Browsing by Author "Johnson, Owen A."

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    Innovative informatics methods for process mining in health care
    (ACADEMIC PRESS INC ELSEVIER SCIENCE, 2022) Muñoz Gama, Jorge; Martin, Niels; Fernandez-Llatas, Carlos; Johnson, Owen A.; Sepulveda, Marcos
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    Process mining in primary care: A literature review
    (2018) Williams, Richard; Rojas Balcazar, Erick; Peek, Niels; Johnson, Owen A.
    Process mining is the discipline of discovering processes from event logs, checking the conformance of real world events to idealized processes, and ultimately finding ways to improve those processes. It was originally applied to business processes and has recently been applied to healthcare. It can reveal insights into clinical care pathways and inform the redesign of healthcare services. We reviewed the literature on process mining, to investigate the extent to which process mining has been applied to primary care, and to identify specific challenges that may arise in this setting. We identified 143 relevant papers, of which only a small minority (n=7) focused on primary care settings. Reported challenges included data quality (consistency and completeness of routinely collected data); selection of appropriate algorithms and tools; presentation of results; and utilization of results in real-world applications.
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    The assessment of data quality issues for process mining in healthcare using Medical Information Mart for Intensive Care III, a freely available e-health record database
    (2018) Kurniati, Angelina Prima; Rojas, Eric; Hogg, David; Hall, Geoff; Johnson, Owen A.
    There is a growing body of literature on process mining in healthcare. Process mining of electronic health record systems could give benefit into better understanding of the actual processes happened in the patient treatment, from the event log of the hospital information system. Researchers report issues of data access approval, anonymisation constraints, and data quality. One solution to progress methodology development is to use a high-quality, freely available research dataset such as Medical Information Mart for Intensive Care III, a critical care database which contains the records of 46,520 intensive care unit patients over 12 years. Our article aims to (1) explore data quality issues for healthcare process mining using Medical Information Mart for Intensive Care III, (2) provide a structured assessment of Medical Information Mart for Intensive Care III data quality and challenge for process mining, and (3) provide a worked example of cancer treatment as a case study of process mining using Medical Information Mart for Intensive Care III to illustrate an approach and solution to data quality challenges. The electronic health record software was upgraded partway through the period over which data was collected and we use this event to explore the link between electronic health record system design and resulting process models.
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    The ClearPath Method for Care Pathway Process Mining and Simulation
    (2019) Johnson, Owen A.; Ba Dhafari, Thamer; Kurniati, Angelina; Fox, Frank; Rojas, Eric
    Process mining of routine electronic healthcare records can help inform the management of care pathways. Combining process mining with simulation creates a rich set of tools for care pathway improvement. Healthcare process mining creates insight into the reality of patients’ journeys through care pathways while healthcare process simulation can help communicate those insights and explore “what if” options for improvement. In this paper, we outline the ClearPath method, which extends the PM2 process mining method with a process simulation approach that address issues of poor quality and missing data and supports rich stakeholder engagement. We review the literature that informed the development of ClearPath and illustrate the method with case studies of pathways for alcohol-related illness, giant-cell arteritis and functional neurological symptoms. We designed an evidence template that we use to underpin the fidelity of our simulation models by tracing each model element back to literature sources, data and process mining outputs and insights from qualitative research. Our approach may be of benefit to others using process-oriented data science to improve healthcare.