Big Data and the Great Challenges of Health and Medicine
Timothy Landers, RN, CNP, PhD, is an assistant professor at The Ohio State University and a Robert Wood Johnson Foundation (RWJF) Nurse Faculty Scholar.
The Great Challenges Program is an ongoing effort by the TEDMED community to provide innovative, interdisciplinary perspectives on the most complex and challenging issues in health care. A year-long dialogue facilitated through social media tools and panels of experts continued at the annual gathering of TEDMED 2013.
One of the themes of TEDMED 2013 was the creative and thoughtful use of big data and small data to improve health and health care.
Small data includes individual level information specific to an individual or circumstance. In small data, “n=ME.” A vast amount of individual level information is now routinely collected. However, a large volume of data is not required for small data to be useful—in the words of one TEDMED speaker, it’s not the volume of the data, but the complexity of existing data. Data must be available and accessible in order to be useful as well.
Big data refers to patterns of data and information available at the population level. The goal of big data is to use information and take a “macroscopic” view of health. It includes the ability to recognize patterns that are not obvious or readily apparent. Big data analysis permits us to go from pieces of data to collective wisdom, a theme of TEDMED 2013.
Using technology and innovative approaches from TED MED 2013, this blog describes some potential applications of a data‐driven approach to several of the Great Challenges of Health Care.
Small Data and Personalized Medicine
One example of the vast amount of data that can be available was demonstrated by TED MED Innovator Emotiv’s real‐time EEG monitoring system. Available for under $300, this system provides feedback on brain functions including executive processing, visual processing, and sensory integration.
There are implications for the use of this type of data in health professional education, patient education, and in real‐time monitoring of patients. Imagine monitoring brain waves of nursing or medical students. Faculty could then be evaluated based on the degree to which they stimulate engagement and new ways of thinking among their students (along with traditional measures of student satisfaction and standardized test results, of course!). Warnings could be generated from the brain waves of patients who are likely to become aggressive or violent. Caregivers could be monitored for their brain wave responses. When developing patient or health care professional educational materials, brain waves could be monitored to detect the most effective ways of delivering targeted messages.
Data and Rapid Adoption of Evidence-Based Practice
Another great challenge is encouraging more rapid adoption of best practices. This includes decreasing the time to disseminate findings and encourage adoption of the most effective strategies at health care delivery.
One example of the use of data to facilitate adoption of best practices in infection prevention is the use of small devices to monitor hand hygiene compliance among health care workers. One of the TED MED innovators had developed a wristwatch-sized monitor that can detect proximity to hand sanitizer dispensers, and the technique used when these products are applied.
Small data is provided back to the user in the form of a dashboard on how frequently hand hygiene was performed by the worker. A small vibration indicates to the user that sanitizer was applied for the correct amount of time. This small data can be very useful in promoting behavior change.
At the level of big data, this technology could be employed to evaluate hand hygiene interventions. For example, which dispensers are accessed most frequently within a unit? What is the impact of different dispenser designs (color, design) on hand hygiene compliance? Using this technology, data could be collected as new devices are deployed on specific units within days to weeks.
So much data is available, but is not presently harnessed for the transformative insights that make a difference for individuals and our health care system. It is crucial that this data be harnessed for the benefit of those who own it.
There are tools being developed to move in to big data thinking. For example, HIPAA [the Health Insurance Portability and Accountability Act] makes patients the owners of their health care data—a crucial step in the process of data liberation. With the right to access it, health care data is an important tool in moving from data to wisdom.
Technology and Medical Communication
The great challenge of medical communication is to facilitate interaction between patients and their health care providers. Patients with complex health needs benefit from a team approach to treatment that includes nurses, physicians, therapists, and technicians. One innovative solution to the problem would be the use of an app that would track the network of health care professionals involved in the care of a patient.
The TEDMED app is a smart phone application designed for delegates to view the session schedule, follow social media feeds, and manage conference registration. An innovative feature of the app is that it allows tracking of an individualized network of contacts from the conference.
When deployed for patients, such a system could track the names, titles, and contact information for health care professionals along with visit dates, follow‐up appointments, and contact information. From the view of big data, such a system could be used to measure satisfaction and outcomes for different networks of providers. It could measure which nurse, physician, technician team has the best outcomes? How often are specialists consulted by primary treatment teams?
Data and Promoting Active Lifestyles
The Great Challenge addresses the pressing need to promote active lifestyles.
One tool to be able to do this is through small activity monitors. During TEDMED 2013, participants were given a fitbit and encouraged to track their activity levels and compete against other groups of delegates.
This technology is an example of using available data on steps taken, calories burned, and stairs climbed to motivate individual users to be more active. This use of small data can be very useful to people wishing to be more aware of their activity. At the level of groups, this big data could be used to evaluate workplace designs in relationship to the activity levels of workers.
Read more about the experience of RWJF Nurse Faculty Scholars who attended TEDMED 2013.