Identifying the patient types with different economic values can be useful for hospital development.
This work uses the theory of customer relationship management (CRM) to analyze the outpatients in the hospital for infectious diseases in Shanghai, China.
A total of 2,271,020 data elements of outpatients in the research unit between August 2009 and December 2019 were extracted, analyzed and cleaned to obtain 171,107 valid data elements (1 element per person). The main diseases were viral hepatitis B (VHB) and acquired immunodeficiency syndrome (AIDS), and the average percentage of drug expenditure was 80.39 %. We innovatively expanded the classic RFM (R: recency, F: frequency, M: monetary) model in CRM to the dRFM (d: percentage of drug expenditure) model. We selected the best clustering algorithm from the K-means, Kohonen and two-step clustering methods to find the optimal model to distinguish the types of patients with different economic values and the best decision-making algorithm from the C5.0, CART classification regression tree, CHAID and QUEST algorithms to verify the model.
After performing two rounds of K-means clustering analysis on three models: RFM, RFM + dRFM and dRFM, and 97,855 data elements were retained. The RFM + dRFM model was the optimal model, clustering the patients into 3 types: potential patients (24.2 %) to be retained, with a high drug expenditure and the last visit in more than 19.06 months, high-value patients (24.5 %) to be attracted, with the last visit in about 6.66 months; basal patients (51.3 %) to be kept, with the last visit in about 3.7 months. The model was then verified using the C5.0 decision tree algorithm with an accuracy rate of 99.97 %.
This objective CRM analysis of the patients in the hospital for infectious diseases using the dRFM model accurately identified different types of patients, providing an objective and effective basis for hospital management.