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DTSTART;TZID=America/New_York:20260917T120000
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SUMMARY:CMSA National Partnered with League (1 CE) – From Symptom to Signal: What the Evidence Says About AI-Supported Patient Engagement in Cancer\, Palliative\, and Chronic Care
DESCRIPTION:Register Here! \nFrom Symptom to Signal: What the Evidence Says About AI-Supported Patient Engagement in Cancer\, Palliative\, and Chronic Care\nFREE WEBINAR \n1 hr RN\, SW\, CCM \nAbstract: \nCase managers absorb the consequences of a fragmented delivery system. The typical Medicare beneficiary consults seven physicians across four practices annually\, and direct communication between hospital physicians and primary care occurs in as few as 3–20% of discharges. The burden concentrates in the populations case management serves most intensively: 3.85 million adult 30-day readmissions each year at a mean cost of $16\,300; half of older adults attend the emergency department in the final month of life; and approximately half of emergency presentations by patients with cancer are classified as preventable. These pressures coincide with a nursing workforce in which almost 40% report intention to leave within five years\, and with 63 million family caregivers — the majority without formal medical training — sustaining care at home. Patients\, meanwhile\, have adopted new tools independently: most Americans who seek health information online now regard AI-generated content as at least somewhat trustworthy. \nThe trial evidence is more instructive than either enthusiasm or skepticism alone. Electronic patient-reported outcome monitoring in oncology prolonged survival and reduced emergency attendances; early palliative care delivered by video proved equivalent to in-person consultation; and the large remote-monitoring trials in heart failure and COPD that showed no benefit shared a common design flaw — no clinician with authority to act was connected to the incoming signal. The behavioral-science literature accounts for much of the remaining variance\, because engagement rather than technology is the principal point of failure in digital health: defaults\, well-timed prompts\, tailoring\, and patient activation each carry measurable effects on attendance\, adherence\, and cost. \nThis session synthesizes these literatures for case management practice. It defines agentic AI — systems that plan and execute multi-step tasks under human oversight — and sets out the governance expectations of current guidance (NIST AI RMF\, WHO guidance\, FDA and ONC requirements)\, including automation bias and equity risk. A case study of an AI-supported care platform deployed across palliative and chronic disease programs demonstrates these principles in operation\, from structured symptom capture through clinician escalation and overnight caregiver support. Attendees will leave with a structured set of questions with which to appraise any AI tool used in patient care\, and with a clear account of the case manager’s position as the human in the loop on which the evidence base converges. \nLearning Objectives: \n\nDescribe the evidence for digitally supported symptom monitoring\, virtual palliative care\, and remote patient monitoring in oncology\, palliative\, and chronic disease populations — including trials that showed no benefit and the design features that separate success from failure.\nDistinguish agentic AI from chatbots and machine-learning risk models\, and identify the governance safeguards (NIST AI RMF\, WHO guidance\, human-in-the-loop escalation) that current guidance expects of AI in patient care.\nApply behavioral-science principles — defaults\, timing\, tailoring\, and patient activation — to appraise digital patient engagement tools in their own practice.\n\nSpeaker:\nDr. Matea Deliu MD\, PGDip Digital Health\, PhD\, MRCGP\, MBCS
URL:https://cmsa.org/event/cmsa-national-partnered-with-league-1-ce-from-symptom-to-signal-what-the-evidence-says-about-ai-supported-patient-engagement-in-cancer-palliative-and-chronic-care-2/
LOCATION:Virtual Event
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