A machine learning framework for optimizing obesity care by simulating clinical trajectories and targeted interventions

Abstract

Objective

This study aimed to determine the important clinical management bottlenecks that contribute to underuse of weight loss surgery (WLS) and assess risk factors for attrition at each of them.

Methods

A multistate conceptual model of progression from primary care to WLS was developed and used to study all adults who were seen by a primary care provider (PCP) and eligible for WLS from 2016 to 2017 at a large institution. Outcomes were progression from each state to each subsequent state in the model: PCP visit, endocrine weight management referral, endocrine weight management visit, WLS referral, WLS visit, and WLS.

Results

Beginning with an initial PCP visit, the respective 2-year Kaplan-Meier estimate for each outcome was 35% (n = 2063), 15.6% (n = 930), 6.3% (n = 400), 4.7% (n = 298), and 1.0% (n = 69) among 5876 eligible patients. Individual providers and clinics differed significantly in their referral practices. Female patients, younger patients, those with higher BMI, and those seen by trainees were more likely to progress. A simulated intervention to increase referrals among PCPs would generate about 49 additional WLS procedures over 3 years.

Conclusions

This study discovered novel insights into the specific dynamics underlying low WLS use rates. This methodology permits in silico testing of interventions designed to optimize obesity care prior to implementation.