Preventive Care: How Big Data, Analytics, AI and More Drive Better Results

Nanda says Cone Health used patient analytics to decide how many physicians, cardiologists and nurse practitioners to hire as it expanded clinical capacity in Greensboro.

According to Nanda, big data and health analytics systems have matured and can now identify gaps in the workflow of the care team, especially for specific age groups.

“If we can close recommended care gaps for specific age groups and education materials to keep our patients healthy, it will go a long way to support preventive care,” he says.

Nanda points out that in addition to preventing a disease from occurring, preventive care also includes secondary prevention, in which healthcare providers aim to prevent the condition from worsening.

Here are five areas in which Big Data can optimize preventive care:

1. AI in Preventive Analytics and Reporting for Healthcare

AI allows preventive care practitioners to predict the behavior of patient populations, which allows care providers to act before disease occurs.

Integrating AI into analytics and reporting allows healthcare systems to combine retrospective analytics with real-time reporting. Harms says UnityPoint has found success using ML as a forward-looking decision support aid.

Meanwhile, natural language processing could pull information from prescribed documents to determine when patients in recommended age groups needed automated reminders to get mammograms, Nanda explains.

“Otherwise, someone from the provider’s office will be scouring through charts to find out who is due, or they will be filing their charts or tagging their charts in a way that they can identify who is liable for that service,” he says.

According to Nanda, AI can help care teams query a database to see what conditions a patient is at risk for based on ethnicity, gender or family history.

click on the banner below Read on to learn how a modern data analytics program can optimize care.