3 ways big data is improving healthcare analytics
July 17, 2015 in Medical Technology
In an industry under pressure to contain costs and improve member outcomes, big data is proving to be a valuable asset. However, data alone can’t move the needle. It’s all about how data is analyzed to drive smarter decisions about intervention and treatment options.
Healthcare stands to benefit from several major developments in data management: data collection through electronic medical records; data sharing through health information exchanges; and improved data analysis thanks to enterprise data warehouses and new analytical tools.
Here, we will take a closer look at how big data is improving healthcare analytics, including the ability to track trends and patterns from multiple sources. Whether you embrace big data as the answer to better healthcare or have a healthy dose of skepticism, improved healthcare analytics continues to gain merit with health plans. Better data accessibility provides a big boost to healthcare analytics, which can glean the insight needed to deliver improved member outcomes, quality of care and better management decisions.
Finding and targeting the right people
The population a health plan serves consists of diverse groups of people who may be at any point along the health and wellness continuum. How does your plan identify who is at risk for coronary artery disease or diabetes or who could benefit from additional screenings, weight management or smoking cessation programs? Providing care to those who need it best begins by analyzing multiple sources – from claims data to member-provided information to health risk assessments.
For example, health risk assessment data can provide a snapshot of potential plan usage among new enrollees. Without the data, health plans would have to wait and see who requires care coordination. Additionally, health plans can leverage healthcare analytics to understand what motivates people and how to change behavior. Taking a closer look at screening rates among people in different demographic groups can help identify barriers to screening and to determine the best way to encourage specific groups to complete recommended screenings. When dealing with large populations, it becomes even more important to know who can potentially benefit from intervention as a way to improve health and lower costs.
Delivering the right intervention at the right time
Identifying people who are at risk is one area in which big data is improving analytics. Another is ensuring that the most effective intervention is identified for each person – and that it’s provided when needed. This is an area where technological advances combined with analytics are driving improvements. For example, large amounts of real-time information are now available from wireless monitoring devices that postoperative members and those with chronic diseases are wearing at home and in their daily lives.
The ability to deliver the right intervention at the right time will improve as people begin to understand their own risks, monitor their health and share pertinent information with their care providers. It also calls for a coordinated approach: across settings and providers, all caregivers should have the same information and work toward the same goal. With improved data accessibility and analysis of that data, systems can deliver faster identification of high-risk members, recommend more timely interventions and provide data-driven monitoring.
Adjusting programs and closing the loop
As more information on health and disease and the patterns of care is available, more useful insights will allow for programs to be more quickly adjusted. Studies of care management and wellness programs have been largely positive. For example, one recent study tested the hypothesis that exposure to a targeted care management program changed the likelihood of having appropriate medication dispensed; appropriate clinical tests performed; and increased medication adherence. Per the study, 7.3 percent fewer members would have had a prescription for asthma controller medication in the absence of the program. The same study looked at pneumococcal vaccination and statin medication metrics – finding that in year 4, without the care management intervention, 16.6 percent fewer members would have had a prescription for statin medication.
Improved healthcare analytics leads to improved programs and the ability to create new ones. The potential to improve outcomes and contain costs from the analyzing big data in healthcare are, well, big. It has been reported that preventive actions – such as early cholesterol screening for patients with associated histories, hypertension screening for adults or smoking cessation – could reduce the total cost of care by over $38 billion, through the prevention of downstream medical episodes, earlier identification of the most appropriate treatment and avoidance of interim chronic care.