5 Examples of Predictive Modeling Usage in Healthcare

5 Examples of Predictive Modeling Usage in Healthcare

There are diverse applications of predictive models at hospitals and healthcare companies, but we will highlight the five most important of them at this stage of medical science development. Our list covers the health insurance field and other branches of medicine that currently rely on forecasting technologies the most. 

Health Insurance

Predictive modeling in health insurance allows you to calculate the accurate cost of insurance for a specific individual. It is possible to define how reasonable it would be to provide a particular medical insurance plan to the applicant, considering such parameters as insurance case history, age, gender, region, medical history, heredity, bad habits, etc.  

Medical Imaging

Radiology is another field that shows the high efficiency of artificial intelligence and machine learning models. One of the most prominent examples is the University of Montreal Hospital Centre. It uses the AI solution that notices anatomical changes in individual patients and identifies disease-specific markers based on X-ray photographs. This solution also helps to prepare patients for surgical interventions based on predictions.

The use of prognostic software in radiology is especially relevant in lung screening and breast cancer diagnostics. X-rays data is used to predict the patient’s exposure to viral diseases affecting the lungs and help doctors focus primarily on the most vulnerable patient categories. Prognostic models and Digital Breast Tomosynthesis (DBT) provide the full picture of breast anatomy and allow detecting breast diseases early.

Palliative Care

Penn Medicine is one of the most reputed academic medical centers that use predictive models based on electronic health records (EHR). These models let doctors forecast health risks for patients with life-threatening diseases. This model was named Palliative Connect and based on 30 factors for predicting patient status. Palliative Connect can make predictions about a patient for the next six months after admission to the hospital. The machine learning algorithms help doctors focus on people with a high risk of mortality to improve palliative consultations. The Penn Medicine research aims to achieve favorable patient outcomes and reduce the death rate of the population.

Mental Health

According to WHO, almost 800,000 people end their lives by suicide, and over 20 million make self-kill attempts every year. Numerous factors cause most people to suffer from chronic stress, the worst outcomes of which can be severe depression, self-aggression, and suicide. To keep people mentally healthy, various medical organizations and scientists implement predictive solutions based on EHR and mental health visits of a specific patient. 

The prediction-based system analyzes data on both people who have committed suicide and living patients. In this way, it identifies people at high risk of committing or attempting suicide. As a result, it is possible to provide these people with timely support through helper applications and qualified therapists, thus significantly reducing the percentage of self-harm around the world.  

Pharmacy Services

The pharmaceutical industry is one of the first to adapt to changes caused by fierce competition between such companies on the market. Pharmacies mostly use predictive modeling to optimize marketing and sales. They help to forecast medication demand, customer churn, next purchases of a particular patient, and consumer preferences.

However, in some cases, even the most demanded drug can be ineffective for a particular person. Therefore, the pharmacists’ goal is to use predictions to provide the necessary medication to the right patients at the right time. For this purpose, pharmaceutical companies rely on EHR data and new clinical data demonstrating the efficacy of a particular drug in curing atypical forms of well-known diseases.

The next part of this article will unfold the five main benefits of Predictive Modeling in Healthcare, such as: improved diagnostics, high cost-effectiveness, enhanced operational efficiency, decreased re-admission rates, and personalized medical care.

Contact us

Similar Posts