Analytics
Syapse consolidates and processes real-world patient data from numerous large health systems around the world providing oncology care. Insights derived from this data help those health systems improve their quality of care, strengthen the efficiency of clinical trials and add to the body of clinical knowledge that supports oncology care decisions.
External pressures like reimbursement reduction, clinical quality reporting mandates and price transparency rules further underscore the importance of bringing together diverse data sources.
From the custodian to the CEO, everyone is required to take a homegrown course – created with generative artificial intelligence – to prepare for transformation coming to healthcare, the VP of planning explains.
A new retrospective analysis leveraged the American College of Cardiology algorithm to score cardiac arrest arrivals and predict unfavorable neurological outcomes. It's now available on the internet.
Also, the Korean government is investing $25 million in developing AI for drug discovery.
Compliance requirements have left health plans 'scrambling,' Johns Hopkins says, spurring creation of the AI initiative.
Providers should push hard to maximize the use of generative AI and natural language processing tools – but validate performance for each system rigorously before making it available for widespread use, a physician author says.
Julie Harris, Children's Health Alliance senior director of population health, explains how giving providers more information at the point of care by tracking 35 key quality measures reduces their burdens in meeting value-based incentive targets.
Encryption technology used in elections can potentially improve an AI model's predictive capability without compromising patient data privacy.
Offered in partnership with Novo Nordisk Foundation, the fellowship will focus on technologies that analyze vast amounts of data to increase diagnostic accuracy, speed personalized medicine and improve clinical trials.