Decision Support

While RPA has proved its success for some administrative functions, other technologies are emerging as options to help address the worker shortage and reduce workload in clinical and operational areas.

In the COVID-19 era, health systems recognize that existing data infrastructure is inadequate. Here are three things large datasets need to be useful.

Health systems that refuse to see themselves as engineering houses risk falling behind in their ability to properly leverage artificial intelligence and machine learning.

As COVID-19 continues to surge in Los Angeles, LANES is enabling free-flowing data insights – medical, behavioral and socioeconomic ─ to close information gaps and improve clinical decision support for better outcomes.

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.