Our everyday habits generate data that has commercial, social, and health-related value. The ethics of using that data will basically depend on the situation. Commercial interests make money from the data by mining it for insights or reselling it. This is clearly a commercial activity. In healthcare, on the other hand, data has significant potential and value for improving the evidence that healthcare practice is based on. Open data that is driven by the public, rather than corporate interests can achieve a lot of good. Still, there are a number of problems with this idea. First and foremost are patient privacy and confidentiality. Patient privacy and confidentiality create ethical and legal requirements. These manifest as difficult thresholds for access to data. Of course, it is not as though the data is even organized and ready for analysis. Other issues include the fragmentation of data held by individual healthcare providers and the potential for harm. On an individual level, our data has the potential to be used against us. Advancement of improvements in healthcare systems requires addressing these barriers.
Innovation requires access
The discussion of complex health information access and management usually begins with the electronic health record. In many developed countries, this has replaced the use of paper-based information tracking for patient care. There is little evidence, however, that the aggregated information is being accessed and used beyond the reporting and compliance requirements. Usually, there are requirements to collect data in support of reimbursement from insurers or regulators. Performance data tracking has become more common. Nursing entrepreneurship to support quality care and patient safety face many challenges. Most nurses would face significant barriers to conducting limited research studies based on their unit. Data capture can be limited to only those nurses with an informatics mandate, severely limiting the data pipeline even at the organizational level. This underscores the importance of working across specialities in healthcare in order to optimize what is possible.
The future of data in healthcare
Data analysis, coordination, machine learning and artificial intelligence will support the future of assessment, decision making, and estimating risks. Someday, screening algorithms will automatically analyze patient data and apply all known health information to care determination. As a patient sleeps, the screening algorithms will update based on age and health trends customized to each patient. When an acute health event happens, detailed information will be synthesized in the form of critical information. Getting to that point, however, requires a greater connection between the informatics and analysis team, and the clinical staff on the frontlines of service delivery and patient care. It is not the technology that drives this process, it is the people and their expertise, and the greatest risk is a failure to collaborate and create a synergy from diverse specializations to achieve this vision.
Dive in to the research
Abouelmehdi, K., Beni-Hessane, A., & Khaloufi, H. (2018). Big healthcare data: preserving security and privacy. Journal of Big Data, 5(1), 1. doi: 10.1186/s40537-017-0110-7 https://link.springer.com/content/pdf/10.1186/s40537-017-0110-7.pdf
Kostkova, P., Brewer, H., de Lusignan, S., Fottrell, E., Goldacre, B., Hart, G., … & Ross, E. (2016). Who owns the data? Open data for healthcare. Frontiers in public health, 4. doi: 10.3389/fpubh.2016.00007 https://www.frontiersin.org/articles/10.3389/fpubh.2016.00007/full