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Using Machine Learning to Identify Social Determinants of Health that Impact Discharge Disposition for Hospitalized

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Social determinants of health (SDOH) like social connection and transportation needs significantly predict skilled nursing facility (SNF) discharge. Addressing these factors can help more patients return home after hospitalization.

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Area of Science:

  • Health Services Research
  • Social Epidemiology
  • Health Informatics

Background:

  • Hospital discharge planning aims to ensure appropriate patient disposition.
  • Skilled nursing facility (SNF) placement is a common outcome, but home discharge is often preferred.
  • Social determinants of health (SDOH) are increasingly recognized as crucial factors influencing health outcomes.

Purpose of the Study:

  • To identify self-reported social determinants of health (SDOH) that predict discharge to a skilled nursing facility (SNF) among hospitalized patients.
  • To develop and validate predictive models for SNF disposition based on SDOH.

Main Methods:

  • Retrospective cohort analysis of 134,807 hospitalized patients.
  • Utilized stacked elastic net (SENET) and bootstrap imputation-stability selection (BISS) for variable selection with incomplete data.
  • Logistic regression models were developed and validated, with performance assessed using area under the curve (AUC) and calibration.

Main Results:

  • 8.72% of patients were discharged to an SNF.
  • Key SDOH predictors included alcohol consumption, dental care, employment, financial resources, nutrition, physical activity, social connection, and transportation.
  • Models incorporating SDOH, clinical (Charlson Comorbidity Index), and demographic factors achieved an AUC of approximately 0.77 in the validation cohort.

Conclusions:

  • Multiple SDOH characteristics, particularly social connection and marital status, significantly predict SNF disposition.
  • Potentially mitigable factors like nutrition, physical activity, and transportation present actionable targets to increase home discharge rates.
  • Integrating SDOH data into discharge planning can optimize appropriateness and efficiency.