The following is a guest blog post by Mary Hardy, Vice President of Healthcare for Ayasdi.
Variation is a natural element in most healthcare delivery. After all, every patient is unique. But unwarranted clinical variation—the kind that results from a lack of systems and collaboration or the inappropriate use of care and services—is another issue altogether.
Healthcare industry thought leaders have called for the reduction of such unwarranted variation as the key to improving the quality and decreasing the cost of care. They have declared, quite rightly, that the quality of care an individual receives should not depend on geography. In response, hospitals throughout the United States are taking on the significant challenge of understanding and managing this variation.
Most hospitals recognize that the ability to distill the right insights from patient data is the catalyst for eliminating unwarranted clinical variation and is essential to implementing care models based on value. However, the complexity of patient data—a complexity that will only increase with the impending onslaught of data from biometric and personal fitness devices—can be overwhelming to even the most advanced organizations. There aren’t enough data scientists or analysts to make sense of the exponentially growing data sets within each organization.
Enter machine learning. Machine learning applications combine algorithms from computational biology and other disciplines to find patterns within billions of data points. The power of these algorithms enables organizations to uncover the evidence-based insights required for success in the value-based care environment.
Machine Learning and the Evolutionary Leap in Clinical Pathway Development
Since the 1990s, provider organizations have attempted to curb unwarranted variation by developing clinical pathways. A multi-disciplinary 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.
However, the process is burdened by significant limitations. Pathways often require months or years to research, build, and validate. Additionally, today’s clinical pathways are typically one-size-fits-all. Health systems that have the resources to do so often employ their own experts, who review research, pull data, run tables and come to a consensus on the ideal clinical pathway, but are still constrained by the experts’ inability to make sense of billions of data points.
Additionally, once the clinical pathway has been established, hospitals have few resources for tracking the care team’s adherence to the agreed-upon protocol. This alone is enough to derail years of efforts to reduce unwarranted variation.
Machine learning is the evolutionary leap in clinical pathway development and adherence. Acceleration is certainly a positive. High-performance machines and algorithms can examine complex continuously growing data elements far faster and capture insights more comprehensively than traditional or homegrown analytics tools. (Imagine reducing the development of a clinical pathway from months or years to weeks or days.)
But the true value of machine learning is enabling provider organizations to leverage patient population data from their own systems of record to develop clinical pathways that are customized to the organization’s processes, demographics, and clinicians.
Additionally, machine learning applications empower organizations to precisely track care team adherence, improving communication and organization effectiveness. By guiding clinicians to follow best practices through each step of care delivery, clinical pathways that are rooted in machine learning ensure that all patients receive the same level of high-quality care at the lowest possible cost.
Machine Learning Proves its Value
St. Louis-based Mercy, one of the most innovative health systems in the world, used 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 and segmented it. It was then possible to adapt other methods from biology and signals processing to the problem of determining the optimal way to perform the procedure—which drugs, tests, implants and other processes contribute to that optimal outcome. It also was possible to link predictive machine learning methods like regression or classification to perform real-time pathway editing.
The application revealed that Mercy’s patients naturally divided into clusters or groups with similar outcomes. The primary metric of interest to Mercy as an indicator of high quality was length of stay (LOS). The system highlighted clusters of patients with the shortest LOS and quickly discerned what distinguished this cluster from patients with the longest LOS.
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 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.
This innovation was happening in Mercy’s own backyard, and it was undeniably a best practice—the data revealed that each of the best outcomes included administration of this drug. Using traditional approaches, it is highly unlikely that Mercy would have asked the question, “What if we use a shingles drug to improve total knee replacement?” The superior outcomes of four physicians would have remained hidden in a sea of complex data.
This single procedure was worth over $1 million per year for Mercy in direct costs.
What Mercy’s experience demonstrates is that the most difficult, persistent and complex problems in healthcare can resolve themselves through data. The key lies in having the right tools to navigate that data’s complexity. The ability to determine at a glance what differentiates good outcomes from bad outcomes is incredibly powerful—and will transform care delivery.
Mary Hardy is the Vice President of Healthcare for Ayasdi, a developer of machine intelligent applications for health systems and payer organizations.