In this special guest feature, Prashanth Kini, Vice President and Head of Product, Healthcare for Ayasdi provides four real-world examples where machine intelligence is helping provider organizations transform into learning health systems that are continually improving performance. Kini is VP and Head of Product/Healthcare for Ayasdi, a developer of machine intelligent applications for health systems and payer organizations.
Healthcare has mastered the collection and organization of information. Advanced analytics using machine learning applications is the next great frontier in a value-based world.
Healthcare is a data-gathering powerhouse. But the industry has struggled to effectively harness the fire hose of data and translate it into actionable insights to improve outcomes and improve the value of care.
Machine intelligence applications, often referred to as artificial intelligence, offer fresh opportunities to unify and synthesize vast amounts of data across large health systems and deliver prescriptions for better care at a lower price. Machine learning applications can examine complex, continuously growing data elements far faster and capture insights more comprehensively than traditional or homegrown analytics tools.
The Proof is in the pudding
Machine learning techniques are already having a substantial impact. The following are four real-world examples where machine intelligence is helping provider organizations transform into learning health systems that are continually improving performance.
Eliminating clinical/surgical variation
To deliver the best care for every patient, health systems but eliminate wide variation between different practitioners and instead establish best practices and standard protocols.
Mercy Health in St. Louis tested a machine learning application to recreate and improve upon a clinical pathway for total knee replacement surgeryDrawing 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.
The hospital discovered that one small group of doctors had consistently better outcomes with faster ambulation and shorter length of stay. Using machine learning, Mercy uncovered the reason for the better outcomes was the use of a particularly effective painkiller that made it easier for patient to get out of bed sooner.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.
Improving Revenue Cycle Management
From a revenue cycle management perspective, as described earlier, the ability to understand, monitor and manage clinical variation for a variety of episodes of care across the care continuum enables health systems to have a clear line of sight to their performance against bundled payment and other value based arrangements. They will now be able to make the necessary course corrections to minimize an end-of-year shock when payers reconcile performance against contracts.
Mount Sinai hospital in New York recently used machine intelligence to analyze clinical and genomic data from a population of type 2 diabetes (T2D) patients. The unbiased machine intelligence analysis identified heretofore unknown three distinct subtypes of T2D patients with distinct clinical and genomic characteristics. This advanced population stratification capability will inform the design of precision treatment regimens.
Patient Monitoring & TeleHealth
Machine intelligence is also promising for applications in preventive care and telemedicine. A prominent non-profit organization has successfully utilized machine intelligence to distinguish a control set of healthy patients from Parkinson’s patients based purely on smartphone accelerometer and gyroscope sensor data. The organization was also able to identify two distinct sub-cohorts of Parkinson’s patients, again based on the gait patterns revealed in the sensor data.
The ability to detect such differences in disease states holds large promise for the ability to monitor and proactively manage patients at home particularly with debilitating diseases like Parkinson’s where the patient may not always be able to provide the necessary updates on their health.
Better Best Practices, Sooner
The best use of artificial intelligence is to augment, amplify, and guide human intelligence. In healthcare, that means better best practices, sooner. Machine learning tools can deliver faster insights into which processes are working well for which patients and which ones need to be optimized. These distinctions are essential to deliver on the promise of value-based healthcare.
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