Visual analytics: tackling data related challenges in healthcare process mining

Ondimu, Kennedy O.
Lukandu, Ismail A.
Muchiri, Geoffrey M.
Omieno, Kelvin K.
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Strathmore University
Data-science approaches such as Visual analytics tend to be process blind whereas process-science approaches such as process mining tend to be model-driven without considering the “evidence” hidden in the data. Use of either approach separately faces limitations in analysis of healthcare data. Visual analytics allows humans to exploit their perceptual and cognitive capabilities in processing data, while process mining represents the data in terms of activities and resources thereby giving a complete process picture. We use a literature survey of research that has deployed either or both Visual analytics and process mining in the healthcare environments to discover strengths that can help solve open problems and challenges in healthcare data when using process mining. We present a visual analytics (data science) approach in solving data challenges in healthcare process mining (process science). Historical data (event logs) obtained from organizational archives are used to generate accurate and evidence-based activity sequences that are manipulated and analysed to answer questions that could not be tackled by process mining. The approach can help hospital management and clinicians among others, audit their business processes in addition to providing important operational information. Other beneficiaries are those organizations interested in forensic information regarding individuals and groups of patients.
2018 Conference paper presented at Strathmore University. Thematic area(Health, Healthcare Management and Research Ethics)
Healthcare, Visual analytics, Process mining