EHR mortality prediction model boosts palliative care consults
Powered by predictive analytics, an electronic health record mortality prediction model was able to increase palliative care consultations for hospitalized patients by 74 percent.
That’s the finding of a study, conducted by researchers at the University of Pennsylvania’s Perelman School of Medicine, published in the Journal of General Internal Medicine.
In addition, patients in the study at an urban academic medical center were seen by palliative care earlier in their hospital stay—an average of a day and a half sooner.
“Targeting hospital-based palliative care using an EHR mortality prediction model is a clinically promising approach to improve the quality of care among seriously ill medical patients,” concludes the study. “More evidence is needed to determine the generalizability of this approach and its impact on patient- and caregiver-reported outcomes.”
The system, called Palliative Connect, leverages EHR data and uses machine learning to devise a score based on 30 different factors to determine a patient’s prognosis over the next six months.
Researchers contend that their study represents the “first time a scalable, data-driven prediction system of this kind has been tested in a real clinical setting for palliative care,” noting that “there have been other palliative care triggers used, but few that have been developed from empirical evidence, and even fewer that have been rigorously tested.”
Going forward, they will fine-tune the EHR mortality prediction model. A follow-on study is investigating the perspective of physicians, patients, and palliative care clinicians on consult triggers.
“Our goal is for every seriously ill patient to have a conversation with their clinician about their priorities and wishes, and to document those priorities in the medical record,” said senior author Nina O’Connor, MD, the chief of Palliative Care at Penn Medicine, who evaluated Palliative Connect over an eight-week period. “We think that triggers are allowing us to do that, so we’ll continue to evaluate and refine in order to help more patients.”