If you grew up watching Creature Double Feature movies, you know that Godzilla is a giant dinosaur-like monster that destroys Japan (and most recently San Francisco), and battles other monstrous creatures like Mothra and Destoroyah. In the early movies, Godzilla was the villain, but in the later movies he became a giant, albeit destructive, anti-hero. By the same token, big data can be a hero and save the day, or it can be a big, scary monster.
In its most basic form, big data is digital health information that comes from a variety of sources, including electronic health records, clinical trials, insurance claims, mobile apps like Fitbit and social media, where people post information about their health issues.
The power of big data is indisputable, but is it a force for good or evil?
A recent PwC study reported that 95 percent of healthcare CEOs said they were exploring better ways to harness and manage big data. Why are they so committed to exploring new ways to do this?
Experts predict that big data could improve everything from the drug-discovery process to predictions about patients' disease risks. In fact, a McKinsey and Co. report estimated that big data could help reduce U.S. healthcare expenses by as much as $450 billion.
Here are some ways big data can transform healthcare:
- Population health: Big data could allow physicians to study larger populations and analyze the data to cost-effectively implement treatment changes quickly to improve people's lives.
- Preventive care: Carolinas HealthCare System, an integrated delivery network with 900 service locations in North Carolina and South Carolina, purchased consumer spending data to analyze purchases and anticipate patients' future healthcare needs. For example, if a patient buys a lot of alcohol or eats a lot of fast food, he or she could be at a risk for depression or diabetes.
- Reduce healthcare costs: The July issue of Health Affairs identified six ways that big data can help reduce healthcare costs, including improving treatment for high-cost patients; reducing readmissions; improving patient triage; treating patients with deteriorating health conditions; decreasing adverse events; and treating people with diseases that affect multiple systems.
- Organ transplant matching: Hospitals can also use big data to find matching organ donors. Economic professors developed an algorithm to find organs for previously incompatible pairs that takes into account blood type, antibody information of the candidate and the antigen information of the donor. A Carnegie Mellon professor created an advanced algorithm to create a kidney exchange network featuring donor chains. The result: people can get the organs they need to lead healthy lives.
However, even with these promising outcomes, big data is still a giant, destructive monster. The problem facing big data is that no one has answered two very important questions:
- What is the right way to collect this information?
- Who should be allowed access to this data?
Federal Trade Commissioner Julie Brill expressed concernsabout the way smartphone apps and mobile devices are collecting health information and sharing it with third parties. In addition, a recent FTC study reported that health app developers have collected consumer health data and shared it with third-parties, including marketers.
The fact that mobile companies share people's health data with third-parties, without notifying people, raises some major legal and ethical concerns. There aren't any accepted standards for how patients agree to have their information used and shared.
One terrifying scenario would be if this data is collected and shared with the wrong people. Imagine if the number of steps people walk a day, the number of hours they sleep per night, their blood pressure scores and whether they buy alcohol on a regular basis is shared with insurance companies. Independently, these facts might not mean anything, but together, they might indicate that people are at higher risk for diabetes or heart disease and insurance companies could raise their insurance rates.
There are also concerns about whether the data used in predictive analysis is clean. While the data from the patientâ€™s medical record may be accurate, the data from external sources may not be. Combining data from different sources could impact the accuracy of the conclusions and ultimately, lead to prescribing the wrong treatment.
Time will tell whether big data will save the day or destroy the world. But either way, the healthcare landscape will change dramatically. Let's just hope we won't have giant monsters battling in U.S. cities, smashing buildings, squashing cars and making a giant mess of things. Healthcare reform is one thing, but giant monsters that breathe radioactive steam is something completely different.
Jenn Riggle is a vice president at Weber Shandwick Worldwide based in Washington, District of Columbia and member of its healthcare practice.