Sherlock Holmes famously says: “I never guess. It is a capital mistake to theorize without data. Insensibly, one begins to twist facts to suit theories, instead of theories to suit facts.”
Holmes would have approved of predictive analytics. The field has come into its own with growing volumes of data, high-speed computing, and appropriate software. In a nutshell, predictive analytics is about using past data to make forecasts about the future.
According to one report, the global predictive analytics market was valued at USD 5.7 billion in 2019. It is set for growth and is expected to generate revenues to the tune of USD 22.1 billion by the end of 2026.
Frequently, those who employ predictive analytics make use of statistical models. Such models are objective, based on verifiable information, and organize data to suggest likely outcomes. Nowadays, Artificial Intelligence is playing an increasingly significant role in analysis.
In his award-winning book Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, Eric Siegel writes: “An organisation that doesn’t leverage its data in this way is like a person with a photographic memory who never bothers to think.”
Predictive analytics is used in many fields. Here are a few examples.
Finance companies use it to detect fraud, measure credit risk, maximize selling opportunities and build loyalty.
Retailers use predictive analytics for merchandise planning, setting prices, studying promotions' impact, and managing inventory.
Manufacturers can study ways to boost quality, prevent production logjams, and enhance distribution and service.
In healthcare, predictive analytics has many valuable, life-saving applications. These benefit government health agencies, doctors, hospitals, and primary health providers.
For years, healthcare and medicine have progressed because of the results of experiments, research, and patient recoveries. Predictive analytics is an exciting new tool that can enable even more progress.
Some developments that have set the stage for predictive analytics in healthcare are:
Digitization of health records
Access to Big Data
Cloud storage solutions
Increased use of mobile devices and trackers.
Predictive analysis techniques bring about greater operational efficiency and accurate diagnosis and treatment.
With this comes further insights into preventing and controlling diseases and saving lives.
At an individual level, predictive analytics can provide modelling for mortality rates. In addition, the models can help doctors treat new and unfamiliar diseases by finding correlations and patterns. They can then prescribe appropriate treatments and medicines.
Predictive analytics is also being used to assess the risks of surgery. First, it considers the patient’s current condition, medical history, and medicines. Then, it matches the information with data from similar cases.
Predictive analysis techniques bring about greater operational efficiency and accurate diagnosis and treatment.
In hospitals, the use of predictive analytics can anticipate the need for emergency care or a potentially serious health condition before it develops. Naturally, this can save lives and keep patients healthier.
Big Data can create geographic, demographic, and medical profiles with communities in the outside world. This way, predictive analytics can help health organizations and official agencies generate forecasts. Such early steps can save lives and reduce pressure on resources.
The above was a general overview of the value of predictive analytics in healthcare. Now, let us turn to some specific use cases to clarify the issue.
To begin with, the more data there is, the better predictive analytics will work. This observation is also accurate in healthcare. Institutions and agencies should collect data from every possible source - whether from state-run or private hospitals.
Hospitals can pool and unify data across departments for better insights into a patient’s medical condition. Novel ways of treatment can emerge, breaking away from specialized data that may miss the bigger picture.
Outside factors known as social determinants can be fed into predictive models for comprehensive healthcare. Such data include community behaviour, specific locations, and income and education levels.
Social determinants can also be incorporated into models to create probabilities of patients skipping appointments or not taking medicines in a prescribed manner. These can lead to specific actions that conserve resources and improve health outcomes.
That apart, predictive analytics can be valuable at granular and individual levels. For example, data from bio-sensors can predict the best ways to set up ICUs. Equipment malfunctions can be anticipated, and the field of genetics can help in natal care.
Carly Fiorina, the former CEO of Hewlett Packard, once said: “The goal is to turn data into information, and information into insight.” In future, the increasing availability of healthcare and medicinal data will enable predictive analytics to come up with even more precise, life-saving insights.