Unlocking the power of Machine Learning in health
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.
Health organizations need sustainable solutions, especially in the United States, with its large population and complex payment models. Value-based care (that is, “paying for value, not volume”) is gaining currency.
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’s 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.
As health systems strive to “do better with less” 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.
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