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How Predictive Analytics Impacts the Future of Healthcare

Sergey Avdeychik

Sergey Avdeychik

Director of Healthcare Technologies at Andersen

Healthcare
Nov 16, 2022
6 minutes to read
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The decisions doctors make are a question of life and death. They relate to everyone. To make correct decisions, medical care professionals need to process and analyze enormous volumes of data, with every detail taken into consideration. Unfortunately, human factors and human errors are always at play. People, even the most experienced and highly qualified ones, make mistakes. In this aspect, predictive modeling comes to the rescue. Indeed, predictive analytics in healthcare is already helping medical professionals mitigate risks. Available statistics corroborate this statement. As the experts employed by Acumen Research and Consulting stress, back in 2021, the global predictive analytics market in healthcare amounted to $9.5 million. Around 2030, it is projected to hit $87.5 billion. As for the current rates of use of predictive analytics in healthcare, per Statista, they are also impressive:

  • Global average — 56%;
  • Singapore — 92%;
  • China — 79%;
  • US — 66%;
  • India — 59%;
  • Australia — 55%.

As you can see, predictive health analytics solutions are already in high demand. Below, we will explore the most important aspects of this growth. Our extensive and multi-faceted experience in this domain undoubtedly makes us a reliable source of expertise.

What is predictive analytics in healthcare?

It is the process of analyzing both present-day data and historical records and trying to identify, on this basis, new opportunities to make more workable clinical and managerial decisions, foresee trends, fight pandemics, and improve overall health. As you might have guessed, the sources of Big Data required for that may include EHRs, administrative papers, insurance archives, collections of medical images, etc. Whatever combination of sources you may be using, the ultimate goals remain the same. Namely, to build a predictive model relying on empirical information. Hence, the typical sequence the solutions perform is:

  • Mine and structure data;
  • Apply both existing statistical models and ML tools to that data;
  • Specify and describe the resulting patterns to ensure the best possible outcomes.

Benefits of predictive analytics in healthcare

The key mission of medical professionals is to save people’s lives and cure them when they get ill. Their second priority is to help people stay healthy so that treatment and interventions aren’t needed. Finally, healthcare is expected to stay affordable, cost-effective, and transparent as a system. Predictive analytics in healthcare helps us achieve these requirements by:

  • Identifying the cohorts of patients and potential patients under the greatest risks. While it is not impressive to deduce that chain smokers