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Implementing the PREP2 Algorithm to Predict Upper Limb Recovery Potential After Stroke in Clinical Practice: A

Louise A Connell1,2, Brigit Chesworth1, Suzanne Ackerley3

  • 1Faculty of Health and Wellbeing, University of Central Lancashire, Preston, Lancashire, United Kingdom.

Physical Therapy
|February 1, 2021
PubMed
Summary
This summary is machine-generated.

The Predict REcovery Potential (PREP2) tool for predicting motor recovery after stroke was successfully implemented in clinical practice. Therapist-driven initiatives, staff support, and perceived benefits facilitated its adoption, offering valuable lessons for future prediction model implementation.

Keywords:
Implementation SciencesPredictionRecovery of FunctionStroke RehabilitationUpper Extremity

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Area of Science:

  • Neurorehabilitation
  • Clinical implementation science
  • Health services research

Background:

  • Predicting motor recovery after stroke is crucial for tailored rehabilitation planning.
  • The Predict REcovery Potential (PREP2) tool aids clinicians in forecasting upper limb functional outcomes.
  • Successful translation of evidence-based tools into clinical practice remains a significant challenge.

Purpose of the Study:

  • To explore the implementation process of the PREP2 prediction tool within the Auckland District Health Board (ADHB) in New Zealand.
  • To identify factors influencing the adoption of PREP2 in clinical stroke care.
  • To document strategies used for implementing PREP2 in a real-world clinical setting.

Main Methods:

  • A case study design incorporating semi-structured interviews with 19 clinicians involved in stroke care.
  • Analysis of interview data using the consolidated framework for implementation research.
  • Description of implementation strategies based on the Expert Recommendations for Implementing Change Project.

Main Results:

  • The PREP2 tool was successfully implemented, driven by therapists.
  • Key facilitators included strong staff support, clinician self-efficacy, and perceived benefits of the prediction tool.
  • Twenty-six implementation strategies were identified across three domains: implementation team, clinical/academic partnerships, and training.

Conclusions:

  • The PREP2 prediction tool was effectively integrated into clinical practice at ADHB.
  • Understanding barriers and facilitators is essential for successful implementation of predictive models.
  • Lessons learned from this implementation can inform future efforts to integrate similar tools into healthcare settings.