Revolutionizing Healthcare with Predictive Analysis

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Predictive analytics has been a game changer in healthcare, where the use of historical data to estimate the likelihood of a future outcome can save many lives.  Predictive analytics in the healthcare industry doesn’t stop with just real-time alerts, but also tackles the financial, administrative and data security challenges of provider and payer organizations as well.

Here are some ways in which healthcare organizations have deployed predictive analytics:

Risk Scoring For Chronic Diseases

In the world of population health management, risk scores have been central to improving quality and cost outcomes.

Developed from comprehensive patient data, risk scores give healthcare providers an insight into which individuals can benefit most from enhanced care. The provider can then chart out the best recovery plan for these patients in order to avoid long-term health problems.

Avoiding 30-day Hospital Readmissions

Patient readmissions subject hospitals and health systems to significant penalties – making preventive measures a key financial incentive for healthcare providers.

Analytics tools can help identify patients who exhibit traits that indicate a higher likelihood of readmission within the 30-day window. Providers can then focus resources on follow-up and improving discharge-planning protocols to prevent frequent returns to the hospital.

Prevention Of Suicide And Self-harm

Electronic health record (EHR) data, in combination with other tools such as standard depression questionnaires have helped assist the accurate identification of people at high risk of suicide or self-harm.

The strongest predictors include mental health or substance abuse diagnoses, previous suicide attempts, the use of psychiatric medications, and high scores on the depression questionnaire.

Early identification ensures that these individuals receive suitable remedial measures or treatments in order to avoid these life-threatening events.

Foreseeing Patient Deterioration

While admitted in the hospital, patients are susceptible to many potential threats to their well-being. This can include the development of sepsis, catching a hard-to-treat infection, or sudden system failures due to their existing conditions.

Data analytics can help providers react almost immediately to changes in a patient’s vitals. It also has the capability to identify an upcoming deterioration before symptoms become visible.

The Take-Away

Predictive Analytics and Big Data make it possible for industries like healthcare to shift their practice from responsive to preventive. As seen in this article, with the advancement of these technologies, possibilities are only growing!

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