The Big Data opportunity is one of the most tangible in the healthcare industry. New data collection, data integration and data analysis methods have the potential to revolutionize the treatment and prevention of health problems globally.
In this article, I’ll focus on healthcare providers themselves and how they can best exploit Big Data to improve patient care and operations.
As with other industries, integrating disparate data points helps profits. Ezekiel Emanuel, former White House special advisor on health policy, recently observed that healthcare companies are starting to use data analytics to control costs – now crucial for providers that receive a fixed payment up front to serve designated Medicare patient populations as mandated by the Affordable Care Act.
Take Aledade, one of several new ventures that helps primary care physicians form Accountable Care Organizations. ACOs seek to tie provider reimbursements to cost and quality metrics. CEO Farzad Mostashari, MD, observed in an article last year that ACOs can use analytics to improve prevention and treatment services:
“ACOs must understand the actual “flight patterns” of their patients outside of their practice… for example, a discharge alert can enable a primary care practice to ensure that every discharged patient has a telephone follow-up within 48 hours and an office visit within 7 days.”
The result: a healthier patient and lower risk of a costly emergency room visit. More than 5 million US Medicare beneficiaries are now served by hundreds of ACOs that have helped keep spending below Medicare targets.
Here are four best practices that providers can adopt to use analytics to their advantage.
1. Invest in automation and a repeatable process
It’s no surprise that doctors who monitor individual health apps, and providers that cross-check insurance claims with clinical data and patient interview notes, will treat patients more effectively. This is most compelling for the fraction of the population that generates the highest costs with emergencies. Their risk profile might extend beyond physical characteristics and include social or psychological factors. Providers that document such factors, and tap outside Electronic Health Record partners such as Practice Fusion, can treat patients more intelligently and thereby reduce future risk.
The trick is investing in the necessary people and technology to build a formal, repeatable process across the organization. Insurance records might reside on one structured database, and primary care records on another, and hospital physician notes might be captured on an unstructured data platform. Managing all this data flow successfully often requires integration software, a central repository, analytics software and a formal process that automates as many steps as possible.
2. Educate the workforce
Analytics are only as good as the source data. Decision makers need to ensure nurses, physicians and other caregivers understand how to use big data tools from the get-go. Training programs should have business intelligence and analytic processes baked into them, giving employees the necessary skills to improve operations.
3. Keep everyone on the same page
More healthcare facilities are using cloud computing than ever before, introducing both opportunity and risk. Companies should consider using advanced data replication software that ensures individuals in various departments have access to the same information at the same time. This will prevent inaccuracies and inconsistencies that can have a significant negative impact on patient care, finances and other essential aspects of healthcare.
4. Plan for flexibility
Smartphone apps and other innovations create many individual data sources. Hospitals and other providers must plan ahead to incorporate new data opportunities as they arise. For example, correlating provider records with a patient’s Jawbone profile before a doctor visit would be beneficial. Providers must stay flexible to integrate ever-proliferating data sources.
Providers are starting to show confidence that analytics can improve patient outcomes and turn costs in the right direction. By planning ahead and developing a well-rounded policy, organizations can ensure big data projects go off without a hitch.