We have been hearing about a big data revolution in healthcare for the better part of a decade. Unfortunately, that revolution has not yet materialized. There is little doubt that great strides have been made in recent years, but our healthcare system is still doing things largely the same way it has been doing for decades. So what’s the problem?
The healthcare industry is notoriously slow to change. So despite plenty of enthusiasm about employing big data to transform healthcare, the actual practice of implementing the necessary changes has been anything but enthusiastic. In order to truly revolutionize healthcare via big data, the four issues described below must be addressed.
1. Poor Data Quality
The principle of big data is about more than just collecting and storing data, according to Rock West Solutions. To be truly useful, collected data has to be applied in some tangible way. That’s not happening on a large scale right now due to poor data quality.
At issue here is not knowing what kinds of data to collect. And because scientists and engineers do not know specifically what they are looking for, they opt to collect every bit of data they can in hopes that someone will figure out how to extract valuable later. Companies like Rock West Solutions are working on signal processing technologies to do just that, but such technologies are not ready for prime time.
2. Incomplete Data Sets
Hand-in-hand with poor quality data are incomplete data sets. You might have a team working on a specific problem only to be stalled by incomplete data. Now they have to develop a way to close the data hole without generating so much excess data that they bury what they really need.
The truth of the matter is that small data sets don’t work well. Take predictive analytics, for example. If an engineering team is attempting to develop an algorithm capable of predicting future health events in the lives of patients, the accuracy of their algorithm will depend largely on the size of the data set. A small data set will not yield very accurate results.
3. Current Best Practices
The healthcare industry’s current best practices for collection, curation, and sharing of data are inadequate. They haven’t even kept up with our technological ability. As such, most of the data being collected is not being used. It is languishing in growing databases just waiting for someone to figure out what to do with it.
The industry needs new standards that are not only in line with current technology but flexible enough to adapt to future technological advances. Until best practices change, not much will change in big data applications.
4. Professional Attitudes
Finally, professional attitudes are inhibiting the effectiveness of big data in healthcare. Unfortunately, the healthcare industry is one in which power and prestige are very important. Turning over more of medicine to data and technology steals some of that power and prestige from medical professionals not enthusiastic about giving it up.
Likewise, the new outcome-based model of healthcare delivery focuses less on individual providers and more on healthcare teams with a primary focus of creating happy patients. There is resistance to outcome-based medicine among professionals who cannot see the value of treating patients like customers.
Big data does have the potential to truly revolutionize medicine. But no such revolution will occur until the four things described in this post are addressed. Every successful revolution in the history of the world has demanded systemic changes in the way things were being done. The purported healthcare revolution is no different.