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A machine learning approach for identifying predictors of success in a Medicaid-funded, community-based behavioral health program using the Child and Adolescent Needs and Strengths (CANS)

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  • Troy, Jesse D.
  • Torrie, Ryan M.
  • Warner, Daniel N.

Abstract

The CANS is the most popular measurement tool in the System of Care (SoC), with the potential to generate an estimated 1.9 million evaluations per year in the United States. This dataset has broad potential for decision support and outcomes monitoring, yet many SoC services do not yet leverage this information asset. We report here the results of a pilot project in which we applied machine learning methods to CANS data for the purpose of identifying clinical profiles associated with improvement in a public community-based behavioral health program in Pennsylvania.

Suggested Citation

  • Troy, Jesse D. & Torrie, Ryan M. & Warner, Daniel N., 2021. "A machine learning approach for identifying predictors of success in a Medicaid-funded, community-based behavioral health program using the Child and Adolescent Needs and Strengths (CANS)," Children and Youth Services Review, Elsevier, vol. 126(C).
    • Handle: RePEc:eee:cysrev:v:126:y:2021:i:c:s019074092100089x
    DOI: 10.1016/j.childyouth.2021.106010
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    References listed on IDEAS

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    1. Bartlett, Jessica Dym & Griffin, Jessica L. & Spinazzola, Joseph & Fraser, Jenifer Goldman & Noroña, Carmen Rosa & Bodian, Ruth & Todd, Marybeth & Montagna, Crystaltina & Barto, Beth, 2018. "The impact of a statewide trauma-informed care initiative in child welfare on the well-being of children and youth with complex trauma," Children and Youth Services Review, Elsevier, vol. 84(C), pages 110-117.
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