Indianapolis Provider’s Use Of Wraparound Services Associated With Reduced Hospitalizations And Emergency Department Visits

Vest, Joshua R.; Harris, Lisa E.; Haut, Dawn P.; Halverson, Paul K.; Menachemi, Nir

doi:10.1377/hlthaff.2018.0075

Cited by 22 publications

(29 citation statements)

References 21 publications

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“… 22 This intervention has been used to integrate mental health services, social work, case management, health education, substance use counseling, legal advocacy, career counseling, and other wraparound services within the primary care setting. 22 , 55 Although there has been a significant emphasis on the implementation of such models of care coordination and the rationale for them is sound, there has been limited research to assess the success of their implementation and their efficacy in achieving desired outcomes, particularly in pediatrics. 56 …”

Section: Practical Guidelines To Advance the Pursuit Of Health Equitymentioning

confidence: 99%

In Pursuit of Health Equity in Pediatrics

Montoya-Williams

Peña

Fuentes-Afflick

2020

The Journal of Pediatrics: X

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Section: Practical Guidelines To Advance the Pursuit Of Health Equitymentioning

confidence: 99%

In Pursuit of Health Equity in Pediatrics

Montoya-Williams

Peña

Fuentes-Afflick

2020

The Journal of Pediatrics: X

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“…One important opportunity to advance population health within US medical schools is promoting a holistic view of health that includes both clinical and social determinants of health, well-being, disease, and disability and the multidisciplinary and cross-sector interventions and policies required to address them, such as early childhood education, economic development, and environmental protection. 12 This would help extend the engagement of health care beyond its principal focus on sick care (ie, diagnosis and treatment) to encompass both traditional and upstream approaches to prevention.…”

Section: Discussionmentioning

confidence: 99%

The Emergence of Population Health in US Academic Medicine

et al. 2019

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“…The availability of these data sets, together with a focus on mitigating patient social factors and uptake of machine learning solutions for health care present considerable potential for predictive modeling in support of risk prediction and intervention allocation [ 4 , 5 ]. This is particularly significant for wraparound services that can enhance primary care by utilizing providers who are trained in behavioral health, social work, nutritional counseling, patient navigation, health education, and medical legal partnerships in order to mitigate the effects of social risk and to address social needs [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…Wraparound services focus on the socioeconomic, behavioral, and financial factors that typical medical care encounters cannot address satisfactorily [ 7 , 8 ], and when used, can result in improved health care outcomes, reduced costs [ 6 , 9 ], and better coordination of care. As such, these services are of significant importance to health care organizations that are incentivized by United States reimbursement policies to mitigate the effects of social issues that influence poor health outcomes and unnecessary utilization of costly services [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Precision Health–Enabled Machine Learning to Identify Need for Wraparound Social Services Using Patient- and Population-Level Data Sets: Algorithm Development and Validation

Kasthurirathne¹,

Grannis²,

Halverson³

et al. 2020

JMIR Med Inform

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Background Emerging interest in precision health and the increasing availability of patient- and population-level data sets present considerable potential to enable analytical approaches to identify and mitigate the negative effects of social factors on health. These issues are not satisfactorily addressed in typical medical care encounters, and thus, opportunities to improve health outcomes, reduce costs, and improve coordination of care are not realized. Furthermore, methodological expertise on the use of varied patient- and population-level data sets and machine learning to predict need for supplemental services is limited. Objective The objective of this study was to leverage a comprehensive range of clinical, behavioral, social risk, and social determinants of health factors in order to develop decision models capable of identifying patients in need of various wraparound social services. Methods We used comprehensive patient- and population-level data sets to build decision models capable of predicting need for behavioral health, dietitian, social work, or other social service referrals within a safety-net health system using area under the receiver operating characteristic curve (AUROC), sensitivity, precision, F1 score, and specificity. We also evaluated the value of population-level social determinants of health data sets in improving machine learning performance of the models. Results Decision models for each wraparound service demonstrated performance measures ranging between 59.2%% and 99.3%. These results were statistically superior to the performance measures demonstrated by our previous models which used a limited data set and whose performance measures ranged from 38.2% to 88.3% (behavioural health: F1 score P<.001, AUROC P=.01; social work: F1 score P<.001, AUROC P=.03; dietitian: F1 score P=.001, AUROC P=.001; other: F1 score P=.01, AUROC P=.02); however, inclusion of additional population-level social determinants of health did not contribute to any performance improvements (behavioural health: F1 score P=.08, AUROC P=.09; social work: F1 score P=.16, AUROC P=.09; dietitian: F1 score P=.08, AUROC P=.14; other: F1 score P=.33, AUROC P=.21) in predicting the need for referral in our population of vulnerable patients seeking care at a safety-net provider. Conclusions Precision health–enabled decision models that leverage a wide range of patient- and population-level data sets and advanced machine learning methods are capable of predicting need for various wraparound social services with good performance.

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Indianapolis Provider’s Use Of Wraparound Services Associated With Reduced Hospitalizations And Emergency Department Visits

Cited by 22 publications

References 21 publications

In Pursuit of Health Equity in Pediatrics

In Pursuit of Health Equity in Pediatrics

The Emergence of Population Health in US Academic Medicine

Precision Health–Enabled Machine Learning to Identify Need for Wraparound Social Services Using Patient- and Population-Level Data Sets: Algorithm Development and Validation

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