Developing a codebook to characterize barriers, enablers, and strategies for implementing learning health systems from a multilevel perspective - Louise Shaw; Meg Perrier; Kasra Tahmasebian; Kimberly Wong; Pranjali Yajnik; Zihan Zhu; Kayley Lyons (Learning Health Systems)
Abstract Introduction Learning health systems (LHSs) play a crucial role in improving healthcare delivery and outcomes through continuous learning and data‐driven decision‐making. Implementation of LHSs spans individual, organization, and systemic levels of healthcare. This paper outlines a systematic approach for developing a comprehensive codebook to identify barriers, enablers, and strategies associated with the establishment and operation of LHS from a multilevel perspective. Methods The codebook development process was divided into two phases and employed a coding team. Phase 1 involved the synthesis of previous literature, which drove the development of initial codes. Phase 2 included the testing of the codebook with a pilot dataset to derive new codes or iterative refinement, ensuring robustness, and validity. Results The literature search revealed 12 papers that detailed the barriers, enablers, and strategies for LHS implementation. Micro‐level codes were derived from a mixture of existing literature and our pilot dataset. Most meso‐level codes barriers and enablers were derived from the literature, with some subcodes derived from participant interviews. All strategies for implementation at the meso‐level were identified in the literature. At the macro‐level, all codes and subcodes were from the literature. Conclusions The codebook contributes to the advancement of implementation science in LHS. The codebook facilitates effective analysis and understanding of the key factors influencing the success of LHS implementation, offering practical insights for policymakers, healthcare practitioners and researchers engaged in the ongoing evolution of LHS.