According to the National Academy of Medicine, waste in healthcare is widespread and is estimated to be about a fourth of all the money spent on healthcare each year, a staggering $765 billion annually. One of the key areas of waste is unneeded testing or routine tests that are rarely used. The number of tests can add up for an individual patient and result in significant costs that do not contribute to the quality of care or positive patient outcomes.
AI based solutions can be used to help clinicians make better decisions by narrowing the types of tests that are likely to be useful for a patient. AI models can be created using volumes of patient information from healthcare systems together with data from pharmaceutical companies to predict likely test results a given patients. This model is then deployed into an AI-driven application that can provide indications of which tests are likely to produce definitive or valuable results based on the patient’s medical history and current symptoms. With this knowledge, the clinician can pursue treatments with the best outcomes and minimize the number of tests, which saves time and reduces costs to the patient.
The mission at H2O.ai is to democratize AI for all so that more people across industries can use the power of AI to solve business and social challenges. The healthcare industry is a key focus for the company with an initiative to help develop AI healthcare solutions including dedicated, experienced resources for customers, driving healthcare AI events and meetups for healthcare professionals, and membership in Health IT Now, the leading coalition of patient groups, provider organizations, employers, insurers, and other stakeholders. H2O.ai is already working with top healthcare companies including Change Healthcare, Armada Health, Kaiser Permanente, and HCA, and its products include industry leading features for machine learning interpretability required by the healthcare industry for compliance purposes.
Related Case Studies
Director, Change Healthcare
"H2O has been the driver for building models at scale. We are talking about billions of claims. You can't do this with standard off the shelf open source techniques. "Watch the Video