Predictive Analytics in Healthcare

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What would you say if your doctor could predict your health risks before they become a problem? Sounds impressive, considering that we are talking about heart disease or even cancer.

Predictive analytics in healthcare makes this possible. With the ability to use data mining (as well as machine learning), doctors have a powerful tool in their hands to identify risk factors and potential problems that can be prevented in advance. This predictive modeling in medical technology is helping to make the healthcare system more innovative, efficient, and responsive to patient needs.

As of February 2022, 92 percent of surveyed healthcare executives in Singapore reported that they have implemented or are currently implementing predictive analytics in their healthcare organizations, the highest adoption rate of any county survey, according to Statista. China was second on the list, with an acceptance rate of 79 percent, followed by Brazil and the US with 66 percent.

What is predictive analytics in healthcare?

Predictive analytics in healthcare makes it possible to analyze current and archived patient records. It enables better clinical and management decisions to be completed and predicts trends in disease exacerbations, pandemics, and overall health improvements. Big data here are electronic medical records, administrative documents, insurance archives, collections of medical images, etc. All this is needed to build a predictive model based on empirical information.

A typical decision-making sequence is as follows:

  • Collection and structuring of data;
  • Applying statistical models to the collected data and machine learning tools;
  • Identify patterns that give results and choose the best ones.

Benefits of predictive analytics in healthcare

For healthcare professionals, saving lives is a top priority. Accordingly, any means that will help prevent severe conditions and early assistance are the most relevant in medicine. Predictive medicine allows you to create conditions under which the patient's health analytics will be predictable and transparent. Here's how you can do it:

  • Identification of patient groups under health risk. For example, AI in healthcare can identify groups of diabetic patients at risk of hospitalization based on their age, gender, lifestyle, comorbidities, adherence to medical rules, exercise habits, etc.
  • Reducing public health risks through early recognition of outbreaks and other emergencies.
  • Streamlining administrative tasks, speeding up medical response and timely discharge from the hospital, and improving insurance procedures for more efficient distribution of money.
  • Reducing the cost of regular appointments and hospitalizations. Using the power of machine learning and artificial intelligence, we can predict health dynamics and the behavioral patterns of various groups to prepare for potential problems.
  • Streamlining administrative tasks, speeding up medical response and timely discharge from the hospital, and improving insurance procedures to reduce costs and allocate money and effort more efficiently.

The challenges of predictive analytics in healthcare

Without a doubt, predictive analytics in healthcare faces some challenges. It happens because human lives are at stake. The price of a mistake is too high, and it is too difficult to predict everything.

Predictive analytics in the healthcare industry faces the following challenges:

Making clinical decisions

Numerous studies show that we relax when we give AI part of the tasks. In healthcare, introducing predictive analytics could encourage clinicians not to be picky about their tests and make riskier choices. In addition, at the moment, the point at which a doctor can delegate decision-making to a computer needs to be clearly defined. It's unclear who is taking responsibility for a wrong medical decision - a data scientist, a doctor, or both.

Analytics

With Azure, it’s easy to gather, store, process, and analyze any kind of data. Users have access to advanced analytics solutions, including real-time analytics, machine learning, big data analytics, and business intelligence, among others.

Slow recovery

Due to the lack of human interaction, the patient may recover more slowly, and this is the point that cannot be replaced by artificial intelligence. That's why healthcare professionals can only rely on predictive analytics and machine learning software to a certain extent, doing most tasks manually for the sake of their patient's well-being.

Lack of regulation

In most countries, there are no official documents regulating the use of predictive analytics. To some extent, these technologies are self-regulated by associations of physicians; however, clear rules for data collection, processing, and clinical decision-making are vital to the legal protection of patients and caregivers.

Prejudice of the algorithm

Due to different cultures and mentalities, we are not immune to biases, conscious or unconscious. It is why relying on machine decisions as a 100% goal is unwise. Instead, physicians and data scientists must collaborate to pinpoint and circumvent algorithm bias. For example, careful analysis of datasets helps reduce their impact.

To minimize risks, developers of predictive analytics need to:

  • pay close attention to the collection and storage of data
  • have clear medical records
  • create a product that is easy to use and accessible to users

Data Gathering and Cleansing

Collects data from all sources to extract the necessary information through cleaning operations to remove noisy data so that the forecast can be accurate.

Data analysis

Before you build your model, create a simple data chart and explore it. You should understand how the data behaves and the relationships between variables. If you don't, you won't be able to build a good model. However, if you can, you will learn a lot. By creating a simple chart of your data to explore, you can get a good idea of the answer to the problem you're trying to solve based on the overall trend.

Building a predictive model

Sometimes data lends itself to a particular algorithm or model. In other cases, the best approach is not so obvious. While analyzing the data, run as many algorithms as possible and compare their results. Define test data and apply classification rules to test the performance of the classification model against the test data.

Include the model in your business process

To make the model valuable to your healthcare organization, you need to integrate it into your organization's processes so that it can be used to improve patient care.

Predictive healthcare modeling helps improve patient care and deliver beneficial outcomes. It can identify patients at the highest risk and in poor health who would benefit the most from intervention; gain insight into stationary data patterns for developing effective campaigns; predict product safety and optimize dosing; inform about the design of clinical trials, and much more.

Conclusion

Predictive analytics in healthcare requires more than just data. Given its potential to boost healthcare delivery and equipment maintenance, further expansion of predictive analytics in healthcare should be expected. Other applications include predicting and preventing no-shows for better patient scheduling and modeling and managing patient flows in a hospital for optimal staffing and resource allocation.

However, as informative as predictive algorithms are, their impact ultimately depends on their clever use by domain experts—physicians, nurses, engineers, and hospital administrators—who know how to weigh probabilities in the unique context of a patient or healthcare facility. Developing and deploying such algorithms requires expert input and the latest analytical capabilities. Data can help make decisions, but people still should make them.

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