The implementation of healthcare Information System (HIS) has had a major impact on the interpretation of the vast amount of data available in the medical field. This has led to techniques relating to data science such as process mining (PM) which has emerged as a set of methods and tools aimed at discovering and analysing process execution. Researchers have capitalized on this to solve problems are relating to time delay in diagnosis and treatment process. However, there still areas in the healthcare that lacks the application of PM to better discover and analyse process executions. One such area is tuberculosis (TB), the disease which has been the cause of considerable morbidity and mortality worldwide. Early diagnosis and prompt initiation of treatment are essential for an effective TB control program. Delayed diagnosis and treatment of tuberculosis TB leads to greater morbidity and mortality and can also increase the rate of infection within the community. Moreover, with PM as a promising technique to combat the above-mentioned problems there are still issues with applying PM to the existing data in the HIS. Since event data is typically not stored in a process-oriented manner, an event log should be generated first. Literature shows that event log generation takes a substantial effort in PM projects. Our goal was to design and develop a software system capable of collecting and storing event log data for TB patients as this would bridge the identified gaps. The problem of lack of data with timestamps of events has been a major concern to TB specialist and process miners. The time at which each event occurs in the diagnosis and treatment of TB is crucial if the disease behaviour, diagnosis, and treatment pathways are to be understood. Therefore, this study designed and developed a software system called ELS using Design Science Research (DSR) methodology and using the JavaScript Node.js platform that can track the underlying processes in the diagnosis and treatment of TB and thus stored the data and timestamps in a secured databases and cast the data as events log for the purpose of adequate mining and conformity checks of the process of TB. The results obtained shows that the database developed could assist to unravel the diagnosis and treatment of TB from data analysis perspective.