Use of the electronic health record to target a patient navigation intervention preventing loss to follow up in the treatment of urinary stone disease

University Of California, San Francisco, San Francisco CA

Investigators

Linked publications, trials & patents

Abstract

PROJECT SUMMARY This is a K23 award application for Dr. David Bayne, an Assistant Professor at UCSF. His career goal is to become a clinician-researcher focused on studying and reducing disparities in treatment outcomes for urinary stone disease (USD). This award will provide him with training and research experience to: (1) leverage electronic health record (EHR) data to streamline the identification of patients at risk for being lost to follow up (LTFU) after diagnosis of USD in the emergency department (ED); (2) elucidate stakeholder perspectives on root causes of, and potential solutions for, LTFU; and (3) evaluate acceptability and feasibility of early risk identification paired with patient navigator support as a pilot intervention to reduce LTFU for USD. Dr. Bayne has assembled an ideal team composed of co-primary mentors, Dr. Marshall Stoller and Dr. Charles Scales, experts in clinical trial design and implementation for USD; and co-mentors Dr. John Neuhaus, an expert in biostatistics and prediction model development, Dr. Sara Ackerman, an expert in application of qualitative research methods, and Dr. Lilia Cervantes, an expert in clinical trial design and implementation for patient navigation interventions. Although disparities along lines of socioeconomic status (SES) have been consistently described in the diagnosis and treatment of USD, interventions to mitigate these disparities are lacking. Dr. Bayne will build on findings from his prior work showing a consistent, independent association between delays in urologic care for USD and multiple EHR-derived predictors that correspond to low SES (e.g. insurance, demographic information, community level data). Predictive statistical models will be employed to streamline the identification of patients at risk for LTFU for USD upon placement of an outpatient referral to urological care in the ED by leveraging EHR-derived clinical and social data covariates (Aim 1). In addition, semi-structured interviews with patients and their providers will be conducted to elucidate themes contributing to LTFU for USD (Aim 2). A paired LTFU risk identification and patient navigation intervention will be developed and piloted to assess for acceptability and feasibility (Aim 3). This work will be the foundation of an R01 proposal for a randomized control trial to assess LTFU risk identification and tailored patient navigation as an intervention to reduce disparities in care delays in the treatment of USD. Through a focused program of mentored training and coursework, the candidate will gain new and necessary skills to deliver these research aims and advance in his career and professional development. These skills include: (1) prediction modeling with advanced biostatistics, (2) qualitative and mixed methods research, and (3) intervention development for clinical trials. These skills will enable Dr. Bayne’s transition to independence by uniquely positioning him to identify, study, and intervene upon factors contributing to disparities in USD care outcomes in low SES patients.