In April of this year, I addressed the Carolina Health Research Initiative at the University of North Carolina Chapel Hill on the topic, “The Complex Relationship Among Data Privacy, Security and Access.” Afterwards, I had a terrific conversation with my new friend Michael Levy, Chief Executive Officer at Bluedoor Health and Entrepreneur in Residence at UNC Health Care, in which he observed that healthcare technology is connected but not aligned. What an absolutely brilliant way to succinctly summarize the sad state of the industry!
Technology is everywhere, but up until fairly recently, there has been little effort to try to align resources or data definitions to provide seamless communication and, therefore, streamlined care.
That observations reminds me of one of my pet peeves about critics of the healthcare technology industry. I can’t tell you have many times I’ve heard variations of this comment: I was in Madrid last month, and I was able to withdraw money from my bank in Atlanta. If I can get money half a world away, why can’t my medical record make it across the street, from the ER to my doctor’s office?
Forgive me, but that is a simplistic question that makes the mistake of assuming the key variable is physical distance between the access point and where the data resides. That is irrelevant in the connected world. The key factor is complexity of transaction. When I am withdrawing money in Madrid, the ATM is merely recording a subtraction from my account. And then behind the scenes, some mathematical function converts Euros to dollars. That’s it.
The reason my medical record can’t make it across the street is that, when the various medical codes were established and information systems were installed, interoperability was not a primary concern. Healthcare has multiple code-sets: ICD-10s, CPT diagnostic codes, CPT treatment codes, DRGs, SNOMEDs, lab code sets, multiple billing code sets, various internal home-grown code-sets, etc. Each was developed essentially in isolation with little concern for interoperabilty. This creates horrendous mapping issues.
Just one simple example of data mismatch: When I was Executive Vice President at Georgia Hospital Association (GHA), one of the areas I led was Data. GHA was the collection point for the state hospital discharge database. At one point, we discussed with the Georgia public health department possibly combining databases. We quickly abandoned the idea. Something as simple as ethnic demographic categorization torpedoed the effort. The GHA database had seven demographic categories, while the state had nine. Obviously, the “boundary points” were different for each data set. And although it might be possible (but unlikely) to collapse our nine categories down to match their seven, once the detail from the nine categories is lost, going in the other direction (from seven to nine) is impossible.
This is just one simple example of misalignment in healthcare data. This problem is replicated in hundreds of ways throughout the system, with both data and other technology applications.
So thanks, Michael, for so eloquently encapsulating the gaps that exist in healthcare technology: connected but not aligned.