Jun 20 2023
Data Analytics

Preventive Care: How Big Data, Analytics, AI and More Improve Outcomes

Healthcare professionals are using artificial intelligence, Big Data and analytics to ensure care is preventive rather than reactive.

Big Data brings significant value to healthcare organizations, but they need help optimizing their data analytics capabilities.

The amount of data being collected by healthcare organizations is often too large for traditional database management tools to process. By using machine learning (ML), predictive modeling and advanced analytics applications, health systems can mine structured, semistructured and unstructured data.

“Advanced analytics, automation and artificial intelligence are all important tools and techniques that healthcare organizations should be deploying to improve clinical, operational and financial outcomes,” says Rhiannon Harms, chief data and analytics officer at UnityPoint Health.

Big Data and health analytics can play a helpful role in preventive care, which includes annual checkups; flu shots; routine X-rays, such as mammograms; and screenings, such as colonoscopies.

Data enables physicians to provide preventive care rather than reactive care. In the past, preventive care physicians would keep track of procedures that patients need based on when they show up, according to Dr. Jeetu Nanda, senior vice president and chief medical information officer at Cone Health, a not-for-profit health system in Greensboro, N.C. Now, with electronic medical records and Big Data, physicians can keep better track not only of individual patients but also of entire patient communities, he says.

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

Big Data and health analytics systems have matured and can now identify gaps in a care team’s workflow, particularly for specific age groups, according to Nanda.

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

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

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

1. AI in Predictive Analytics and Reporting for Healthcare

AI allows preventive care physicians to predict the behavior of patient populations, which allows care providers to act before an illness occurs.

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

Meanwhile, natural language processing can pull information from dictated documents to determine which patients in recommended age groups require automated reminders to get mammograms, Nanda explains.

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

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

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2. Big Data and Health Analytics to Improve Patient Engagement

By using Big Data to generate timely insights for providers, care teams can spend more time on preventive care rather than combing through “disparate data sets to determine the preventive care opportunities that exist,” Harms says.

In addition, data and analytics allow care teams to know when the right time is to reach out to patients.

“Analytics can personalize the engagement opportunities for each patient, so our care teams are able to effectively spend their time with each patient where they are in their wellness journey,” Harms says.

READ MORE: What are modern data platforms, and how can they boost healthcare agility?

3. Data Quality and Accuracy to Support Preventive Care

Data quality in preventive care requires establishing data standards and avoiding inaccuracies and inconsistencies, Nanda explains. Inaccuracies can lead to incorrect diagnoses.

As more data becomes available, it will be easier to meet data quality and accuracy standards, Harms suggests. Health systems such as UnityPoint Health use a data governance strategy to prioritize the most important data and metrics, according to Harms.

“This ensures that we have a strong partnership with the clinical and business owners who are producers and stewards of the data, and that our governance practices are sized appropriately to provide value,” Harms says.

In addition, a centralized data warehouse helps healthcare providers establish a “single source of truth” when an organization is using multiple data sources, says Nanda.

4. Healthcare Data Privacy and Security

Healthcare providers are responsible for maintaining patient privacy and keeping data secure. That requires an active and consistent approach, according to Harms.

UnityPoint uses multifactor authentication to protect its online patient portal, Harms says, and the health system’s email monitoring tools have blocked thousands of threats to the health system.

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5. Interoperability Among Care Providers

Data interoperability can prevent patients from receiving duplicate preventive care tests or conflicting information from other healthcare facilities, Nanda notes. State health information exchanges enable healthcare providers to share data bidirectionally, he says.

“Interoperability is critical, as a person who has received appropriate care in another facility should not have to repeat the test or perhaps even get conflicting information,” Nanda says.

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