This appeared a few days ago.
Guthrie to use AI-based system to help anticipate sepsis
September 06, 2019, 3:05 p.m. EDT
A four-hospital health system will implement the POC Advisor clinical intelligence platform of Wolters Kluwer Health to aid in quicker detection and treatment of sepsis.
Combatting sepsis is an ever-present challenge, says Terri Couts, vice president of Epic program applications at Guthrie, an integrated delivery system serving parts of Pennsylvania and New York. Based on feedback from providers, the organization sought a sepsis detection product with meaningful alerts that provide more accuracy and specificity than what could be achieved by the electronic health record, she adds.
“Using natural language processing with POC Advisor can help us maximize the value of our EHR investment by using both patient data and extensive clinician notes to trigger alerts accurately and earlier, so our care team can effectively intervene to improve sepsis outcomes,” Couts explains.
More stories here:
There was also this last week:
Duke leverages AI to identify patients with early stage sepsis
September 03, 2019, 1:27 a.m. EDT
Time is of the essence when it comes to detecting sepsis—the deadly condition has no clear time of onset and no clear biomarker, making it difficult to diagnose.
To address the problem, the Duke Institute for Health Innovation has developed an artificial intelligence system to help clinicians identify patients in the early stages of sepsis so that they can intervene before it’s too late.
The early warning system leverages a machine learning model, a custom dashboard to present risk scores, and a rapid response team to monitor patients at-risk of sepsis and deliver appropriate treatment.
The AI system, called Sepsis Watch, was initially implemented in November 2018 at Duke University Hospital’s emergency department—this past May, the pilot phase was completed.
Will Ratliff, innovation program manager at the Duke Institute for Health Innovation, reported the pilot’s preliminary bundle compliance results for patients with sepsis at last month’s Machine Learning for Health Care conference in Ann Arbor, Mich.
The pilot’s primary outcome measures were the rate of Centers for Medicare and Medicaid Services bundle completion for patients with sepsis (within 96 hours of emergency department arrival), and the proportion of patients with sepsis that complete CMS treatment bundle.
More here:
These are only 2 of a number of similar articles I have seen over the last few months – and the systems do seem to be working and making a difference.
The question. Which hospital in Australia are leveraging their EMRs with similar decision support. Looks like many more than are should be don’t you think? Sepsis is such a lethal condition if not recognized and treated early the onus is on the health departments to get on with it!
David.
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