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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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A single logic model capturing clinical, service, and implementation interventions using a practical example in

Skye McKay1, Joseph Elias1,2, Carolyn Mazariego1

  • 1Implementation to Impact (i2i), School of Population Health, Faculty of Medicine and Health, UNSW Sydney, Sydney, NSW, Australia.

JBI Evidence Implementation
|May 14, 2026
PubMed
Summary

The Clinical, Service, and Implementation Intervention Research Logic Model (CSII-RLM) helps differentiate complex health interventions. This new logic model aids in designing and implementing service interventions for pediatric precision medicine.

Keywords:
causal pathwayscomplex interventionslogic modelmechanisms

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Area of Science:

  • Implementation Science
  • Health Services Research
  • Pediatric Precision Medicine

Background:

  • The Implementation Research Logic Model (IRLM) is crucial for evidence-based healthcare implementation.
  • Distinguishing multilevel interventions, especially clinical and implementation aspects, presents a significant challenge.

Purpose of the Study:

  • To develop the Clinical, Service, and Implementation Intervention Research Logic Model (CSII-RLM).
  • To support the co-design and implementation of ProCure, a database for off-label therapy in pediatric precision medicine.

Main Methods:

  • Qualitative data from 17 pediatric healthcare professional interviews were analyzed using the Consolidated Framework for Implementation Research (CFIR).
  • The existing IRLM Clinical Intervention template was adapted, incorporating new elements to create the CSII-RLM.
  • Worked examples and algorithms were developed to illustrate the model's application.

Main Results:

  • The CSII-RLM successfully defined "Service Intervention" as a distinct component supporting ProCure within pediatric precision medicine.
  • Worked examples demonstrated the CSII-RLM's ability to differentiate clinical and service interventions for real-world implementation understanding.
  • Novel algorithms were created to clarify causal pathways for each intervention type.

Conclusions:

  • The CSII-RLM shows promise for designing and implementing service interventions that support complex clinical interventions in healthcare.
  • The CSII-RLM's utility was demonstrated during the ProCure implementation planning phase.
  • Further testing and refinement of the CSII-RLM are planned throughout the ProCure implementation.