Making Strides Toward Greater Success In Virtual Care
By Josh Weiner, CEO, SR Health by Solutionreach
A well-known proverb posits that necessity is the mother of invention. And while telehealth slightly predates the worldwide COVID-19 calamity, we were sure glad and relieved the technology was available when the pandemic threatened in-person care.
Before COVID, in-office care was the only viable option healthcare organizations had for most appointments. But plying telehealth as a safe delivery method during COVID protocols opened a broad range of care and revenue possibilities for providers. Telehealth use by providers exploded from 22 percent of physicians offering it in 2019 to 80 percent in 2020.
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HSR.
health s GeoHealth Platform is the only solution that merges data on social determinants of health with social media data, health outcomes, and costs to extract clinical insights. In doing so, the cloud-based analytic and visualization platform offers point-of-care decision support, anticipates future healthcare delivery needs, and serves the diverse needs of health systems, health plans, regulators and insurers.
As the COVID-19 crisis swept the nation last spring, HSR.
health successfully adapted their Health Risk Index model to improve pandemic emergency response efforts with hyper-focused risk indices that agencies have adopted worldwide to address critical needs tied to transmission, hospitalization, mortality, medical supply, testing, vaccination, and more.
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s we close out the year, we asked several healthcare executives to share their predictions and trends for 2021.
Kimberly Powell, Vice President & General Manager, NVIDIA Healthcare
Federated Learning: The clinical community will increase their use of federated learning approaches to build robust AI models across various institutions, geographies, patient demographics, and medical scanners. The sensitivity and selectivity of these models are outperforming AI models built at a single institution, even when there is copious data to train with. As an added bonus, researchers can collaborate on AI model creation without sharing confidential patient information. Federated learning is also beneficial for building AI models for areas where data is scarce, such as for pediatrics and rare diseases.