care value is achieved when patient outcomes are maximized per unit cost to deliver those outcomes [2]. If we can improve clinical outcomes without increasing costs, or preferably at lower cost, then we progress towards the goal of higher value care. Several value-inspired delivery models have emerged including regional patientcentered care homes, system-wide accountable care organizations, and bundled payments for diseases or intervention packages. Despite their promise, these solutions have yet to address the complexity of individual decision making in a large lake of data. The probabilit
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