Decision Support

CIOs need to look beyond just EHRs and explore stand-alone platforms to enhance care delivery – keeping focused on reliable patient data and streamlined clinical workflows.

From pandemic-necessitated go-live pauses to major rip-and-replaces, FHIR-based efficiencies to interoperability strides, digital records are evolving.

Whether assessing vaccine safety and efficacy, assisting with X-ray readings or tracking communities' vulnerability to COVID-19, artificial intelligence has been put to work in new and innovative ways throughout the pandemic.

The pandemic’s impact will be shown on a per-hospital basis, enabling researchers, policymakers and others to have greater insights into local healthcare response efforts.

The New York health system has made AI and automation central to its mission, embedding algorithms into a wide array of workflows to target dozens of improvement initiatives.

From genetic sequencing to symptom tracking to vaccine development, machine learning algorithms have been instrumental in helping uncover hidden clues about the novel coronavirus, says Cris Ross.

An Intel survey of healthcare decision-makers suggests great excitement for more widespread artificial intelligence and machine learning adoption, but also some skepticism.

An integrated coaction model, or iCaM, is ideal for addressing complex multispecialty parameters associated with health inequities and COVID-19.

Social Determinants of Health Analytics is meant as a resource to help health systems plan for and implement community programs as part of their population health management efforts.

Researchers say the new tools, developed using EHR data from the pandemic's first wave, can forecast short- and medium-term risks for patients over the course of their hospitalizations.