Almost every interaction we have nowadays is added to a growing trail of data. This is especially true when it comes to health and medicine. Thanks to the adoption of smartphones and digital health apps, wearable technology such as Fitbit, and electronic health records (EHRs), patients are generating more data every day. This data can range from simple things such as exercise and food eaten, to detailed notes and blood glucose readings that help clinicians, digital therapeutic applications, and patients themselves make smarter decisions.
Unfortunately, even with modern technology, patients rarely have control over who can access their valuable health information. Patients generate data that is invaluable for research, but find it difficult to contribute it to research efforts they have vested interests in. A patient today with, say Alzheimer’s disease, would usually be unable to request, receive, and send their health data to leading scientists working on a treatment or cure.
Scientists fare no better than patients. A researcher has to traverse through archaic and convoluted processes to request data, which can take months and sometimes years. Even after successful retrieval, the data is messy and often unfit for their specific research purposes. Researchers must publish quickly to advance their careers, and are disincentivized to work on studies that require a lot of time and labor just to get useful data. The result is an inefficient system that discourages the world’s experts from pursuing many large-scale and high impact projects.
This is not sustainable as science and technology continue to evolve. If the health industry truly wants to deliver solutions based on artificial intelligence, machine learning and the plethora of exciting concepts that will become usable in the near future, we urgently need to redesign how information is being generated and utilized [1].
Patients are at the core of this necessary transformation. Patients must be able to securely integrate their various health data streams, control access, and earn economic benefits for sharing their data. Under such a system, a scientist studying Alzheimer’s could make a highly specific data request into a platform, telling the system that they want the past 5 years of genomic data from female, 40–60 year old patients who have no family history of neurological diseases. The scientist would attach a bounty that the patient receives upon securely sharing that specific data stream. The request goes to all the patients who fit that profile, and the patient gets to see who is going to be doing what with their data.
If the patient agrees, the system executes a completely digital contract that delivers payment to the patient and data to the researcher. Both parties can have full trust that the terms of their transaction will not be violated. After the study is done, the researcher no longer has access to the data and the patient receives a publication or report detailing study results. This new approach to research cuts down many of the barriers to entry that high-impact projects and studies face today, opening the floodgates to promising therapeutics and higher standards for research. This platform realigns a broken incentive system to benefit the stakeholders that produce real value in the healthcare economy, and minimizes the costs created by unnecessary intermediaries.
What’s really interesting here is the emergence of an economic cycle designed to improve data quality and patient engagement rates. In data science, specifically health data science, high quality data is exponentially more valuable than infrequently and irregularly updated data. A patient that inputs their weight, exercise, and diet data every other day is far more useful to a researcher than a patient that inputs their weight once a week and their exercise whenever they feel like it [2]. What that means is that patients who engage more frequently and continuously with their health will be rewarded a higher bounty for sharing their data, thus being encouraged to take a more active role in managing their health.
This is very exciting since digital health is expected to grow into a $200B industry by 2020 [3]. Health apps and digital therapeutics, just like other software products, suffer from engagement problems where patients stop using the product a couple weeks in. If we can incentivize patients to become more proactive about their health and keep up engagement levels, then we can provide critical support for promising digital therapeutics that may become the blockbuster drugs of the future.
Sources
[1] Kruse, C. S., Goswamy, R., Raval, Y., & Marawi, S. (2016). Challenges and Opportunities of Big Data in Health Care: A Systematic Review. JMIR Medical Informatics, 4(4), e38. http://doi.org/10.2196/medinform.5359
[2]Lee, C. H., & Yoon, H.-J. (2017). Medical big data: promise and challenges. Kidney Research and Clinical Practice, 36(1), 3–11. http://doi.org/10.23876/j.krcp.2017.36.1.3
[3] Roland Berger. (n.d.). Global digital health market from 2015 to 2020, by major segment (in billion U.S. dollars).https://goo.gl/szp5rn
