How to Fix Data Overload in Healthcare With Predictive Analytics

By Michael Rivera, PhD

Healthcare professionals deal with large amounts of data daily. They sift through big data like medical history, the rate of patients suffering from an illness, health survey data, and treatment trends. And most times, this data is too complex for conventional data processing techniques.

So, it’s no wonder hospitals are looking for simplicity. They need customized solutions that reduce the amount of data they must consider before making decisions.

At Thinaer, we’ve helped healthcare organizations solve this challenge using Artificial Intelligence. AI, such as predictive analytics, can help health workers overcome burdensome data by understanding past trends and providing tailored recommendations based on each patient.

Here’s how predictive analytics can help you deal with data overload:

Reduce Hospital Readmission Rates

The CMS penalized 2,499 hospitals for high readmissions within 30 days in FY2022.

But healthcare organizations can avoid this by fixing data overload. Patients readmitted in 2018 referred to one of these four conditions – heart failure, diabetes, COPD, and septicemia. This data is recorded but drowns in a sea of big data.

Predictive analytics can discover patients with a high risk of readmission. This will help warn doctors to provide better preventive care. For example, the University of Texas Medical Branch reduced 30-day-all readmissions by 14.5%. The hospital avoided $1.9 million in readmission-related expenses through a data-driven approach that fixed data overload.

Establish Priorities in Patient Health

Predictive analytics can use data to detect early signs of a patient’s deteriorating condition, especially those in ICU. The pandemic has made it more crucial for hospitals to be more proactive. Since each patient’s vital signs are monitored, predictive analytics can use the data. Patients with a high risk of a deteriorating condition in the next 1 hour can be identified using deep predictive algorithms. This empowers the response team to be proactive and prevent the danger or minimize its effects.

Provide Risk Scores for Chronic Illnesses

Predictive analytics can also convert data overload in chronic illnesses to risk scores. 6 out of 10 US adults suffer from ongoing chronic diseases. And most of them are constantly at risk of complications.

Health professionals need to continually analyze the patient’s data to determine the possibility of a complication happening. Predictive AI can assign a specific risk score to a person based on lab results, data generated from their lifestyle, and biometric data.

This score indicates the likelihood of a complication occurring soon. It can also spot early indicators of deterioration and report them to a doctor.

Manage Hospital Supply Chain

Predictive analysis can help the hospital in making data-driven purchase decisions. Deep learning algorithms use data to understand patient specifics, making buying equipment efficient and cost-effective.

Identify Genetic Irregularities

Analyzing genetic data is usually complicated for health workers because the human genome is complex. Predictive analytics can compare and analyze a patient’s genetic information using a database of probable abnormalities and disorders. It can even be used as early as the neonatal stage to alert parents to the condition their infant may be suffering from.

Final Thoughts

Predictive analytics isn’t a new phenomenon, and it’s already being used. It helps health organizations manage big data and use it effectively to improve their patients’ lives and health. At Thinaer, we’ve helped our clients build custom predictive systems. These deep learning AI systems are equipped with 360-degree visibility that helps your organization forecast clinical needs, become proactive, and save thousands in healthcare costs. Contact us today to fix data overload and optimize your healthcare performance.