Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.