A hybrid IT framework for identifying high-quality physicians using big data analytics

Patients face difficulties identifying appropriate doctors owing to the sizeable quantity and uneven quality of information in online healthcare communities. In studying physician searches, researchers often focus on expertise similarity matches and sentiment analyses of reviews. However, the quality is often ignored. To address patients' information needs holistically, we propose a four-dimensional IT framework based on signaling theory. The model takes expertise knowledge, online reviews, profile descriptions (e.g., hospital reputation, number of patients, city) and service quality (e.g., response speed, interaction frequency, cost) as signals that distinguish high-quality physicians. It uses machine learning approaches to derive similarity matches and sentiment analysis. It also measures the relative importance of the signals by multi-criterion analysis and derives the physician rankings through the aggregated scores. Our study revealed that the proposed approach performs better compared with the other two recommend techniques. This research expands the boundary of signaling theory to healthcare management and enriches the literature on IT use and inter-organizational systems. The proposed IT model may improve patient care, alleviate the physician-patient relationship and reduce lawsuits against hospitals; it also has practical implications for healthcare management.