U.S. Reports

Predict Patient Outcomes And Dramatically Improve Cost Effective Delivery Of Care Report

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Healthcare Disruption Is Underway With Machine Learning Models That Can Predict Patient Outcomes And Dramatically Improve Cost Effective Delivery Of Care Amadeus Intelligence model to screen patients for the life threatening condition of Abdominal Aortic Aneurysm (AAA). The Machine Learning Approach Healthcare has become an industry which hosts immense quantities of data that's not being used to its full potential. It's essential to ensure that tools are developed to efficiently process this information and present back meaningful insights for healthcare decision makers to support better outcomes and lower costs of care. Machine learning models have already demonstrated their ability to support healthcare professionals, with providing improved target interventions to the right patients. Research has demonstrated that machine learning models can out- perform existing approaches which predict lack of adherence to medication, chances of readmission, and high cost users by more than 30%. Machine learning will improve the way health systems target limited resources toward those patients with the highest needs, driving better outcomes and reduced costs. The ability to predict patient outcomes and clinical decision support tools can give clinicians context around the data, allowing them to make better and more personalized decisions. Data driven health aims to help clinicians quickly understand the patient, the conditions, and interpret the information for that individual patient. Current Challenges for Healthcare Organizations There is an estimated $1 trillion worth of wastage in the United States Healthcare System. These wasted health dollars can be attributed to many causes, ranging from duplication of administration efforts and lack of adherence to medications, right through to avoidable patient readmissions. Healthcare is experiencing increasing costs, massive pressure on budgets and a projected shortfall in the clinical workforce. The digitization of health records through electronic medical records and health information exchanges hasn't quite delivered yet on promised cost savings. Machine learning represents the biggest opportunity to leverage these existing investments and deliver significant cost reductions for health systems. The current shift from fee-for-service to value-based contracting is driving a fundamental change in the way the health system contracts for, and provides care to, patients. This change creates a renewed focus on the cost effectiveness of interventions and ensuring each intervention is right for each patient. The shift to electronic medical records, combined with advances in genomics, wearable technology and environmental monitoring, has meant there has been an explosion in both published literature and usable data about individual patients. Current models and processes simply can't keep up with the volume of data. The options are either to continue the way health has always been delivered, or utilize technology such as machine learning to consume and process this vast repository of information to produce meaningful insights for healthcare decision makers. There are many applications where machine learning models will help to reduce the cost of healthcare, in this report we discuss exciting projects underway, including the Orion Health Amadeus Intelligence Project for Clinical Decision Support Tools. In this project Orion Health has created an accurate machine learning Why is Machine Learning Important? Orion Health is leading ground-breaking research in machine learning, exploring meaningful ways to minimize waste, reduce operating costs and help clinicians make more accurate decisions at the point of care. Significant amounts of data exist that will support better decision making, drawing on key information from entire populations to treat and manage a person's health. The healthcare sector is being transformed by the ability to record massive amounts of information about patients and their environments. Machine learning provides a way to find patterns and reason about data, which enables healthcare professionals to move to personalized medicine. There are many possibilities for how machine learning can be used in healthcare, and all of them depend on having sufficient data and permission to use it.

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