NOVEMBER 2020CIOAPPLICATIONS.COM8omputational health combines availability of more data and advances in software/hardware technologies to solve problems in healthcare with computation-based approaches. The explosion in volume of various kinds of data -- genomic, transcriptomic, proteomic, epigenetic, metabolomic, microbiome, clinical, and behavioral -- is driven by the lowering cost of data acquisition and storage. For example, sequencing a human genome used to cost around $3 billion in the 90's. Today it only costs a few thousand dollars -- a faster decline than what we observed in computing cost depicted by Moore's Law. In addition, advances in software and hardware technologies are rapidly accelerating, particularly around artificial intelligence.The confluence of these trends is transforming healthcare, and companies are harnessing data and applying AI to solve meaningful problems in diagnostics, therapeutics, treatment & care, and clinical & administrative workflows. In diagnostics, researchers are applying deep learning to ultrasound imaging, to help with both acquisition and interpretation of echocardiograms. Other groups are developing blood screening tests and novel computation methods to detect cancer early. In therapeutics, researchers have developed a computational platform that characterizes disease heterogeneity and identifies compounds that will be effective for specific patient sub-populations. Several research groups are also building a number of machine Trends in CDamion NeroDAMION NERO, EX DIRECTOR OF HEOR RESEARCH ANALYTICS, CARDINAL HEALTHIN MY Viewlearning modules to interpret genetic variations and how they impact various biological processes, such as splicing, in the body, discovering new drug targets for nucleic acid therapeutics. Further several companies are using predictive algorithms and machine learning to understand their vastly growing databasesof integrated data such as administrative claims and electronic medical records.As multi-morbid patients continue to grow in volume and make up ever more of the cost of care provided, hospitals are increasingly forced to seek cost-effective, proactive, multi-touch methods to manage these patients. Several companiesare offering services that help Medicare Advantage plans to more accurately adjust risk based on patient records, and other companies are enabling payors to speed up important decisions such as prior authorizations to reduce unnecessary services. Several groups are currently working to fuse multiple data sources to reveal drivers of cost and quality so that clinical teams can deliver the best possible care at the lowest cost. Outside the hospital, we see smart care coordination platforms prevent costly acute care readmissions which account for the bulk of healthcare cost. Companies are using these platforms to help care coordinators detect signs of mental disorders such as depression through automated patient voice analysis, enabling the clinician to kick-off depression protocols potentially even before patients themselves are aware of their depression. Other companies are taking this further, transforming care coordination by taking Computational Healthcare Research and Digital Therapeutics
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