An integrated coaction model, or iCaM, is ideal for addressing complex multispecialty parameters associated with health inequities and COVID-19.
Data from a real-time location system, covering nearly 4.3 million square feet, offers the ability to see patients and staff who may have come into proximity with an infected person.
COVID-19: What is different in our understanding of this pandemic to the accepted wisdom in the spring of 2020?
As we have heard repeatedly over the past few months, there is a need to think of managing this pandemic in the same way we prepare for a marathon rather than a sprint, says Dr Charles Alessi, chief clinical officer, HIMSS.
Data science and clinical teams at PCCI, in collaboration with Parkland informaticists, have developed an AI-driven predictive model that predicts for individual COVID-19 exposure risk, based on population density and their proximity to positive cases.
A healthcare system in which stakeholders share, adopt and apply medical knowledge in real time enables improved care, accelerated workflows, streamlined business processes and a better balance of resources with demand.
The COVID-19 pandemic may be viewed as the single largest disruptor in the history of American healthcare. At Geisinger, the crisis has been a catalyst to accelerate digital transformation.
Learn about the knowledge graph that’s helping map relationships between patents, publications and genes in the battle against the novel coronavirus from Dr Alexander Jarasch at the Federal Republic’s main diabetes research facility, DZD, in Germany.
A proposal: Create a Federal Reserve-type structure for supply chain management of protective equipment, powered by distributed ledger technology.
In a region of 10 million residents, the nonprofit Los Angeles Network for Enhanced Services is helping achieve care coordination, closing care gaps when providers are able to access data at the point of care, using a central interoperable platform.
Fundamental issues related to executive support, staff buy-in and patient risk stratification need to be understood and addressed before machine learning applications can help with population health goals.