Data-Guided Healthcare Decision Making

This book effectively exposes and illustrates the ideas and tools for optimal healthcare decisions taken from evidence.

Ramalingam Shanmugam (Author)

9781009212014, Cambridge University Press

Hardback, published 22 June 2023

500 pages
26.1 x 21.1 x 2.9 cm, 1.41 kg

How does data evidence matter in decision-making in healthcare? How do you implement and maintain cost effective healthcare operations? Do decision trees help to sharpen decision making? This book will answer these questions, demystifying the many questions by clearly showing how to analyse data and how to interpret the results – vital skills for anyone who will go on to work in health administration in hospitals, clinics, pharmaceutical or insurance industries. Written by an expert in health and medical informatics, this book introduces readers to the fundamentals of operational decision making by illustrating the ideas and tools to reach optimal healthcare, drawing on numerous healthcare data sets from multiple sources. Aimed at an audience of graduate students and lecturers in Healthcare Administration and Business Administration courses and heavily illustrated throughout, this book includes up-to-date concepts, new methodologies and interpretations using widely available software: Excel, Microsoft Mathematics, MathSolver and JASP.

1. Why and how healthcare decisions are made
2. Are data guided healthcare decisions superior?
3. Software: EXCEL, Microsoft Mathematics and JASP
4. How to collect authentic data
5. Uncertainties and their impact on healthcare decision
6. Why models are important in healthcare
7. How healthcare decision trees emerge and function
8. How are group decisions practiced in healthcare?
9. Tracing and remedying root causes of adversities
10. Healthcare decision making for cost-effectiveness
11. Risk analysis in healthcare decision making
12. Evaluation of healthcare programs
13. Six sigma and lean management in healthcare sectors
14. Forecasting in healthcare sectors.

Subject Areas: Epidemiology & medical statistics [MBNS]