Unlocking the power of Machine Learning in health<\/p>\n\n\n\n
Health systems all over the world face complex challenges in attempting to deliver high quality, cost-effective care. Factors contributing to this include continual cost increases at rates much higher than inflation and GDP growth rate, a higher prevalence of chronic diseases and their complications, higher use of expensive medical technology, a rise in the proportion of patients over 65, and continuing fragmentation of care delivery.<\/p>\n\n\n\n
Health organizations need sustainable solutions, especially in the United States, with its large population and complex payment models. Value-based care (that is, \u201cpaying for value, not volume\u201d) is gaining currency.<\/p>\n\n\n\n
One important aspect to improving the value proposition is that healthcare should be delivered in the setting (whether primary, secondary or community) that best suits a patient\u2019s needs and delivers care at a sustainable price. Increasingly we recognize that care delivered in the community can meet both goals by providing the care the patient needs in the most cost-effective way.<\/p>\n\n\n\n
As health systems strive to \u201cdo better with less\u201d there is a real opportunity for machine learning approaches to guide the priorities and actions of providers across a population in a highly effective manner.<\/p>\n\n\n\n
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