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Four ways artificial intelligence is revolutionizing health care

March 01, 2017
Health IT
From the March 2017 issue of HealthCare Business News magazine

1: Clinical/surgical variation

One of the best opportunities for using machine intelligence to improve care is the development of clinical pathways. By guiding clinicians to follow best practices through each step of care delivery, clinical pathways ensure that all patients receive consistent high-quality care at the lowest possible cost.

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Variation is a necessary and natural element in most health care delivery because every patient is unique. A certain amount of informed variation is critical to driving innovation in clinical practices. However, variation caused by gaps in knowledge or lack of data-based evidence makes it difficult — if not impossible — for health systems to reduce costs, improve patient outcomes and quality of care, and minimize medical errors. Eliminating unnecessary variation is fundamental to achieving value-based care.

Health reform also has upped the ante. The Department of Health & Human Services recently announced an accelerated timeline for tying payments to quality and value. In addition, the Centers for Medicare & Medicaid Services (CMS) published its final rule for a mandatory bundled payment model for total knee and hip replacements. It can be expected that CMS will expand the list of procedures for which it mandates alternative payment models.

Since the early 1990s, hospitals and health systems have used clinical pathways as a way to reduce unwarranted variation. Sometimes branded as integrated care process models or collaborative care pathways, they all share the same basic structure: a multidisciplinary team of providers use peer-reviewed literature and patient population data to develop and validate best-practice protocols and guidance for specific conditions, treatments and outcomes.

The case of Mercy Health in St. Louis is a perfect example of machine learning in action. In 2014, Mercy tested a machine learning application to recreate and improve upon a clinical pathway for total knee replacement surgery. Drawing from Mercy’s integrated electronic medical record (EMR), the application grouped data from a highly complex series of events related to the procedure, discovered natural variations in clinical practice of the procedure across the health system and identified those flavors associated with the best outcomes.

It was then possible to adapt other methods from biology and signals processing, and incorporate extant evidence-based guidelines into the problem of determining an optimal way to perform the procedure — which drugs, tests, implants and other processes contribute to that optimal outcome. It also was possible to continue to monitor and measure adherence against these standardized pathways. Moreover, predictive machine learning methods can be used to examine the impact of potential pathway modifications to outcomes, enabling a variety of what-if scenarios.

What this analysis revealed was an unforeseen and groundbreaking care pathway for high-quality total knee replacement. The common denominator between all patients with the shortest length of stay (LOS) and best outcomes was administration of pregabalin — a drug generally prescribed for shingles. A group of four physicians had seen something in the medical literature that led them to believe that administering the drug prior to surgery would inhibit postoperative pain, reduce opiate usage and produce faster ambulation. It did. Mercy never would have discovered this best practice using traditional approaches. This single procedure was worth over $1 million per year for Mercy in direct costs.

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