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For a long time, healthcare providers have struggled to find the necessary means to provide their patients with the best healthcare. But traditional healthcare facilities and techniques became hurdles for healthcare providers to treat their patients. Over the past few years, the healthcare industry has witnessed a remarkable transformation and growth in providing quality healthcare facilities with technological intervention. As healthcare companies and organizations keep developing new technologies and facilities with the help of AI and machine learning, predictive analytics in healthcare have quite successfully proven itself to be an integral part of the current healthcare system.

Rather than simply providing the medical history of patients, the causes, and treatments of the diseases, predictive analytics in healthcare estimate the possibility of a future outcome, based on past medical records of the patients. It allows doctors, physicians, and other healthcare providers to stay alert about any potential complexities that the patients might face and make decisions based on that information.

The machine learning analysis of large data sets creates predictive models and algorithms, which help to intercept the health conditions of patients. It enables earlier intervention in complex cases and identifies the most effective treatments based on the patient's condition. By comparing the medical records of the different patients in the database, doctors can provide accurate information and personalize treatment plans.

The use of predictive analytics in healthcare has benefitted healthcare providers and patients to a great extent. Here are some other ways in which predictive analytics is improving healthcare.

  • Predicting Chronic Diseases: The rising population in the world made it tough for the healthcare authorities to keep track of the growth of chronic diseases and the general health of the population. Now healthcare organizations are using AI-based predictive health analytics to study and analyze the population's health through data analysis and machine learning to control the growth of chronic diseases and predict any such cases. 
  • Preventing Suicide and Patient Self-Harm: Possible early detection of individuals who are likely to harm themselves ensures the patients receive the medical attention they require avoiding serious events leading to suicide. According to reports, using predictive algorithms to detect patients' mental health has proven 200 times more effective in avoiding suicide attempts than the top 1% of the patients flagged. 
  • Predicting Epidemic Situations: The use of predictive analytics in healthcare has now made it possible to detect epidemics before it even begins. Health organizations analyze infectious diseases using the data available in their system based on population density, economic status, reported and confirmed cases, and weather reports. This data can massively influence the patient's treatment and save unnecessary expenditure at the same time.

The future of Predictive Analytics

Predictive analytics in healthcare has proven to be a positive influence for all the involved parties. Chronic diseases like cancer, heart diseases or failure, and other health issues will be curable with the routine implementation of predictive analytics in the treatments. Predictive analytics in medicine will also prove useful in detecting the effect and usefulness of a medicine for a particular treatment.