5 Benefits of Predictive Modeling in Healthcare

The medical industry owes its fast development to digital transformation, which has provided numerous advantages for healthcare providers, doctors, and patients. In this part, we will describe the benefits that healthcare predictive analytics opens for healthcare providers.

1. Improved Diagnostics

Some diseases have typical symptoms, and qualified doctors can easily define them and cure according to the predefined treatment plan. However, each patient can have individual or, so to say, atypical symptoms that might point at a specific disease, but make diagnostics more complicated. The presence of atypical symptoms makes specialists rely on individual patient data and family history to define whether a person needs hospitalization to cure a disease. Laboratory testing and diagnostic procedures like CT, X-rays, MRI, etc. combined with predictive analytics have more value for further diagnosis and treatment plan choice. 

2. High Cost-Effectiveness

One of the essential reasons why healthcare organizations choose to implement predictive analytics is the ability to reduce costs significantly. With a great amount of available data on patients, staff, equipment, supplies, administrative tasks, and scheduling, you can generate detailed information on managing costs and patient risks. Based on this information, it becomes clear what expenses you can cut without sacrificing anything important.

Supply management is one of the complex processes requiring considerable expenses. Predictions about patient conditions, staffing, devices, contractors, etc. can reduce the number of suppliers for the same items. They can also help you save money by urging doctors to use more economical equipment and avoid miscarriages and poor management within the supply chain.

3. Enhanced Operational Efficiency

When overloaded, hospitals face medical staff shortage that often severely affect the quality of patient care. By implementing predictive models, your organization can allocate administrative resources and management to know about staffing challenges in advance. In this case, a software specialist can build a model following the analysis of such predictors as the number of medical personnel available, seasonal changes affecting population health, holidays, outbreaks, etc. 

Medical organizations can also manage skipped appointments and adjust patient flow in the most convenient way for doctors and patients. Specialists can include data on patients who frequently fail to show up and provide notice about skipping appointments in advance to predict gaps in doctors’ schedules and adjust it more efficiently, thus eliminating time waste.

4. Decreased Re-admission Rates

The re-admission rate is a factor indicating the quality of care provided at a particular hospital. According to the applicable regulations in many European countries and throughout the US, hospitals must pay the penalty for patient readmissions in case of disease relapse. When patients are re-admitted to the hospital 30 days after their initial admission with the same complaints, it indicates that the treatment wasn’t efficient enough the previous time. Predictive models help your organization prevent this by calculating the readmission probability during the first patient’s assessment, based on current and historical healthcare data. 

5. Personalized Medical Care

Healthcare institutions tend to increase their efficiency by using the opportunities of precision medicine. Predictive modeling helps to improve patient-centered care based on personal health records and contributes to the creation of the most effective treatment plans tailored for each patient. Prognostic models are exceptionally efficient for inpatient and emergency treatment when fast decisions have to be made. Any available data at the organization allows predicting the effectiveness of procedures, manipulations, laboratory tests, and medications depending on the specifics of a person’s anatomy and genes.

Final Thoughts

Predictive analytics has the most significant positive impact on businesses that operate massive amounts of data, face numerous risks, and quickly adapt to market changes. The healthcare industry is one of them. Medical companies often work under pressure, considering a great deal of responsibility to improve the health of millions of people. Advanced technologies are the key to making their work much more efficient and bearable. Data scientists at our company use big data, artificial intelligence, data analytics, and machine learning to build advanced predictive solutions for businesses. Don’t hesitate to contact us, if you want to use all the advantages of predictive analytics in your project.

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