A single logic model capturing clinical, service, and implementation interventions using a practical example in pediatric precision medicine

INTRODUCTION: The Implementation Research Logic Model (IRLM) is a key tool for guiding evidence-based health care implementation. However, multilevel interventions are inherently complex, making it challenging to distinguish the inter-relationships between clinical and implementation interventions. AIMS: To address this, we developed the Clinical, Service, and Implementation Intervention Research Logic Model (CSII-RLM) to support the co-design and implementation of ProCure, a database that supports clinicians with off-label therapy applications in pediatric precision medicine. METHODS: We deductively coded and analyzed a qualitative dataset using the Consolidated Framework for Implementation Research (CFIR), generated from 17 pediatric health care professional semi-structured interviews. We entered the synthesized data into the IRLM Clinical Intervention template and made adaptations to allow for a more comprehensive fit of the data, resulting in the CSII-RLM. We incorporated worked examples and developed algorithms to demonstrate application and functionality. RESULTS: The new intermediary "Service Intervention" successfully defined ProCure as a separate, supporting intervention within the broader context of pediatric precision medicine. Worked examples demonstrate how the CSII-RLM distinguishes between clinical and service-level interventions and helps to capture and understand real-world implementation. Novel algorithms further support the development and understanding of causal pathways for each intervention. CONCLUSIONS: The CSII-RLM is a promising tool that can assist the design and implementation of service interventions to support complex clinical interventions within the health care setting. The application of the CSII-RLM has been demonstrated in the implementation planning phase of ProCure and will continue to be tested and refined throughout its implementation. SPANISH ABSTRACT: http://links.lww.com/IJEBH/A582.

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Publication Details

Journal
JBI Evidence Implementation
Published
2026-05-14
DOI
https://doi.org/10.1097/xeb.0000000000000600
Primary Topic
Health Policy Implementation Science
Type
article
Field-Weighted Citation Impact
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article

A single logic model capturing clinical, service, and implementation interventions using a practical example in pediatric precision medicine

Joseph Elias, David S. Ziegler, S. McKay, Claire E. Wakefield et al.
JBI Evidence Implementation
Health Policy Implementation Science
article

A single logic model capturing clinical, service, and implementation interventions using a practical example in pediatric precision medicine

Joseph Elias, David S. Ziegler, S. McKay, Claire E. Wakefield, Justin D. Smith, Carolyn Mazariego, Elijah Tyedmers, April Morrow, Natalie Taylor
article en

Abstract

INTRODUCTION: The Implementation Research Logic Model (IRLM) is a key tool for guiding evidence-based health care implementation. However, multilevel interventions are inherently complex, making it challenging to distinguish the inter-relationships between clinical and implementation interventions. AIMS: To address this, we developed the Clinical, Service, and Implementation Intervention Research Logic Model (CSII-RLM) to support the co-design and implementation of ProCure, a database that supports clinicians with off-label therapy applications in pediatric precision medicine. METHODS: We deductively coded and analyzed a qualitative dataset using the Consolidated Framework for Implementation Research (CFIR), generated from 17 pediatric health care professional semi-structured interviews. We entered the synthesized data into the IRLM Clinical Intervention template and made adaptations to allow for a more comprehensive fit of the data, resulting in the CSII-RLM. We incorporated worked examples and developed algorithms to demonstrate application and functionality. RESULTS: The new intermediary "Service Intervention" successfully defined ProCure as a separate, supporting intervention within the broader context of pediatric precision medicine. Worked examples demonstrate how the CSII-RLM distinguishes between clinical and service-level interventions and helps to capture and understand real-world implementation. Novel algorithms further support the development and understanding of causal pathways for each intervention. CONCLUSIONS: The CSII-RLM is a promising tool that can assist the design and implementation of service interventions to support complex clinical interventions within the health care setting. The application of the CSII-RLM has been demonstrated in the implementation planning phase of ProCure and will continue to be tested and refined throughout its implementation. SPANISH ABSTRACT: http://links.lww.com/IJEBH/A582.

JBI Evidence Implementation
Spencer Foundation (US), UNSW Sydney (AU), Children's Cancer Institute Australia (AU), Sydney Children's Hospital (AU), Sydney Local Health District (AU)
Partnerships for the goals
Openalex Percentile: Top 3%
Health Policy Implementation Science
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