Name: Healthcare Quality Measurement. Volume 1, History and Practice of Healthcare Quality Measurement (en Inglés)
SKU: 67320757
Price: 105940.00 CLP
Availability: InStock
Author: Sharon-Lise T. Normand;David M. Shahian
ISBN: 9780521718110

portada Healthcare Quality Measurement. Volume 1, History and Practice of Healthcare Quality Measurement (en Inglés)

Healthcare Quality Measurement. Volume 1, History and Practice of Healthcare Quality Measurement (en Inglés)

Formato

Libro Físico

Editorial

Cambridge University Press

Autor

Sharon-Lise T. Normand;David M. Shahian

Año

2026

Idioma

Inglés

N° páginas

650

Encuadernación

Tapa Blanda

Dimensiones

22.9x15.2x3.3 cm

ISBN13

9780521718110

Healthcare Quality Measurement. Volume 1, History and Practice of Healthcare Quality Measurement (en Inglés)

Sharon-Lise T. Normand;David M. Shahian (Autor) · Cambridge University Press · Tapa Blanda

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Reseña del libro "Healthcare Quality Measurement. Volume 1, History and Practice of Healthcare Quality Measurement (en Inglés)"

This comprehensive guide presents a data science approach to healthcare quality measurement and provider profiling for policymakers, regulators, hospital quality leaders, clinicians, and researchers. Two volumes encompass basic and advanced statistical techniques and diverse practical applications. Volume 1 begins with a historical review followed by core concepts including measure types and attributes (bias, validity, reliability, power, sample size); data sources; target conditions and procedures; patient and provider observation periods; attribution level; risk modeling; social risk factors; outlier classification; data presentation; public reporting; and graphical approaches. Volume 2 introduces causal inference for provider profiling, focusing on hierarchical regression models. These models appropriately partition systematic and random variation in observations, accounting for within-provider clustering. Item Response Theory models are introduced for linking multiple categorical quality metrics to underlying quality constructs. Computational strategies are discussed, followed by various approaches to inference. Finally, methods to assess and compare model fit are presented.