[{"innerBlocks":[],"name":"core\/freeform","postId":429,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"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\u2019s the finding of a study, conducted by researchers at the University of Pennsylvania\u2019s Perelman School of Medicine, published<\/a> in the Journal of General Internal Medicine.<\/i> In addition, patients in the study at an urban academic medical center were seen by palliative care earlier in their hospital stay\u2014an average of a day and a half sooner. \u201cTargeting 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,\u201d concludes the study. \u201cMore evidence is needed to determine the generalizability of this approach and its impact on patient- and caregiver-reported outcomes.\u201d 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\u2019s prognosis over the next six months. Researchers contend that their study represents the \u201cfirst time a scalable, data-driven prediction system of this kind has been tested in a real clinical setting for palliative care,\u201d noting that \u201cthere have been other palliative care triggers used, but few that have been developed from empirical evidence, and even fewer that have been rigorously tested.\u201d 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. \u201cOur 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,\u201d said senior author Nina O\u2019Connor, MD, the chief of Palliative Care at Penn Medicine, who evaluated Palliative Connect over an eight-week period. \u201cWe think that triggers are allowing us to do that, so we\u2019ll continue to evaluate and refine in order to help more patients.\u201d","saveContent":"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\u2019s the finding of a study, conducted by researchers at the University of Pennsylvania\u2019s Perelman School of Medicine, published<\/a> in the Journal of General Internal Medicine.<\/i> In addition, patients in the study at an urban academic medical center were seen by palliative care earlier in their hospital stay\u2014an average of a day and a half sooner. \u201cTargeting 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,\u201d concludes the study. \u201cMore evidence is needed to determine the generalizability of this approach and its impact on patient- and caregiver-reported outcomes.\u201d 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\u2019s prognosis over the next six months. Researchers contend that their study represents the \u201cfirst time a scalable, data-driven prediction system of this kind has been tested in a real clinical setting for palliative care,\u201d noting that \u201cthere have been other palliative care triggers used, but few that have been developed from empirical evidence, and even fewer that have been rigorously tested.\u201d 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. \u201cOur 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,\u201d said senior author Nina O\u2019Connor, MD, the chief of Palliative Care at Penn Medicine, who evaluated Palliative Connect over an eight-week period. \u201cWe think that triggers are allowing us to do that, so we\u2019ll continue to evaluate and refine in order to help more patients.\u201d","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

EHR mortality prediction model boosts palliative care consults

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