Using Data Mining Techniques for Designing Patient-Friendly Hospitals

Spending a long time in the hospital and the intense circulation between the clinics involve various risks for both patients and medical officials in the hospital. Especially in the COVID-19 outbreak, the minimization of this period is vital because of reducing the risk of transmission. Symptomatic patients can be immediately taken into the isolation and treatment process, while asymptomatic patients continue to spread the disease. Because patients suffering from other diseases like diabetes, cancer, or other chronic illness also have immune system problems, epidemics can be fatal for them. Therefore, especially during the pandemic, patients tend to delay their hospital visits due to the risk of contamination. In this situation, unless they reach the diagnosis and treatment, they will face more serious health problems. If we provide patients uncrowded hospitals and shorter hospital visits, we will reduce the risk of transmission of COVID-19 and other seasonal epidemics. Therefore, it is necessary to determine which clinics are visited frequently by the patients and which clinics and medical units work together in the diagnosis and treatment process. In this chapter, data mining techniques used in healthcare system design are explained and exampled by a real-life case study. Analyzing patient data by using data mining techniques allows us to reach the aim of this chapter. Association rules between clinics and other related medical units like blood-letting and nuclear medicine services are determined. They also reveal the circulation of patients in the hospital. Frequency analysis shows crowded clinics and other medical units. Minimizing this circulation and crowd of patients in the hospital also minimizes the risk of transmission of COVID-19. In this chapter, six months’ data of patients treated in a hospital in Turkey are used. These data include demographic information of the patients, as well as which clinics they visited and how many days they were treated in the hospital. As a result of the data mining analysis, clinics and medical units working together in the diagnosis and treatment process and the most crowded clinics will be determined. Recommendations will be made to reduce the distance between the clinics and units which have associations and increase the service capacities of the most crowded clinics, respectively. Thus, the application of data mining techniques for designing patient-friendly healthcare services is presented.