provide me a complete poster for presentation in the conference with this - Here is a complete, structured abstract tailored for your conference submission.Title: Clinician-in-the-Loop Artificial Intelligence for Targeted Deprescribing in Frail Older Adults: Prioritizing Quality of Life Over Conventional Survival EndpointsBackground:The iatrogenic burden of polypharmacy in older adults necessitates innovative management strategies. While generative artificial intelligence (GenAI) shows significant potential in identifying drug-related problems, organizations like the American Geriatrics Society emphasize that it must strictly serve as an assistive, "clinician-in-the-loop" tool rather than replacing human clinical judgment. Simultaneously, robust evidence confirms that deprescribing preventive medications in frail older adults with limited life expectancy is safe. However, clinical research frequently misaligns with this demographic by prioritizing traditional overall survival endpoints over patient-centered outcomes, such as health-related quality of life (HR-QOL) and treatment tolerance. Methods:This research proposes a pharmacist-led, AI-assisted medication review protocol targeting frail older adults. Clinical pharmacists utilize GenAI decision support to identify deprescribing opportunities for long-term preventive medications (e.g., statins, antihypertensives, and antidiabetics). Baseline patient vulnerability is quantified using validated tools such as the Geriatric-8 (G-8) and Vulnerable Elders Survey-13 (VES-13), which are highly prognostic for severe toxicity and hospitalization risk. To capture meaningful outcomes, the framework advocates for the formal adoption of novel primary endpoints—specifically disability-free survival and HR-QOL—rather than relying on conventional survival-only metrics. Expected Results:Recent comparative studies demonstrate a 27.7% agreement between GenAI (ChatGPT-4) and healthcare professionals in complex medication reviews, underscoring AI's value as a supplementary screening tool to enhance clinical efficiency. By executing AI-flagged deprescribing opportunities, clinical pharmacists can significantly reduce pill burden. Systematic reviews of randomized controlled trials confirm that structured withdrawal of preventive medications in frail populations does not compromise survival. Furthermore, implementing these comprehensive medication reviews in acute settings is associated with a statistically significant 8% reduction in hospital readmissions without increasing all-cause mortality. Conclusions:Integrating clinician-guided GenAI into deprescribing workflows offers a safe, highly efficient pathway to mitigate polypharmacy in vulnerable geriatric populations. To accurately measure the value of such optimized pharmacotherapy, it is both a scientific and ethical imperative that future trials abandon traditional survival endpoints in favor of validated frailty assessments and quality-of-life metrics.
provide me a complete poster for presentation in the conference with this - Here is a complete, structured abstract tailored for your conference submission.Title: Clinician-in-the-Loop Artificial Intelligence for Targeted Deprescribing in Frail Older Adults: Prioritizing Quality of Life Over Conventional Survival EndpointsBackground:The iatrogenic burden of polypharmacy in older adults necessitates innovative management strategies. While generative artificial intelligence (GenAI) shows significant potential in identifying drug-related problems, organizations like the American Geriatrics Society emphasize that it must strictly serve as an assistive, "clinician-in-the-loop" tool rather than replacing human clinical judgment. Simultaneously, robust evidence confirms that deprescribing preventive medications in frail older adults with limited life expectancy is safe. However, clinical research frequently misaligns with this demographic by prioritizing traditional overall survival endpoints over patient-centered outcomes, such as health-related quality of life (HR-QOL) and treatment tolerance. Methods:This research proposes a pharmacist-led, AI-assisted medication review protocol targeting frail older adults. Clinical pharmacists utilize GenAI decision support to identify deprescribing opportunities for long-term preventive medications (e.g., statins, antihypertensives, and antidiabetics). Baseline patient vulnerability is quantified using validated tools such as the Geriatric-8 (G-8) and Vulnerable Elders Survey-13 (VES-13), which are highly prognostic for severe toxicity and hospitalization risk. To capture meaningful outcomes, the framework advocates for the formal adoption of novel primary endpoints—specifically disability-free survival and HR-QOL—rather than relying on conventional survival-only metrics. Expected Results:Recent comparative studies demonstrate a 27.7% agreement between GenAI (ChatGPT-4) and healthcare professionals in complex medication reviews, underscoring AI's value as a supplementary screening tool to enhance clinical efficiency. By executing AI-flagged deprescribing opportunities, clinical pharmacists can significantly reduce pill burden. Systematic reviews of randomized controlled trials confirm that structured withdrawal of preventive medications in frail populations does not compromise survival. Furthermore, implementing these comprehensive medication reviews in acute settings is associated with a statistically significant 8% reduction in hospital readmissions without increasing all-cause mortality. Conclusions:Integrating clinician-guided GenAI into deprescribing workflows offers a safe, highly efficient pathway to mitigate polypharmacy in vulnerable geriatric populations. To accurately measure the value of such optimized pharmacotherapy, it is both a scientific and ethical imperative that future trials abandon traditional survival endpoints in favor of validated frailty assessments and quality-of-life metrics.
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This initiative emphasizes the need for a new model in deprescribing due to the challenges of polypharmacy and its impact on patient well-being. It introduces a pharmacist-led AI protocol to assess frailty and manage medication withdrawal effectively. The focus is on improving quality of life for frail adults, supported by data showing reduced hospital readmissions and the importance of measuring relevant health outcomes.