In the healthcare sector, our ability to interpret data, analyse behaviours and predict trends continues to evolve as technology advances. Today, analytics and predictive analytics are embedded into our everyday existence, which provides exciting opportunities to transform the quality of care we offer patients – now and in the future.
Looking for signs
The desire to know what the future holds is by no means a new concept. To understand where we are now, it is useful to look back to the humble beginnings of predictive analytics.
Throughout ancient history, different cultures had their own weird and wonderful ways to predict the future. The Greeks inspected the organs from sacrificial animals for good omens, the Chinese cracked open animal bones with hot metal rods and looked for signs, and the Romans practised ‘ornithomancy’, divination using birds.
It wasn’t until the birth of probability mathematics in the mid-17th that there was a shift away from these traditional approaches towards mathematically derived predictions.1
The birth of probability mathematics
In 1654, two mathematicians, Blaise Pascal and Pierre de Fermat debated how to divide a winning stake between two gamblers whose game of heads or tails was interrupted.2 To solve the problem, the two mathematicians considered all possible outcomes for the unfinished game and reasoned which player would be the winner. This marked the birth of modern probability theory as we know it.2
We have come a long way since then.2 The acceptance of benchmarks for testing hypotheses were built on these early foundations and strengthened the validity of mathematically derived predictions.2
Predictive mathematics has allowed us to make several prognoses of note with remarkable accuracy.2 We have successfully predicted the existence of Neptune, black holes and even the trajectory of a comet with such accuracy we were able to land a space probe on it.2
AI and the future
Our progress in mathematical analysis has led to an explosion in its application. Today, analytics, often referred to as Artificial Intelligence (in its ‘narrow’, ‘general’ or ‘super’ forms), or Machine Learning, are commonplace across almost all aspects of our daily lives.2-4
They are used to predict changes in emergency hospital care needs and the stock market, to assess risk for insurance and to forecast the weather.2,4,5 These principles have been implemented across a wide range of industries including sport, music and law enforcement.2,5,6 Analytics have even been used to make predictions about our future actions before we know about them ourselves.
In 2013, Facebook engineer Lars Backstrom and Jon Kleinberg of Cornell University teamed up to publish a paper about what makes a long-term relationship successful. They highlighted mutual online friends and photos of the couple as key contributors.7 A little worryingly, the duo could predict the breakdown of relationships with 60% accuracy.7
As technology has evolved, it has become more entrenched in our private lives, and in doing so, our personal information has become more available than ever before.7 As a result, the potential for predictive analytics continues to expand.
Want to learn more? See our article Power in Prediction – Part 2: a need for data and analytics in the future.