• continuous quality improvement
• quality improvement
• quality measurement

The Tour de France is revered as one of sports’ most challenging events. Team Sky, a cycling team from the UK, recently captured its sixth Tour victory in 7 years, a feat that makes it one of sport’s greatest dynasties. Dave Brailsford, the coach and architect behind Team Sky, became the Billy Beane of the cycling world by using advanced analytics to identify areas where his team could make marginal gains in their performance producing substantial improvements in outcomes. Beane’s success using this approach with the Oakland Athletics was made famous by a book and movie Moneyball and has been followed by many major success stories in the sporting world over the last decade revealing a similar trend. National Basketball Association players use heat maps and wearable devices to improve shooting accuracy. National Hockey League teams optimise line performance using entropy mapping. Moreover, professional soccer teams develop novel metrics to assess fatigue in an attempt to reduce injury. These examples have made the term ‘advanced analytics’ a household expression, a term essentially encompassing the use of automated collection and examination of big data using tools such as artificial intelligence, neural networks and machine learning to analyse otherwise hidden areas of potential performance improvement. Advanced analytics differs from basic analytics in that it involves more granular data, sometimes derived from machine learning, and more sophisticated statistical techniques. Table 1 shows some examples of differences between the two types of analytics in both sports and healthcare.

Healthcare is similar to professional sports in many ways, and examples of how high-functioning sports teams, such as those of Formula 1 teams, may pave the way for healthcare improvement has sparked widespread discussion.1 Akin to athletes, clinicians make quick consequential decisions in …