1. The UCSF Health Problem
The UCSF Health Problem: Hospital Acquired Pressure Injury (HAPI) is a preventable injury to skin or soft tissue that is acquired during a patient’s hospital stay. Reducing HAPI rates is a top priority for UCSF Health leadership as the occurrence of HAPI is detrimental to patient experience and outcomes, results in significant costs (estimated cost to the health system for 1 HAPI is $18,000-$27,000), and is a critical quality measure in the evaluation of hospital performance.
This proposal outlines the development of an AI-Augmented Fall Prevention Tool aimed at reducing inpatient falls by providing nurses with real-time, tailored, and actionable recommendations using clinical data. The tool integrates a dynamic risk prediction model, LLM-generated clinical decision support system, and feedback mechanism with "a nurse in the loop" to continuously refine model performance. The initiative seeks to improve patient safety, reduce documentation burden, and serve as a scalable model for nursing-led AI innovation in healthcare.
1. The UCSF Health Problem
We have previously constructed a liver disease specific Large Language Model (LLM) called “LiVersa” using Retrieval Augmented Generation (PMID 38451962, PMID 38935858). We have also constructed “CirrhosisRx,” which is a rule-based (non-AI) clinical decision support system for guideline adherent inpatient cirrhosis care, built on the EngageRx platform using SMART-on-FHIR (NCT05967273, PMID 38407255). In this proposal, we plan to integrate LiVersa into CirrhosisRx via Fast Healthcare Interoperability Resources (FHIR) application progressing interface (API) calls to provide a patient-personalized dynamic information retrieval system to provide guideline-based clinical recommendations to clinician end-users.
Patients with chronic liver diseases, including those with cirrhosis and those awaiting transplantation, have unique and often stringent nutritional requirements. There is an unmet need to provide treating clinicians with culturally-appropriate, evidence-based, and patient-specific nutritional recommendations that resonates with that person’s dietary norms and preferences. This proposal seeks to bridge this gap through a culturally appropriate and personalized RAG-LLM assistant that supports nutritional counseling in liver diseases.
Early-stage or covert hepatic encephalopathy (CHE), which can be present in up to 60% of patients with cirrhosis, often goes unrecognized by both patients and clinicians. Without timely identification and intervention, such as initiating or titrating lactulose or rifaximin, patients can rapidly progress to overt HE. This is strong emerging evidence that suggest a patient’s voice can serve as a biomarker for CHE (PMID 35861546, PMID 39264936). This proposal seeks to leverage ambient AI scribe recordings obtained through routine clinical care as a voice-based biomarker for CHE detection.
Section 1: The UCSF Health Problem
1. The UCSF Health problem.
Diagnostic errors are common and harmful. Previous work from our group suggests a missed or delayed diagnosis takes place in 25% of patients who die or who are transferred to the ICU; a diagnostic error is the direct cause of death in as many as 8% of deaths.