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

Objective: Predicting motor recovery after stroke is a key factor when planning and providing rehabilitation for individual patients. The Predict REcovery Potential (PREP2) prediction tool was developed to help clinicians predict upper limb functional outcome. In parallel to further model validation, the purpose of this study was to explore how PREP2 was implemented in clinical practice within the Auckland District Health Board (ADHB) in New Zealand.

Methods: In this case study design using semi-structured interviews, 19 interviews were conducted with clinicians involved in stroke care at ADHB. To explore factors influencing implementation, interview content was coded and analyzed using the consolidated framework for implementation research. Strategies identified by the Expert Recommendations for Implementing Change Project were used to describe how implementation was undertaken.

Results: Implementation of PREP2 was initiated and driven by therapists. Key factors driving implementation were as follows: the support given to staff from the implementation team; the knowledge, beliefs, and self-efficacy of staff; and the perceived benefits of having PREP2 prediction information. Twenty-six Expert Recommendations for Implementing Change strategies were identified relating to 3 areas: implementation team, clinical/academic partnerships, and training.

Conclusions: The PREP2 prediction tool was successfully implemented in clinical practice at ADHB. Barriers and facilitators to implementation success were identified, and implementation strategies were described. Lessons learned can aid future development and implementation of prediction models in clinical practice.

Impact: Translating evidence-based interventions into clinical practice can be challenging and slow; however, shortly after its local validation, PREP2 was successfully implemented into clinical practice at the same site in New Zealand. In parallel to further model validation, organizations and practices can glean useful lessons to aid future implementation.