Determinants of remote measurement-based care uptake in a safety net outpatient psychiatry department as part of learning health system transition

Abstract

Introduction

Behavioral measurement-based care (MBC) can improve patient outcomes and has also been advanced as a critical learning health system (LHS) tool for identifying and mitigating potential disparities in mental health treatment. However, little is known about the uptake of remote behavioral MBC in safety net settings, or possible disparities occurring in remote MBC implementation.

Methods

This study uses electronic health record data to study variation in completion rates at the clinic and patient level of a remote MBC symptom measure tool during the first 6 months of implementation at three adult outpatient psychiatry clinics in a safety net health system. Provider-reported barriers to MBC adoption were also measured using repeated surveys at one of the three sites.

Results

Out of 1219 patients who were sent an MBC measure request, uptake of completing at least one measure varied by clinic: General Adult Clinic, 38% (n = 262 of 696); Substance Use Clinic, 28% (n = 73 of 265); and Transitions Clinic, 17% (n = 44 of 258). Compared with White patients, Black and Portuguese or Brazilian patients had lower uptake. Older patients also had lower uptake. Spanish language of care was associated with much lower uptake at the patient level. Significant patient-level disparities in uptake persisted after adjusting for the clinic, mental health diagnoses, and number of measure requests sent. Providers cited time within visits and bandwidth in their workflow as the greatest consistent barriers to discussing MBC results with patients.

Conclusions

There are significant disparities in MBC uptake at the patient and clinic level. From an LHS data infrastructure perspective, safety net health systems may need to address the need for possible ways to adapt MBC to better fit their populations and clinical needs, or identify targeted implementation strategies to close data gaps for the identified disparity populations.