Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Sep 8, 2019
Date Accepted: Apr 9, 2020
Date Submitted to PubMed: Jun 1, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Use of Patient and Population-Level Datasets to Identify Need of Wraparound Social Services: A Precision Health Enabled Machine Learning Approach
ABSTRACT
Background:
Emerging interest in precision health and the increasing availability of varied patient and population-level datasets present considerable potential to enable analytical approaches to identify and mitigate the effects of upstream social issues. These issues are not satisfactorily addressed in typical medical care encounters and thus opportunities to improve health outcomes, reduced costs and improve care coordination are not realized. Further, methodological expertise on the use of varied patient and population-level datasets and machine learning to predict need of upstream services is limited.
Objective:
To leverage a comprehensive range of patient and population-level data measuring clinical, behavioral and social determinants of health (SDoH) to develop decision models capable of identifying patients in need of various “wraparound” social services that mitigate the effects of upstream social needs.
Methods:
We leveraged comprehensive patient and population-level datasets representing an individual’s clinical, behavioral and SDoH to build decision models capable of predicting need for behavioral health, dietitian, social work, or other SDoH service referrals. We also evaluated the value of population-level SDoH datasets in improving machine learning performance.
Results:
Decision models for each wraparound service reported performance measures ranging between 56% and 99%. These results were statistically superior to performance measures reported during a previous machine learning effort using a limited dataset. However, inclusion of additional population-level SDoH did not contribute to any performance improvements in our population of vulnerable patients seeking care at a safety net provider. Optimal decision models were integrated into nine federally qualified health center sites operated by Eskenazi Health of Indianapolis, Indiana. These models are currently in operation at these clinics, where they are used to predict need of wraparound services to patients presenting for primary care.
Conclusions:
Precision health enabled decision models that leverage a wide range of patient and population-level datasets and advanced machine learning methods are capable of predicting need of various wraparound social services with considerable performance measures.
Citation
The author of this paper has made a PDF available, but requires the user to login, or create an account.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.