Assessing the capacity of social determinants of health data to augment predictive models identifying patients in need of wraparound social services

Abstract

Introduction: A growing variety of diverse data sources is emerging to better inform health care delivery and health outcomes. We sought to evaluate the capacity for clinical, socioeconomic, and public health data sources to predict the need for various social service referrals among patients at a safety-net hospital.

Materials and methods: We integrated patient clinical data and community-level data representing patients' social determinants of health (SDH) obtained from multiple sources to build random forest decision models to predict the need for any, mental health, dietitian, social work, or other SDH service referrals. To assess the impact of SDH on improving performance, we built separate decision models using clinical and SDH determinants and clinical data only.

Results: Decision models predicting the need for any, mental health, and dietitian referrals yielded sensitivity, specificity, and accuracy measures ranging between 60% and 75%. Specificity and accuracy scores for social work and other SDH services ranged between 67% and 77%, while sensitivity scores were between 50% and 63%. Area under the receiver operating characteristic curve values for the decision models ranged between 70% and 78%. Models for predicting the need for any services reported positive predictive values between 65% and 73%. Positive predictive values for predicting individual outcomes were below 40%.

Discussion: The need for various social service referrals can be predicted with considerable accuracy using a wide range of readily available clinical and community data that measure socioeconomic and public health conditions. While the use of SDH did not result in significant performance improvements, our approach represents a novel and important application of risk predictive modeling.

Keywords: delivery of health care; integrated; social determinants of health; supervised machine learning; wraparound social services.

© The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Figures

Figure 1.

The complete study approach, from data collection and decision model building to evaluation of results.

Figure 2.

Sensitivity, specificity, accuracy, PPV: the sensitivity values of decision models to predict the need for any referrals using the clinical and master data vectors, together with 95% confidence interval for each performance metric.

Figure 3.

Sensitivity, specificity, accuracy, PPV: the sensitivity values of decision models to predict the need for mental health, social work, dietitian, and other SDH service referrals using the clinical and master data vectors, together with 95% confidence interval for each performance metric.