Analytical capacities at the heart of learning health systems: Conceptual framework based on a developmental literature review
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".