U.S. Reports

Predict Clinical Progression With Machine Learning Report

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The Ability To Predict Clinical Progression With Machine Learning Will Have A Positive Disruption On Value-Based Care Programs, Especially Those Managing Chronically Ill Patients Amadeus Intelligence exciting projects underway, including the Orion Health Amadeus Intelligence Project for Predicting Clinical Progression. In this project Orion Health has created an accurate machine learning model that predicts patient outcomes following a Stroke. The Machine Learning Approach Current evidence suggests machine learning will help predict outcomes for patients with chronic conditions. Diabetes, Cancer and Stroke are all examples of long term conditions where early detection and intervention are highly beneficial. Examples of current machine learning models are based around utilizing the data collected by healthcare organizations to augment their current practices. Hospitals are using support vector machine modelling, using EHR data and risk factors to determine early diagnosis. Hospitals are using machine learning models to empower clinical decision support, where the model can detect patients deteriorating and alert clinicians so they can intervene at an earlier stage to manage these high risk patients. This leads to improved outcomes for high risk patients as they are receiving timely interventions by clinicians. Machine Learning will have a positive disruption on the care of patients with Chronic Disease especially Diabetes. Machine learning models can predict long-term conditions, enabling interventions that could dramatically cut treatment costs and save healthcare organizations billions of dollars each year. Current Challenges for Healthcare Organizations In the United States, Chronic Diseases are the leading cause of death and disability. Amongst the population, as of 2012, about half of all adults—117 million people—had one or more chronic health conditions and one in four adults had two or more chronic health conditions. Seven of the top 10 causes of death in 2014 were Chronic Diseases, Heart Disease and Cancer which, combined, accounted for 46% of all deaths. Diabetes is of major concern, there are an estimated 24 million people with Diabetes in the United States, the leading cause of kidney failure, non-injury related lower-limb amputations, and new cases of blindness among adults. Predicting patients' risk of developing these chronic health issues has great benefits for healthcare organizations. Firstly, it allows early intervention to occur, giving patients and their healthcare organizations the opportunity to reduce suboptimal outcomes. Secondly at-risk patients can be placed on value-based care programs to manage and promote lifestyle changes that can help to prevent or slow down the development of long-term health issues. The ability to predict clinical disease progression will enable healthcare organizations to develop more targeted care plans, containing healthcare costs and reducing unnecessary escalations into episodic care. Machine learning models have the ability to predict clinical progression of patients, this is especially helpful in the chronically ill. As hospitals strive to improve clinical care management, machine learning can rapidly help identify the right protocols and interventions to create a personalized plan of care that will achieve the best outcomes for patients. The number of patients with Chronic Diseases is rising. This is especially evident in Diabetes. Both the number of diabetics and the associated costs are expected to double in the next 25 years, and without advancements such as machine learning modelling, healthcare organizations will struggle to bear this cost. There are many applications where machine learning models will help to reduce the cost of healthcare, in this report we discuss 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 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 new way to find patterns and reason about data, which enables healthcare professionals to move closer 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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