After the “Peak of Inflated Expectations” set by vendors of black-box algorithms, healthcare organizations are now learning that building custom AI/ML models, tailored to specific member populations, is the key to success.But what does success look like, and how are organizations ensuring its realization?
Join Ryan Pretnik of KLAS Research and Andrew Eye, CEO of 2022 Best in KLAS Healthcare AI category winner ClosedLoop, to learn how healthcare organizations like yours are rapidly adopting new and custom AI to tackle some of healthcare’s big challenges.
ClosedLoop’s platform ━ purpose-built and dedicated to healthcare ━ combines an intuitive end-to-end machine learning platform with a comprehensive library of healthcare-specific models and features. The platform is designed so that healthcare organizations (HCOs) can leverage the power of AI to address their biggest challenges.
Artificial intelligence (AI) and machine learning (ML) are increasingly used in healthcare to combat unsustainable spending and produce better outcomes with limited resources, but healthcare organizations (HCOs) must take steps to ensure they are actively mitigating and avoiding algorithmic bias.
Implementing a comprehensive strategy to advance health equity is a moral and financial imperative for healthcare organizations (HCOs). Persistent health disparities create preventable suffering and excess costs, are fueled by social determinants of health (SDoH), and consistently disadvantage people of color. Recent studies of racial equity estimate that $135 billion could be saved annually if racial disparities in health were eliminated, including $93 billion in excess costs of care.
COVID-19 simultaneously exacerbated existing health disparities and introduced entirely new ones. The pandemic disproportionately impacted people of color, and due to a combination of persistent health disparities and social determinants of health (SDoH), they are at higher risk for infection, severe illness, and death.
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