Introducing CRISP-PIP: A Cross-Industry Standard Process Framework to Empower Translation of Big Data into Actionable Population Insight Products for Healthcare
Year: 2025
Type: article
Abstract: Healthcare is generating vast amounts of data, offering opportunities to improve care quality, efficiency, and outcomes. Big Data Analytics enables the transformation of complex data into actionable tools - Population Insight Products (PIPs) - such as dashboards or predictive models. These tools can support evidence-based, participatory decision-making. Yet, Big Data Analytics adoption in healthcare is hindered by technical, organisational, legal, and user-related challenges. Existing frameworks like CRISP-MED-DM offer a process for data mining in healthcare but lack integration of open science principles and stakeholder collaboration. To bridge this gap, we developed CRISP-PIP - a participatory framework aligned with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles to guide the structured development and implementation of PIPs.
Authors: Berends-Hoekstra, Wikje; id_orcid 0009-0006-6686-1809, Peters, Lilian L.; id_orcid 0000-0003-2342-0799, Weiland, Stella; id_orcid 0000-0003-3708-6672, van der Veen, Adriëlla; id_orcid 0009-0001-1880-3851
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7001176798
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