Text Mining Techniques for Healthcare Provider Quality Determination: Methods for Rank Comparisons

The quest for quality in healthcare has led to attempts to develop models to determine which providers have the highest quality in healthcare, with the best outcomes for patients.

Text Mining Techniques for Healthcare Provider Quality Determination: Methods for Rank Comparisons discusses the general practice of defining a patient severity index in order to make risk adjustments to compare patient outcomes across multiple providers with the intent of ranking the providers in terms of quality. This innovative reference source, valuable to medical practitioners, researchers, and academicians, brings together research from across the globe focusing on how severity indices are generally defined when determining the best outcome for patient care.

The many academic areas covered in this publication include, but are not limited to:

• Central limit theorem
• Hospital reimbursements
• Introduction to ranking models
• Kernel density estimation
• Patient severity index
• Predictive modeling based on providers
• Predictive modeling in SAS enterprise miner
• Provider quality measures
• Risk adjustment based upon resource utilization
• Risk adjustment models for provider reimbursements
• Statistical examination of the Charlson Comorbidity Index
• Text mining to define patient severity