VAPAHCS is committed to continuous improvement in providing veterans with exceptional healthcare to improve their health and well-being. A key part of the continuous improvement process is the analysis and understanding of veterans' comments and responses to their healthcare experiences.The mission of the VET Project (Veterans Experiences and Thoughts) is to assemble and analyze veterans' feedback, thus providing VAPAHCS with timely sentiment analysis and predictive topic identifications to support the highest quality of veteran healthcare.
Sources for patient feedback come from several avenues including HCAPHS, Press-Ganey, HealthStream, and others. Veterans' comments are collected, organized, and analyzed using a system called Press-Ganey. Press-Ganey categorizes patient's free-form text respnses into positive, negative, or mixed categories, however, it does not provide any detailed analysis of these responses, nor the ability to summarize and statistically analyze large numbers of such responses. This project has reviewed and analyzed nearly two years worth of Press-Ganey free-form veterans' comments gathered by VAPAHCS. The techniques of Medical Informatics has enabled us to develop a suite of Data Analytics to analyze and extract valuable information from the Press-Ganey data far beyond the current sentiment categories that exist.
Sources for patient feedback come from several avenues including HCAPHS, Press-Ganey, HealthStream, and others. Veterans' comments are collected, organized, and analyzed using a system called Press-Ganey. Press-Ganey categorizes patient's free-form text respnses into positive, negative, or mixed categories, however, it does not provide any detailed analysis of these responses, nor the ability to summarize and statistically analyze large numbers of such responses. This project has reviewed and analyzed nearly two years worth of Press-Ganey free-form veterans' comments gathered by VAPAHCS. The techniques of Medical Informatics has enabled us to develop a suite of Data Analytics to analyze and extract valuable information from the Press-Ganey data far beyond the current sentiment categories that exist.