Predictive analytics in healthcare: a game-changer or far-fetched dream?

Predictive analytics has become a key element of healthcare informatics. In the past, predictive analytics was used to forecast demand for various products and services, but it has evolved to include many more applications, including predicting the future performance of a treatment or medication.

According to PR Newswire, the market for predictive analytics in healthcare is projected to arrive at over $67 billion by 2030. Thanks to its unrivaled forecasting potential, the technology steals the limelight among other innovations in healthcare.

Today, we’ll have a closer look at the technology and its real-life benefits for the healthcare industry.

Fundamentals of predictive analytics

Predictive analytics is the use of algorithms, data mining, and statistical techniques to predict future outcomes. It can be used by healthcare organizations to improve the quality and safety of patient care by making better decisions about treatments.

In simple words, machine learning algorithms can be programmed to provide insight into the types of treatments that will be most effective for the current patients by looking at data and outcomes of previous patients.

How does it work?

Predictive analytics is a type of data analysis that uses historical data to make predictions about future trends. This type of analysis can be used to identify patterns and trends that can help businesses make better decisions about things like marketing, product development, and customer service.

The technology relies on a variety of data sources, including customer data, financial data, and market data. This data is then analyzed to look for patterns and trends. Once these patterns and trends are identified, they can be used to make predictions about future customer behavior, market trends, and more.

In the healthcare industry, the input may encompass any kind of patient-generated data – from treatment results to individual vitals.

To generate a forecast for any field, predictive analytics goes through five steps. These include:

  1. Determining the purpose of the analysis – to identify the kind of data to be collected.
  2. Collecting data from multiple sources – to make a more accurate forecast, data points should be cleansed and standardized. 
  3. Analysis using statistical tools – there are ready-made solutions for this purpose, but some companies prefer to create custom software for their own needs.
  4. Modeling – analysts identify dependencies and factors influencing the behavior of indicators, and build a model with a prediction.
  5. Applying the prediction – the model is trained on new data and the forecast is adjusted based on the evolving input.

What are the main benefits of predictive analytics in healthcare?

Predictive analytics has the potential to transform healthcare by improving the quality of patient care and reducing costs. Here are some of the main benefits of predictive analytics in healthcare:

The ability to predict outcomes of treatments and procedures

Predictive analytics can help clinicians figure out how likely a patient is to have a particular health problem or respond well to a particular treatment. It can also help identify potential problems early, such as when someone is at high risk for cancer or heart disease based on age, family history, or other factors. This can prevent unnecessary tests and treatments and save money on care costs.

Better patient outcomes

Predictive analytics can help clinicians identify which patients are at higher risk for complications from surgery or chemotherapy, which will improve their chances of receiving better or more proactive treatment. It can also identify patients who are suffering from complications due to drug side effects, allowing physicians to prescribe more effective medications that aren’t harmful to these patients’ bodies.

More efficient allocation of resources

Predictive analytics can help hospitals to make better decisions about how to allocate their resources between different types of patients (e.g., those who might benefit from a specific type of medical intervention versus those who might not). This can help hospitals make more informed decisions about where to invest in more expensive treatments or high-tech equipment, versus lower-cost options that are still beneficial for some patients.

Reduced costs for healthcare facilities and insurers

Predictive analytics can also help reduce costs for hospitals and insurance companies through improvements in productivity, reduced waste, and greater efficiency in allocating resources.

Another benefit of predictive analytics is that it allows healthcare providers to take action based on real-time analysis rather than waiting until after an event has already occurred, which can be costly in terms of both time and money.

What are the main applications of predictive analytics?

The application area of predictive analytics in healthcare is immense. Therefore, the healthcare industry relies on technology to reduce costs, optimize resources, and improve health practice efficiencies.

Patient and health data analysis

In the last few years, there has been a dramatic increase in the amount of data being generated from the healthcare industry. This data is becoming more and more valuable, but it is also growing at a faster pace than ever before.

The value of this data can be seen in the explosion of new products and services that are being created using predictive analytics. The use of predictive analytics in healthcare can be broken down into two categories: 

  • identifying trends and patterns from past data, and 
  • making predictions about future patient outcomes based on those patterns and trends.

Therefore, this AI-based technology can step in to analyze a vast patchwork of data – from disease data to drug interactions – to improve patient outcomes and promote precision medicine.

Predicting missed appointments

The healthcare sector suffers an average yearly loss of $150 billion due to missed appointments. For each hourly visit, that comes to about $200. Care teams also apply predictive analytics on clinics’ past appointment data to forecast no-shows. This helps save time and resources for healthcare personnel.

Detecting claims fraud

By analyzing past data patterns and comparing them with the new input, AI-based systems can detect suspicious activity during claims management. In other situations, data engineers create a checklist with ratings for the various fraud indications. The claim is then checked against these criteria. The indicators and thresholds should be evaluated statistically and recalibrated on a regular basis.

The final word

As the amount and complexity of data grow, healthcare practitioners struggle to make sense of critical patient data. Powered by AI, predictive analytics helps care teams drill down into patient-generated input and produce actionable insights to provide better healthcare and amplify decision-making. 

Today, the technology is making inroads in the field of clinical analysis, disease progression monitoring, insurance reimbursement, and other data-driven areas. In the upcoming years, the market of predictive analytics is poised to grow its dominance in drug development, hospital overstays, and precision medicine.