Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review

Golembiewski, Elizabeth H.; Allen, Katie; Blackmon, Amber M; Hinrichs, Rachel; Vest, Joshua R.

doi:10.2196/12846

Cited by 12 publications

(11 citation statements)

References 201 publications

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“…Nevertheless, the continued lack of meaningful contribution to our models prompts questions regarding how to best leverage aggregate social determinants of health measures for decision making. This is an important and unanswered question, as census-based aggregate measures are the most widely available and easily accessible indicators of social determinants of health available to researchers and health organizations [ 35 ]. In contrast, several patient-level social and behavioral factors measures were influential in the models.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…[9][10][11] However, DHDNs have focused almost exclusively on clinical data and rarely contain information about a patient's use of community resources, 9,[12][13][14] and nonclinical data (eg, community resource interactions) are typically absent in DHDNbased research. 15 Bringing data together across disparate systems requires both a technical solution and governance infrastructure for data exchange. Governance must address privacy needs of patients and families while fulfilling responsibilities of health care and community-based organizations to conduct ethical research.…”

Section: Abstract: Community-based Organizations Distributed Health D...mentioning

confidence: 99%

A Governance Framework to Integrate Longitudinal Clinical and Community Data in a Distributed Data Network: The Childhood Obesity Data Initiative

Kraus

Scott²,

Zucker

et al. 2021

Journal of Public Health Management and Practice

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Context: Integrating longitudinal data from community-based organizations (eg, physical activity programs) with electronic health record information can improve capacity for childhood obesity research. Objective: A governance framework that protects individual privacy, accommodates organizational data stewardship requirements, and complies with laws and regulations was developed and implemented to support the harmonization of data from disparate clinical and community information systems. Participants and Setting: Through the Childhood Obesity Data Initiative (CODI), 5 Colorado-based organizations collaborated to expand an existing distributed health data network (DHDN) to include community-generated data and assemble longitudinal patient records for research. Design: A governance work group expanded an existing DHDN governance infrastructure with CODI-specific data use and exchange policies and procedures that were codified in a governance plan and a delegated-authority, multiparty, reciprocal agreement. Results: A CODI governance work group met from January 2019 to March 2020 to conceive an approach, develop documentation, and coordinate activities. Governance requirements were synthesized from the CODI use case, and a customized governance approach was constructed to address governance gaps in record linkage, a procedure to request data, and

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“…A wide range of SBDH variables and data sources are used in predictive models and no guidelines exist to distinguish which variables and data sources would best improve the performance of the predictive model. A rapid review of social, behavioral, and environmental determinants of health used with clinical data identified 744 variables among 178 articles, in which the majority of articles included socioeconomic and material conditions [ 48 ]. Data sources vary from individual-level EHR data and insurance claims to community-level data from the United States census and similar sources as well as commercial data such as information from credit reporting agencies.…”

Section: Present State Of Including Social and Behavioral Determinantmentioning

confidence: 99%

Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities

Tan¹,

Hatef²,

Taghipour³

et al. 2020

JMIR Med Inform

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In an era of accelerated health information technology capability, health care organizations increasingly use digital data to predict outcomes such as emergency department use, hospitalizations, and health care costs. This trend occurs alongside a growing recognition that social and behavioral determinants of health (SBDH) influence health and medical care use. Consequently, health providers and insurers are starting to incorporate new SBDH data sources into a wide range of health care prediction models, although existing models that use SBDH variables have not been shown to improve health care predictions more than models that use exclusively clinical variables. In this viewpoint, we review the rationale behind the push to integrate SBDH data into health care predictive models and explore the technical, strategic, and ethical challenges faced as this process unfolds across the United States. We also offer several recommendations to overcome these challenges to reach the promise of SBDH predictive analytics to improve health and reduce health care disparities.

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Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review

Cited by 12 publications

References 201 publications

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

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

A Governance Framework to Integrate Longitudinal Clinical and Community Data in a Distributed Data Network: The Childhood Obesity Data Initiative

Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities

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