Manually abstracting patient data at scale is an herculean task for humans alone. It is slow, expensive, difficult, and requires extreme precision and accuracy. Organizations have to choose between breadth and depth when it comes to making data useful for decision making.
Because of these challenges, the Mendel team created Carbon. Carbon is an easy to use workspace that allows clinical abstraction teams to efficiently curate high quality clinical datasets at scale. The foundation of Carbon is Mendel’s AI. Carbon pulls directly from Mendel’s AI platform to give abstractors a headstart in identifying relevant data elements within a patient’s chart.
Carbon began as an internal, proprietary tool for Mendel’s clinical abstraction team and AI teams. We didn’t find anything in the market that matched our needs, so we decided to build our own. Carbon makes it easier for these teams to work together to train and develop our models.
Today we are spotlighting one of our internal clinical abstractors, Shanna Wells.
Tell me a little bit about your role. What is your title, and your daily responsibilities?
I'm a clinical abstractor with Mendel. My daily responsibilities are varied as our tasks are varied. We run the gamut from actual abstraction of clinical data to verification of the AI's output to training part time team members.
What would you say is the biggest challenge facing clinical abstraction teams today?
The sheer amount of raw data that is available is daunting. There are so many uses for this data that abstraction is really becoming a clinical subspecialty.
Walk me through a project from end-to-end.
Where do you start?
When we first get a project, I typically review my patients to get an idea of what is ahead, then I dive right in. Once I get into a chart, I am going to review all of the documentation to determine if there is duplication of documents or anything within that might cause the chart to be rejected. Once I have determined that the chart is good to go, I begin extracting data and analyzing the information contained for the datapoints which are specified within the individual data model.
These data models are agreed upon "dictionaries" so to speak between our client and the abstraction team to keep our data consistent and only abstract the data points that our client is interested in receiving. After completing the chart, I move on to the next one until the project is complete.
What does it mean to be “finished” with a project?
Depending on the purpose of the project (i.e., creating a gold set to compare with the AI data, or to deliver to our client, then there may be adjudications, or comparisons of abstraction between human/human or human/AI, and then a final decision on the extracted data points to arrive at what we would consider the "absolute truth," thus creating a gold set!
What does success look like for clinical abstraction teams?
Success means meeting our deadline with impeccable accuracy, completeness and truth in data.
How does the Carbon Abstraction Workspace help you better accomplish your goals?
Carbon creates an environment that makes abstraction easily translate unstructured raw data to usable structured and searchable data. It creates a work environment that is intuitive, attractive, and efficient.
Share a tip for customers using Carbon!
Don't be afraid of customization! Having Carbon built to function specifically for your needs is possible!
Thank you, Shanna!
Want to learn more about Mendel’s Abstraction Workspace and how it can help your clinical abstractors? Contact us at hello@mendel.ai.
