Application of process mining in healthcare logistics: a systematic literature review protocol.
systematic_review · Level I
Where this comes from
- Record sourced from PubMed, PMID 41688120.
- Also identified by DOI 10.1136/bmjopen-2025-113096 and PMC identifier 12911781.
- Licence recorded as CC BY-NC.
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Abstract
Healthcare logistics involves the coordination of resources, services and infrastructure to ensure timely and efficient care delivery. Process mining offers data-driven insights into logistical workflows such as patient transport, inventory management and scheduling. This systematic review aims to synthesise evidence on the application of process mining in healthcare logistics, focusing on its impact on operational efficiency, resource utilisation and service delivery. A systematic search will be conducted in MEDLINE, Embase, Google Scholar, Web of Science and ABI/Inform for studies published from 1999 onward. Eligible studies will include observational studies, case reports, conference papers and meta-analyses focusing on process mining applications to logistical processes in healthcare settings. Studies screening, data extraction and methodological quality assessment will be conducted using the Mixed Methods Appraisal Tool. Data will be extracted on key dimensions and performance indicators and will be presented in a structured format. A narrative synthesis will be conducted, and findings will be categorised and thematically analysed where appropriate. Primary outcomes include improvements in logistical efficiency, traceability, resource utilisation and sustainability. Secondary outcomes include implementation challenges, data integration issues and limitations in applying process mining techniques to logistical workflows. The results of the systematic review will be disseminated via publication in a peer-reviewed journal and presented at a relevant conference. The data we will use do not include individual patient data, so ethical approval is not required. CRD420251164812.
Medical subject headings
- Delivery of Health Care
- Data Mining
- Efficiency, Organizational