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Standardizing Fidelity to Evidence-Based Practice in the Early Psychosis Intervention Network.

Donald Addington1, Tina Marshall1, Abram Rosenblatt1

  • 1Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada (Addington); Westat, Rockville, Maryland (Marshall, Rosenblatt, Bond, Goldman); Department of Psychiatry, University of Maryland School of Medicine, Baltimore (Goldman).

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Variations in coordinated specialty care (CSC) fidelity scales were mapped to a standard measure. This revealed significant differences in measurement, impacting data quality within learning health systems.

Keywords:
Learning health systemprocess assessment health careprogram evaluation health services researchpsychotic disordersquality of health care

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Area of Science:

  • Mental Health Services Research
  • Health Systems Science
  • Psychosis Treatment

Background:

  • Coordinated Specialty Care (CSC) is a key intervention for early psychosis.
  • The Early Psychosis Intervention Network (EPINET) is a learning health system comprising 101 CSC programs.
  • Measuring fidelity to CSC models is crucial for quality improvement.

Purpose of the Study:

  • To investigate variations in fidelity measurement across different scales used in CSC programs.
  • To assess the comparability of five fidelity scales to a standard scale, the First Episode Psychosis Services Fidelity Scale (FEPS-FS).

Main Methods:

  • Identified six fidelity scales used within EPINET.
  • Examined item content, scoring, and data sources of these scales.
  • Mapped five selected scales onto the FEPS-FS.

Main Results:

  • The percentage of FEPS-FS components successfully mapped across the five scales ranged from 42% to 81%.
  • Significant variation exists in how different fidelity scales capture CSC components.

Conclusions:

  • Mapping fidelity scales highlights considerable measurement variability.
  • This variability can reduce the amount and quality of usable fidelity data.
  • Understanding fidelity measurement variations is essential for learning health systems to improve care quality.