Temporal Data Mining in AI-based Patient Navigation Service

Temporal Data Mining in AI-based Patient Navigation Service

연구 분야: Databases

학회: 2024 IEEE International Conference on Data Mining Workshops (ICDMW)

Improving patient satisfaction is a challenging problem of large-scale hospital where various patients receive medical consultations across different departments. It is because there are wide variety factors that influences of patient satisfaction, such as quality of medical care, medical staff servicce, waiting time and so on. The pandemic of COVID-19 reminds us of the basic important principles for prevention of infection: avoid the "Three Cs": closed spaces, crowded places and close contact settings. Outpatient clinics in Japan are typical examples of three Cs, where some kinds of decision support system are required to solve the above situation. Although the pandemic has already been solved, these three C factors are still important issues related with medical staff service. This paper proposes data mining based patient navigation support system to prevent the Three Cs. Behind the systems, temporal data mining units plays an important role in providing temporal information to the patients, such as waiting time and human densities in the waiting rooms. It analyzes the data stored in hospital information systems, including patient information, logs of clinical orders. The analysis results show that several aspects of patients’ waiting are visualized by temporal data mining.