How predictive analytics can improve healthcare

The healthcare sector appears to be set for disruption by way of AI in the form of predictive analysis in medicine. According to Statista, the global market size for AI in healthcare in 2016, 2017 and a prediction for 2025 will increase from about USD 1 billion to over USD 28 billion.

One of the major explanations for this exponential growth is the existence of incredible amounts of medical data in the form of patient clinical information, such as insurance data, diagnosis data, lab results, and R&D data like ones from clinical trials and published papers.

There are numerous instances where big data can be applied in healthcare, from enhancing patient engagement to informing strategic planning and from reducing fraud cases to enhancing security. The goal of AI in healthcare is to assist physicians in making data-driven decisions within seconds and improving the treatment of patients.

This is especially true for patients who have a complicated medical history or are suffering from multiple conditions. For instance, through evaluation, doctors can identify patients who are candidates for laparoscopic gallbladder removal (cholecystectomy) and those who aren’t. Doctors use the patient’s current and past medical information and compare them with other patients data in their databank in real-time, to help come with conclusions.

Widely speaking, there are five significant benefits of predictive analysis in medicine; and are as follows;

Help pharmaceutical firms meet the public needs

The pharmaceutical industry is going to be one of the greatest beneficiaries of predictive analytics soon, as it will provide precise, more evidence-based speculation about the disorders and diseases that are likely to affect many people. Predictive analysis will help pharmaceutical companies to concentrate on producing larger quantities of medicines for those diseases, instead of spending their efforts, time and money manufacturing drugs that won’t be as needed.

Improved accuracy of diagnosis

Another application of predictive analytics in the healthcare industry is assisting doctors to make an accurate diagnosis, promptly. According to patient safety experts, Johns Hopkins, more than 250,000 medical deaths in the United States every year are as a result of medical errors. These deaths are in no way linked to the physician’s performance; instead, they are due to:

  • Inefficient collaboration and adoption of information technology in health
  • Gaps in communication among doctors, patients, and their families
  • Work systems that are not designed to support diagnostic procedures adequately

Unfortunately, these medical errors have the potential to harm the patients in different ways, including preventing or delaying treatment, administering wrong or harmful treatment, or causing financial or psychological issues.

For instance, weight loss could be a symptom of different diseases and disorders. But without efficient adoption and integration of health IT, doctors will have to depend on their knowledge to diagnose the issue and administer care. And while their diagnosis may be up to 96% right, there’s still a 4% room for error.

Predictive analysis solutions act as an extra layer that will help doctors make a more educated diagnosis. By analyzing the previous medical history of the patient, against an intricate databank, the doctor is in a better position to give a more informed diagnosis and prescribe an early treatment plan.

Ability to determine and treat serious diseases faster

The modern lifestyle has exposed people to the ever-growing number of life-threatening conditions and diseases. This places the pressure on researchers and doctors to consistently find more advanced solutions. Sadly, these debilitating diseases are sometimes not accurately diagnosed until when they have already progressed.

But with a combination of predictive analytics and advanced genetics, it’s possible for doctors to determine those who are at risk of these life-threatening diseases and even arm them with the necessary information that they require to take preventative health measures.

Predicting hospital readmissions

Predicting hospital readmission is a hot topic – this can be seen by the number of peer-reviewed articles that are published on the subject. Today, hospitals are using a publically available algorithm to foresee the patients who are likely to go back to the facility within 30 days of discharge for the same condition.

Helps in the development of better prediction models

Medical researchers can use predictive analysis to develop prediction models that may steadily enhance their accuracy and rely on fewer numbers of case studies. It’s essential to realize that the difference between clinical and statistical significance bears a lot of weight in medical research.

For instance, an observational study performed on a huge farming population may reveal that the use of a certain kind of pesticide is making them more prone to a type of infection, while a predictive analysis conducted on a few farmers may show completely different results.

What makes predictive analysis appealing is its ability to deliver accurate results steadily, with more insights and inferences gathered over a period of time.

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