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Healthcare Agencies II01:17

Healthcare Agencies II

There are various healthcare agencies in the United States—some of which are managed by religious institutions and others by different government branches.
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Using agency data for evidence-based programming: a university-agency collaboration.

Dale Fitch1, Andrew Grogan-Kaylor

  • 1University of Missouri, School of Social Work, Columbia, MO 65211, USA. fitchd@missouri.edu

Evaluation and Program Planning
|November 8, 2011
PubMed
Summary
This summary is machine-generated.

Residential treatment agencies can use administrative data to track patient progress. Specialized programming implemented in 2006 showed improved outcomes for residents, demonstrating the value of evidence-based practices.

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The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time
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The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time
06:05

The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time

Published on: February 19, 2021

Area of Science:

  • Child and Adolescent Psychiatry
  • Health Services Research
  • Data Science in Healthcare

Background:

  • Residential treatment agencies are increasingly required to collect outcomes data and implement evidence-based practices.
  • Guidance on utilizing ongoing administrative data for these purposes is limited.
  • Administrative data offers a valuable, continuous resource for monitoring treatment effectiveness.

Purpose of the Study:

  • To evaluate the utility of administrative data for assessing treatment outcomes in residential care.
  • To examine functional improvements in residents using repeated measures of the Child and Adolescent Functional Assessment Scale (CAFAS).
  • To assess the impact of program changes on resident outcomes.

Main Methods:

  • Analysis of 1608 residential treatment admissions data from 2002-2008.
  • Utilized repeated Child and Adolescent Functional Assessment Scale (CAFAS) scores collected every 90 days.
  • Employed multilevel models to estimate growth trajectories from repeated measures data.

Main Results:

  • Residents demonstrated significant functional improvement over the course of treatment on average.
  • Residents admitted after program modifications in October 2006 showed accelerated improvements in CAFAS scores.
  • Multilevel modeling effectively captured individual growth trajectories and program impact.

Conclusions:

  • Administrative data, when analyzed with advanced statistical models, can effectively inform organizational decision-making.
  • Evidence-based programming and continuous data monitoring are crucial for optimizing residential treatment outcomes.
  • The study highlights the successful integration of data-driven insights into clinical practice for improved adolescent care.