This guest blog was written by Matt Oldham, Director of Data Architecture at Graphium Health.
I deal with data everyday. As a data architect, database administrator, and data integration engineer, data has been my life for over 15 years, and I can’t recall a time when I did not feel the weight of the responsibility for every bit of data that lay within my realm of control. I believe this is the essence of data stewardship - a sense of responsibility for ensuring and maintaining the highest levels of quality, security, and integrity for data with which we interact.
Make no mistake, we are all data generators. We turn on the lights in the morning when we wake, generating charges through our electric meters. We hail a ride in Uber or Lyft and make our contribution to someone’s transit dashboard. We receive text messages, send emails, stream music, make online purchases, order take-out for dinner, etc, etc. The list is endless.
Many of these activities generate data in a way that doesn't necessarily allow us to contribute in the role of data steward. But suddenly, once we enter the realm of our jobs, be it in healthcare or any other industry, we find we have the opportunity to not only generate, but also to steward that data.
I frequently deal with data generated by clinicians and other healthcare practitioners, and I can attest to the fact that data stewardship in healthcare is sorely lacking. I get it - everyone is under pressure to prep quickly, perform well, and get out of the way for the next patient in the queue. And, after all, the care of the patient is (and should be) the primary focus. There is a real impact, though, to the business side of the house when the quality, integrity and security of data is not also kept in focus.
How can this be accomplished? I believe there are four key motivators of data stewardship that, when recognized and internalized, can affect real change in this area.
#1: Financial Motivators
When data generators take ownership of their data and make an effort to maximize accuracy, mistakes are reduced and money is saved. Discovering bad data is usually the result of spending a significant amount of time investigating anomalies that crop up in financial reports, charge summaries, etc. Such discoveries are typically not immediate, either, which means wading through days, weeks, or months of data to identify the root cause of the anomaly. All of this takes time, and time is money. The financial bottom line here is that good data leads to good decisions, bad data leads to bad decisions, and bad decisions cost money.
#2: Operational Motivators
Any way you cut it, high-quality data ultimately leads to a more efficient operating environment. We already discussed the issue of re-work, which essentially boils down to having to do a job twice because it wasn’t done right the first time. Real and tangible downstream impacts are caused by poor data stewardship. Take for instance an organizational project to measure the efficiency of clinical workflows in an effort to identify areas needing improvement. Such efforts depend on the data collected during those workflows. If the measurements derived from those efforts are based on low-quality data, operational inefficiencies cannot be identified, much less addressed and resolved. High data quality means both accuracy *and* completeness. One without the other results in (you guessed it) bad data.
#3: Ethical Motivators
Consider the unethical user who is not motivated to enter accurate or complete data. Subsequent, downstream analysis of that data will regard it no differently than that of an ethically motivated (or well-intentioned) user who simply neglected to devote the necessary time or attention to their data generation. Intentions do not get recorded alongside the data, so the only measurement of the effort taken by a data generator is the accuracy and completeness of that data. Accuracy and completeness, therefore, imply integrity. By increasing our ownership stake in the data we create, our actions become more intentional, and we become better data stewards. As a result, we fulfill our commitments as responsible employees, practitioners, and caregivers.
#4: Personal Motivators
While it may sound a bit self-serving, I can attest to the existence of a genuine comfort and confidence that accompanies the proper care and feeding of your data. One very immediate motivation toward data stewardship is simply the satisfaction of a job well done. I also believe our level of data stewardship reflects directly on our reputations as practitioners in our respective industries. Considering the fact that data is the lifeblood of any organization, our willingness to steward that data is representative of how much we value the organization. I can assure you that when the data each of us generates is analyzed - and it will be analyzed - our efforts to steward that data, or not, will be evident. And, as our efforts to better steward data become noticed, it engenders trust and encourages a higher sense of stewardship in others.
We must remember that decisions are being made (or will eventually be made) based on data that each of us has a hand in creating. My opinion is that every bit of data matters, regardless of how statistically insignificant it seems in the grand scheme of things. We live in an era when data is being generated at an unprecedented rate, and never before have we been more aware of the intrinsic value in that data. I believe that as we move toward better data stewardship individually we can each contribute to improving the status quo of the business of healthcare.