Florence Nightingale was the first to use statistics during the Crimean War. Her findings, which included the introduction of the idea of evidence-based medical practice, helped to shape the modern medical field. Since then, statistics have become increasingly more important in understanding disease processes, predicting outcomes & evaluating interventions/treatments with regards to both efficacy & safety.
Clinical risk stratification is an example of how statistical analysis has been used in recent years to influence health care practices and operations. The mathematical equations use factors like age, gender and co-morbidities to calculate numerical scores that can be used to categorize patients according to their likelihood of adverse events, such as hospital readmissions, or not responding to certain treatments. This information can then be used by health care providers & payers alike for purposes such as resource allocation decisions or targeted prevention initiatives across large populations – ultimately leading to improved quality & cost efficiency throughout all levels of delivery systems