Big Data can Help Reduce Healthcare Costs and Improve Care
June 10, 2014
We would all like to see healthcare costs reduced. As an employer, I know how important benefits are to my employees. Unfortunately, the cost of providing healthcare insurance continues to rise. Following the passage of the so-called “Affordable Healthcare act,” some of my employees saw the cost of providing healthcare for their families nearly double. The healthcare industry is one sector where analysts believe Big Data analytics can make a difference. Alison Diana reports, “Healthcare organizations hope big data and analytics projects can help reduce costs and improve care.” [“Healthcare Dives Into Big Data,” Information Week, 14 May 2014] “With the mandated adoption of electronic health records (EHRs),” writes Diana, “many healthcare professionals for the first time got centralized access to patient records. Now they’re figuring out how to use all this information.” She continues:
“Although the healthcare industry has been slow to delve into big data, that might be about to change. At stake: not only money saved from more efficient use of information, but also new research and treatments — and that’s just the beginning. For instance, data from wireless, wearable devices such as FitBits is expected to eventually flood providers and insurers; by 2019, spending on wearables-data collection will reach $52 million, according to ABI Research. Another source of health data waiting to be analyzed: social media. Monitoring what people post can help fight insurance fraud and improve customer service. These are just two ways big data can be used to improve care while cutting costs, experts say.”
Diana is not alone in believing that the wearables industry is about to explode. For more on that topic, read my post entitled “Wearable Devices and the Quantified Self.” Andrea Peterson reports, “Fitness tracking apps and devices have gone from an early adopter novelty to a staple of many users’ exercise routines during the past few years,” she writes, “helping users set goals and measure progress over time. Some employers even offer incentives, including insurance discounts, when workers sign up.” But, she asserts, many users aren’t aware of privacy issues associated with such devices. [“Privacy advocates warn of ‘nightmare’ scenario as tech giants consider fitness tracking,” Washington Post, 19 May 2014] She explains:
The traditional healthcare field is certainly aware of privacy issues, which is one of the reasons that Big Data analytics has been slow getting established despite the potential upsides involved. As Diana noted above, the mandated use of EHRs provides better access to data for medical professionals than they have had in the past. The hope is that this access will improve care and lower costs. Amy Dockser Marcus reports, “Researchers are analyzing pools of patient information collected from routine checkups to help doctors better diagnose their patients. This type of data is easier to mine thanks to the rise in electronic health records that contain information collected in regular doctor visits.” [“Big Data Treasure Trove From Routine Medical Checkups,” Wall Street Journal, 12 May 2014] Marcus provides a few examples of how Big Data can be used by doctors and patients:
“In one instance, a group of researchers looked at data from patients with sore throats and came up with a way to help determine whether people should see a doctor for a strep throat test or stay home and take aspirin. In another, a pediatrician was able to follow a hunch to study a connection between an eye disease and allergies in young patients with arthritis.”
The latter example nicely demonstrates one of the huge benefits of Big Data analytics — discovering non-obvious connections. Two doctors, Cole Zanetti and Jay Bhatt, are big believers in the transformational power of Big Data analytics; especially, for achieving the goals of the Institute for Healthcare Improvement’s Triple Aim Initiative, which are:
- Improving the patient experience of care (including quality and satisfaction);
- Improving the health of populations; and
- Reducing the per capita cost of health care.
Zanetti and Bhatt write, “Over the last few years, the triple aim has taken center stage in health care. Through more effective identification of individuals at higher risk, health care systems can become more strategic about resource allocation in order to achieve the triple aim. A tool called predictive analytics has created opportunities for customized prediction and relative risk scores to achieve this very goal.” [“Big Data With a Personal Touch: The Convergence of Predictive Analytics and Positive Deviance,” Huffington Post The Blog, 28 April 2014] They continue:
“Most initiatives that utilize predictive analytics, thus far, have only concentrated on one side of the bell curve, fixating on over-utilizers of health care services. Although this is an important field of study that further helps identify risk factors and obstacles to care, it may not be a complete picture of opportunities for resourceful quality improvement within a population. Other great opportunities lie on the other side of the bell curve through the ‘Positive Deviance’ approach to quality improvement. Positive deviants are the high-risk individuals whose uncommon behaviors and methods enable them to find better solutions to problems than their high health care-utilizing peers, while having access to the same resources and facing similar or worse challenges. Positive deviance enables the community to discover these successful behaviors and methods and develop a plan of action to promote their adoption by the high risk population and health care system at large. Improvement may be better achieved by looking at both sides of the curve.”
Clearly, by looking “at both sides of the curve,” medical professionals are better able to address treatments and costs. Dr. Anil Jain, senior VP and chief medical officer at Explorys, told Diana, “We, as a society, need to start creating our own metrics for how healthcare quality is defined. In the sense of looking at costs, we know where there’s avoidable cost in healthcare. We just need to get folks the data they need to avoid those pitfalls.” Two of the most obvious ways of helping reduce healthcare cost are reducing fraud and identifying abusers. Karen Weise reports how the state of Washington was able to reduce emergency room visits by identifying Medicaid abusers so that doctors could “direct many of these patients to clinics or other less expensive care centers.” [“How Big Data Helped Cut Emergency Room Visits by 10 Percent,” Bloomberg BusinessWeek, 25 March 2014] According to Weise, the state went to this system after other attempts to reduce abuse were resisted by doctors and hospitals because they would have been stuck with the bill. Big Data came to the rescue.
“The American College of Emergency Physicians called the Washington effort a ‘model for the nation.’ Oregon is setting up a similar program, and doctors involved in Washington’s program say they’ve answered queries from colleagues in California, Texas, Ohio, New York, and Florida. As the Affordable Care Act expands Medicaid to a greater number of patients, the need for states to keep ER costs in check will become more pressing. Washington is showing how far a little data can go.”
Healthcare professionals are still in the infancy stage of what can be achieved with the help of Big Data analytics. At the very least, the goals of the triple aim are more likely to be achieved with Big Data analytics than without them.