Improving Healthcare System Though Process Mining at Tikur Anbessa Specialized Hospital
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Date
2024-10
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Addis Ababa University
Abstract
The quality of hospital services depends on the effective execution of healthcare processes, which encompass a variety of clinical and non-clinical activities performed by diverse resources. These processes are dynamic, complex, and multi-disciplinary, necessitating a deep understanding of improvement. Process mining offers promising techniques for visualizing and analyzing healthcare processes to enhance efficiency and quality. This study therefore aims to apply process mining techniques to discover healthcare process models, identify deviations and inefficiencies, and optimize resource allocation within the hematology department of a healthcare organization.
The process model followed in this research includes data extraction from Tikur Anbessa Specialized Hospital (TASH), data preprocessing using aggregation, temporal approach and simple heuristic, process discovery using heuristics mining and inductive mining, and model evaluation based on fitness, precision, generalization, and simplicity. Control flow, performance and organizational analyses are also conducted, followed by validation of findings through expert
collaboration.
The analysis highlights the most common pathway for hematology patients begins with a laboratory request, followed by a laboratory test, then a hematology diagnosis, and finally a prescription, highlighting the interconnectedness of these processes. However, discrepancies between the number of laboratory requests and completed tests, coupled with an average test duration of 32 days, significantly above World Health Organization (WHO) benchmarks, reveal
inefficiencies, particularly in resource allocation. Comparative analysis using heuristic and inductive miners demonstrated the inductive miner showing superior fitness, precision and simplicity, with the heuristic miner achieves a slightly higher in generalization. Social Network Analysis (SNA) identified strong interdepartmental interactions, especially in the diagnosis and radiology departments. The proposed process improvement framework was well-received,
achieving an overall mean evaluation score of 4.2 and a Cronbach’s alpha of 0.747, indicating its reliability. These findings emphasize the complexity of healthcare processes and the importance of continuous improvement through integrated systems. Future research should address challenges in data quality issue to further enhance the utility of process mining in healthcare settings.
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Keywords
Process Discovery, Process Mining, Process Discovery Techniques, Healthcare Service, Process Improvement