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 on both Visual analytics and process mining in the healthcare environments, to discover strengths that can help
solve open problems in healthcare data when using process mining. We present a visual analytics approach in solving data challenges in
healthcare process mining. Historical data (event logs) obtained from organizational archives are used to generate accurate and evidence
based activity sequences that are manipulated and analyzed 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 include those organizations interested in forensic information regarding individuals and
groups of patients.
Kennedy O. Ondimu : is a PhD candidate in information
Technology at Masinde Muliro University of Science and
Technology as well as a lecturer at Technical University of Mombasa. He has published a number of papers and book
chapters in the area of IT. He has wide experience as a lecture,
academic leadership and as director of ICT at University.
Dr. Kelvin K. Omieno : is Lecturer and also Founding and
Current Dean, School of Computing and Informatics (SCI),
Masinde Muliro University of Science and Technology, Kenya
(www.sci.mmust.ac.ke). He holds a PhD in Business Information
Systems of Jaramogi Oginga Odinga University of Science &
Technology (Kenya). He has MSc in Information Technology
and Bachelor of Science in Computer Science (First Class
Honors) from Masinde Muliro University of Science and
Technology (Kenya). Dr. Omieno has been involved in a number
of research projects of ICTs for Development, Data Analytics,
Computational Grid Project, Health Informatics, E-learning
systems and E-waste management in Kenya. Besides, he has
published widely in journals and conference proceedings in
Information technology and ICTs for development. He is a
professional member of the Association for Computing
Machinery (ACM), the largest association of computing
professionals globally and is a reviewer with three International
Journals.
Geoffrey M. Muketha : is a professor of software
engineering at Murang'a University of Technology and current
Dean, school of Computing and Information Technology. He has
published widely as well as supervised several graduate students
at masters and PhD level. His interests are in Software metrics,
automated static code analysis and structural quality of software.
Ismail A. Lukandu : is an Associate professor at Strathmore
University, faculty of Information Technology and current Dean
of research in the University. He has published widely as well as
supervised several graduate students at masters and PhD level.
His interests are in Information Systems, Modeling and
simulation, Database marketing and Information Technology
among others.
Healthcare, visual analytics, process mining, challenges
Complexity and abstraction in healthcare data is a
challenge when using process mining. Using an integrative
approach, a number of previously open problems when
using process mining can be solved using visual analytics.
Three challenges in healthcare process mining including
identification of most followed paths and exceptional
paths; differences in care paths followed by different
patient groups with same diagnosis; and compliance with
internal and external guidelines are solvable using visual
analytics. The ability of visual analytics to reveal evidence
hidden in the data can also help process owners in
operational running.
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health care reforms and policy learning in the
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and Law, Vol. 30, No.1-2, 2005, pp. 189-210.
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a data-based method using a combination of
process mining and visual analytics", MSc thesis,
Technology Management, Eindhoven University
of Technology, Eindhoven, The Netherlands,
2009.
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Authors -
Kennedy O. Ondimu is a PhD candidate in information
Technology at Masinde Muliro University of Science and
Technology as well as a lecturer at Technical University of
Mombasa. He has published a number of papers and book
chapters in the area of IT. He has wide experience as a lecture,
academic leadership and as director of ICT at University.
