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Analytical capacities at the heart of learning health systems: Conceptual framework based on a developmental
Yan Bertrand1, Stéphanie Lachance2, Aude Motulsky1
1Department of Health Management, Evaluation and Policy, School of Public Health, Université de Montréal, Montréal, QC H3N 1X9, Canada; Research Center, Academic Health Center of the Université de Montréal, Montréal, QC H2X 0C1, Canada.
Introduction:
Practical models for learning health systems implementation often lack a clear understanding of the organizational capacities required to sustain continuous data-driven improvement cycles. Among these, analytical capacities are widely recognized but insufficiently conceptualized. This study aims to develop a comprehensive framework of analytical capacities in healthcare organizations.
Methodology:
A developmental literature review was conducted to identify empirical and conceptual articles related to data management, analytics, and use in healthcare organizations. 697 articles were screened and twelve studies met the inclusion criteria. Data extraction was performed independently by two researchers using a structured grid that included a framework for the initial conceptual classification of relevant data. A thematic analysis was then conducted on the classified data to identify and group underlying processes into distinct analytical capacities. The resulting descriptive model was refined through expert consultation and used to build the conceptual framework.
Results:
Thirteen analytical capacities were identified, and then grouped into five categories according to their nature: data creation, data circulation, data preparation, data analysis, and data appropriation. A conceptual framework was developed to illustrate the relationships between these capacities and their role in a cyclical process of data-driven improvement.
Discussion And Conclusion:
The proposed framework characterizes the different analytical capacities and illustrates their interdependence, offering a practical tool for assessing and planning analytical development in healthcare organizations. It highlights the central role of data mobilization and supports the operationalization of learning health systems by making explicit the capacities that underpin organizational learning. It also lays the groundwork for developing maturity models specific to each analytical capacity, as well as for empirically validating the framework in real-world settings.
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