Another pearl from Intelligent Medical Objects with thoughts I would like to share.
In my work analyzing data across organizations, one of the things that I constantly stumble on is the lack of data quality both within and between various health systems. The short story seems to be that the process of gathering our healthcare data focuses mostly on getting data into our systems in any way possible, and rarely on ensuring that the data is usable when we try to get it back out.
I make this assertion without prejudice; most of us in healthcare professions do not see ourselves as data gatherers or data stewards. The generation of data is a secondary outcome of our rendering care to our patients and/or our supporting those who render care to our patients. The result is that our data encoding tends to be local (driven by encoding in our instance of an EMR system) and our data only gets corrected if an error inhibits our ability to render care (e.g., we only “fix” mis-assigned NDCs if that assignment prevents us from successfully doing our job).
This blog on data quality identifies five other data-intense industries including aerospace, the military, and aviation. It describes challenges each of these industries have had, how they addressed them, and then uses the learned experience from each of these industries to identify five strategies that may help healthcare improve their data quality. :
- Shifting our mindset from reactive to proactive in terms of how we collect data and considering wider uses of the information collected to drive higher quality and broader impact.
- Normalizing our clinical terminology to enhance the usability and value of the data.
- Employing data governance to ensure data quality.
- Developing a clear strategy for how data is collected that ensures completeness and specificity of data.
- Teaching ourselves in early training how to gather, evaluate and use data (data-based decision-making).
I encourage you to read this blog and see how its contents might apply to your individual organizations and how we, as a profession, might work to make data more useful.
One of the things that occurred to me as I read this blog was that, in all these instances, there was an “800 lb. gorilla” who could “sleep anywhere he wanted to” and could drive the necessary changes, including creating organizations, and staffing and funding them in ways to make these changes happen. In all these cases, that “gorilla” was the federal government although, in the aviation case, it was aided by general cooperation from the airline industry.
For healthcare in general, and medication management in particular, who would that “gorilla” be? If I think about how bar coding got started, it was because a couple of very large retailers decided to mandate it as a condition of doing business with them.
Is part of our problem that we struggle to staff and fund our basic workforce, much less staff and fund these kinds of more global efforts?
Is part of our problem that we are not effective in cooperating to make these kinds of changes occur?
Is part of our problem that our systems are not designed to help us ensure data quality?
What do you think stands in our way?
As always, the comments in this blog reflect my thoughts and not necessarily those of ASHP or of my employer.
Dennis A. Tribble, PharmD, FASHP
Ormond Beach, FL
datdoc@aol.com