Science is We: Neurosurgical Research 2030 and Beyond
Amazon launched software that can extract data from patient records late in 2018. Clearly, mega-businesses recognize the potential for big data to transform healthcare in many ways. This trend, coupled with computer capacity, computer learning algorithms and scientific collaboration will drive neuroscience research for tomorrow and beyond.
Research 2030 and Beyond
Neurosurgical research is expected to evolve in a number of fundamental ways between now and 2030. Necessarily, changes to research will be driven by the rapidly expanding ability to acquire, store and analyze information from our patients, as well as the normal population. The National Institutes of Health has recognized the critical importance of these changes on the future of medical research and have launched the Big Data to Knowledge (BD2K) as well as the Precision Medicine Initiative with a goal of longitudinally following one million people to best understand cancer-related features (causative, prevention and new/optimal treatment).2 Specifically, data-related transformations, rooted in ubiquitous data acquisition, expanding storage capabilities and potent machine learning will utilize correlative science developed from big data analytics to come up with more focused, improved and hypothesis-driven research to critically shorten the time to foundational medical discoveries.
Research Data Acquisition
Patient-related data is and will be actively and passively acquired from numerous distributed sources, including electronic medical records (imaging, physician assessments, laboratory values, other clinical measures/tests), social media, mobile apps, wearable sensors (an estimated one trillion sensors will be connected to the internet by 20201), electronic home monitoring devices, genomics testing and monitoring of computing activity. Research-related data that can be derived from these sources spans biological, psychological, behavioral and genetic parameters. In an unprecedented fashion, patient-related data will be derived in structured and unstructured forms from around the world and will be acquired in real-time. The ability to procure accurate and comprehensive data will grow exponentially with decreasing acquisition costs, as well as monitoring and assessment capabilities. These changes will provide historic opportunities for patient data collection and research in 2030 and beyond. There is enormous potential impact on many areas of medicine, including:
- True risk assessment/prediction
- Valid outcome analysis
- Improved understanding of compliance and the impact on cost and outcomes
- Behavior modification
Research Data Storage
Currently, it is estimated that 30 percent of all the data storage in the world is healthcare-related.3 Technologic advances will drive data storage expansion to meet the increasing demand as a consequence of the growing number of data acquisition sources. Corresponding to these increased storage capabilities will be an ongoing exponential decrease in the cost of data storage, as evidenced by historical and ongoing patterns in the marketplace.
- In 2000, one gigabyte of data storage cost $4.17.
- By 2015 it decreased to $0.03 per gigabyte.
- By 2030, data storage cost is expected to be just $0.0003 per gigabyte.
These decreases in data storage costs will permit the economical and comprehensive storage of patient data for biomedical research. The low costs of data storage combined with the global acquisition of data (via worldwide connectivity) will provide opportunities for patient research around the globe.
Research Data Analytics
Data processing speeds in 2030 will permit the near instantaneous and ongoing assessment of research-related information. Data analytic speeds and costs will drop substantially to facilitate the rapid advancement of medical research in 2030 and beyond. To provide context to data analytic speed increases:
- It took nearly 10 years to sequence the human genome in 2001, but it currently can be completed in just 1 day.
- IBM’s Watson can analyze 40 million medical documents in 15 seconds.
Increasing analytic speeds and machine learning will permit critical associations to be derived from the whole spectrum of data sets on an ongoing basis. The costs associated with data analytics have and will continue to decrease in the future. For example:
- Sequencing the human genome in 2001 cost almost $100 million.
- Today it costs less than $1,000, with prices rapidly dropping.
The reduced processing costs will provide new and critical opportunities to expand big data research for patients internationally.
One of the critical features of big data-driven medical research is that it will provide growing opportunities for collaborative science and global patient participation. By 2020, individuals around the world will likely have nearly seven connected devices. Near universal connectivity, coupled with common patient data acquisition capabilities and improved computing power, will radically increase collaboration between researchers around the world and increase access to patient data from non-traditional settings (e.g., private practices, public health agencies, insurance companies, pharmacies and a variety of non-medical settings, including routine in-home monitoring). The sharing of data from non-traditional sources will be further enhanced by emerging methods for securing and encrypting of electronic data during acquisition, storage and transmission (e.g., block chain technologies). New paradigms for research protections and data sharing will need to be established in this new era.
Neurosurgery and Amazon are thinking alike by imagining 2030 and beyond. Expanding data acquisition (structured and unstructured) and storage with powerful machine learning (data analytics) will shift research toward correlative science. Hypothesis-driven research will be greatly enriched by these new opportunities and will remain essential to define the mechanisms that underlie the correlations to gain insights into pathobiology of disease and develop new therapeutics. The results will enhance the care of neurosurgical patients in many ways – that is good news.
1. Ahmad, I. (2018, May 10). 21 Technology Tipping Points We Will Reach By 2030 [Infographic]. Retrieved from https://www.socialmediatoday.com/news/21-technology-tipping-points-we-will-reach-by-2030-infographic/523221/
2. Collins, F. S., & Varmus, H. (2015). A New Initiative on Precision Medicine. New England Journal of Medicine, 372(9), 793-795.
3. Huesch, M. D., Mosher T. J. (2017) Using it or losing it? The case for data scientist inside health care. New England Journal of Medicine Catalyst. Retrieved from https://catalyst.nejm.org/case-data-scientists-inside-health-care/
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