Abstract
INTRODUCTION: Health systems worldwide face the challenge of increasing population health with high-quality care and reducing health care expenditure growth. In pursuit for a solution, regional cross-sectoral partnerships aim to reorganize and integrate services across public health, health care and social care. Although the complexity of regional partnerships demands an incremental strategy, it is yet not known how learning works within these partnerships. To understand learning in regional cross-sectoral partnerships for health, this study aims to map the concept Learning Health System (LHS). METHODS: This mapping review used a qualitative text analysis approach. A literature search was conducted in Embase and was limited to English-language papers published in the period 2015-2020. Title-abstract screening was performed using established exclusion criteria. During full-text screening, we combined deductive and inductive coding. The concept LHS was disentangled into aims, design elements, and process of learning. Data extraction and analysis were performed in MAX QDA 2020. RESULTS: In total, 155 articles were included. All articles used the LHS definition of the Institute of Medicine. The interpretation of the concept LHS varied widely. The description of LHS contained 25 highly connected aims. In addition, we identified nine design elements. Most elements were described similarly, only the interpretation of stakeholders, data infrastructure and data varied. Furthermore, we identified three types of learning: learning as 1) interaction between clinical practice and research; 2) a circular process of converting routine care data to knowledge, knowledge to performance; and performance to data; and 3) recurrent interaction between stakeholders to identify opportunities for change, to reveal underlying values, and to evaluate processes. Typology 3 was underrepresented, and the three types of learning rarely occurred simultaneously. CONCLUSION: To understand learning within regional cross-sectoral partnerships for health, we suggest to specify LHS-aim(s), operationalize design elements, and choose deliberately appropriate learning type(s).