What the heck does astrophysics have to do with provider data quality?
With an entire science department dedicated to solving complex data issues, science is at the very core of Veda’s existence. After all, our Chief Science & Technology Officer and Co-founder, Dr. Bob Lindner, began his career in astrophysics.
After taking a leap from the academic world and into political data analysis, Bob and co-founder Meghan Gaffney realized the potential of provider data automation. [READ Q&A WITH DR. BOB LINDNER]
The commitment to the scientific method and investment in science is what sets Veda apart from other data and healthcare tech companies—and what led to a robust science department with an impressive five IP and automation patents.
But you might be thinking: what exactly does this background in galaxy-staring, particle-measuring, and the expansive universe have to do with ensuring health plans’ provider directories are accurate?
The answer lies in wholeheartedly embracing the scientific method and Veda’s mission: We blend science and imagination to arrive at solutions for our customers. In fact, Bob argues one would not be able to tackle provider data problems accurately and reliably without a science department.
In the healthcare industry, data changes rapidly, some sources of data claim to be sources of truth but may in fact not be accurate, and data can be a heavily manual process. The only way to uncover the truth is with a careful and accurate measurement process.
Here is an expert from Dr. Lindner on problem-solving at Veda:
There are two kinds of main prediction problems. One where the answer to any given problem is self-evident. You can look at it and immediately know what the answer is. You give this problem a fast feedback loop and design your system to get the right answer based on immediate feedback from engineers. Outside of healthcare, an example is image classification. Is there a smiling person in this picture or not? You can look at an image and immediately tell.
A different kind of problem that we’re faced with every day at Veda is if the answer you’re trying to predict is not self-evident by a trained user in the field. For example, does this provider work at this address? It may look like a reasonable address and provider name but you don’t know if it’s accurate just by looking at it.
Dr. Robert Lindner
The only way to know if the system is working is to be very disciplined with the art of measurement and calibration. You must have a good set of test data that you trust that was collected in a way that was very tightly controlled. And you have to trust you are training your models on the data in a way that’s not overfitting because when your system gets used in production you don’t know—aside from that measurement in comparison to your training data set—if it’s working or not. You have to trust in science fully because if you do that part wrong, by using a biased training set or too narrow of a sample, there are errors that are invisible until you actually try to use the data. It can be a devastating effect. If you have a 10-digit number that says it’s the phone number of a provider, and you can’t call every phone number, how do you know it’s correct? You must have faith in the process. And the process to have faith in is the scientific method.
Provider data is complex and vast just like data in the field of astrophysics. However, provider data is nuanced and complicated in ways that even monitoring billions of stars is not.
The challenges with provider data are more complex than say, finding the largest thing in the universe, because the information included in directories changes often and the scope of required information keeps expanding. Practices move locations, physicians change practices, and contracts between practices and health plans expire. Multiple industry reports state between 20% and 30% of directory information changes annually.
Yet, no single party is the exclusive keeper of this information. Some of the information is governed and controlled by the practice, such as contact information and the roster of clinicians who practice there. Other data, such as whether a clinician is accepting new patients under a specific plan, can be owned by the practice, the health plan, or in some instances, shared by both parties.
Having different authoritative sources depending on the data contributes to the difficulty for health plans and practices in keeping information accurate.
So yes, provider data is more complicated to monitor than the stars but the Veda science department, using the scientific method day in and day out, can solve complex provider data problems faster and more accurately than anyone else. We start by understanding problems deeply before pairing them with an appropriate model and AI technology.
Before you select a healthcare data vendor, ask yourself, why don’t they have a science department backed by patented IP?
