Grant Number:	1R01CA271303-01A1 Interpret this number
Primary Investigator:	Chu, Daniel
Organization:	University Of Alabama At Birmingham
Project Title:	Adapting Enhanced Recovery Programs (ERPS) Through Health Literacy to Eliminate Surgical Disparities
Fiscal Year:	2023

PROJECT SUMMARY Rationale: Low health literacy affects over a third of surgical populations and is associated with significantly worse outcomes in surgery. Interventions that reduce disparities in this large population are urgently needed. Our team has previously shown that enhanced recovery programs (ERPs) mitigate racial disparities in surgical outcomes and offer a pragmatic way to address surgical disparities. Existing ERPs, however, work poorly for patients with low health literacy who still experience worse outcomes. This gap arises from the lack of fit between current ERPs and the needs of low health literacy patients. Through a K23, our team assessed these needs and developed a novel multilevel strategy to improve fit: engage patients with VISuAl aids, Coach providers in communication, and Train organizations in health literacy (VISACT). An opportunity now exists to deliver and test the VISACT using a theory-based adaptation framework. Successful adaptations would transform existing ERPs and broaden its disparity-reducing impact to low health literacy populations. Objectives: Our long-term objective is to eliminate disparities and improve outcomes for low health literacy populations in surgery through context-driven adaptations of existing ERPs. We hypothesize that VISACT will improve fidelity to ERP’s components for low health literacy patients and thereby surgical outcomes. To achieve our objective, we aim to: (SA1) identify the health literacy-sensitive components of ERPs to augment with VISACT, (SA2) assess the health literacy needs of providers and organizational units on ERP teams, and (SA3) deliver and pilot test the VISACT implementation strategy on existing ERPs. Methods: Guided by the Dynamics Adaptation Process framework, we will first use machine learning on a large ERP database (n>7,000) to identify the health literacy- sensitive components of ERPs to augment with VISACT (SA1). Second, we will use a convergent mixed- methods integrative approach to identify gaps in health literacy knowledge, best practices, and preparedness to adapt on ERP implementation teams through three interrelated methods: in vivo observations of ERPs in-action at 4 Alabama facilities, extended semi-structured interviews of 120 stakeholders, and surveys measuring health literacy knowledge and organizational preparedness to adapt. Third, we will deliver the VISACT to two sites in Alabama (urban and rural) through a novel interactive response platform in a pilot study and assess for feasibility/acceptability through a RE-AIM framework of reach, efficacy, adoption, implementation, and maintenance measures. Acquired data will inform design of a multi-institutional stepped-wedge trial of the VISACT in the Deep South. Significance: This study will advance the NIH/NIMHD mission to eliminate surgical disparities and responds directly to the NIMHD Science Visioning Research Strategies by removing health literacy barriers (#24) and building the science of adapting interventions to different contexts (#30). Our team will furthermore (i) deliver the first health literate intervention in surgery, (ii) establish a novel implementation strategy (VISACT) to address surgical disparities and (iii) advance the science of interventions through adaptations.





None