Big Data and what it means for evaluating integrated care programmes

Axel Kaehne*

*Corresponding author for this work

    Research output: Contribution to journalArticle (journal)peer-review

    4 Citations (Scopus)
    70 Downloads (Pure)


    Purpose: Big Data is likely to have significant implications for the way in which services are planned, organised or delivered as well as the way in which we evaluate them. The increase in data availability creates particular challenges for evaluators in the field of integrated care and the purpose of this paper is to set out how we may usefully reframe these challenges in the longer term. Design/methodology/approach: Using the characteristics of Big Data as defined in the literature, the paper develops a narrative around the data and research design challenges and how they influence evaluation studies in the field of care integration. Findings: Big Data will have significant implications for how we conduct integrated care evaluations. In particular, dynamic modelling and study designs capable of accommodating new epistemic foundations for the phenomena of social organisations, such as emergence and feedback loops, are likely to be most helpful. Big Data also generates opportunities for exploratory data analysis approaches, as opposed to static model development and testing. Evaluators may find research designs useful that champion realist approaches or single-n designs. Originality/value: This paper reflects on the emerging literature and changing practice of data generation and data use in health care. It draws on organisational theory and outlines implications of Big Data for evaluating care integration initiatives.

    Original languageEnglish
    Pages (from-to)249-258
    Number of pages10
    JournalJournal of Integrated Care
    Issue number3
    Early online date20 Jun 2019
    Publication statusPublished - 20 Jun 2019


    • Big Data
    • Evaluation
    • Health care
    • Integration
    • Realist evaluation
    • Single-n designs


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