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 firstname.lastname@example.org.
The Mendel team is still buzzing from our week-long retreat in Cairo. The theme of the retreat was “coming together” and it was the first time the American and other remote employees were united with their Egyptian counterparts. Although there were many adventures–missing flights, seeing the pyramids, haggling at Khan el-Khalili–the highlight of the trip was collaborating together, as one global organization.
Competence via comprehension
Artificial intelligence (AI) is playing an increasingly important role in the healthcare industry. But to fully leverage the potential of AI, it must be equipped with clinical reasoning skills - the ability to truly comprehend clinical data, or in other words, to read it as a doctor would. When it comes to data processing tools, only a tool capable of clinical reasoning can effectively process unstructured clinical data.
Sailu Challapalli, our Chief Product Officer, spoke at a recent Harvard Business School Healthcare panel. The event brought together different healthcare and AI experts to discuss large language models and their impact.
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.
Within the real world evidence space, the generally accepted process for creating a regulatory grade data set is to have two human abstractors work with the same set of documents and bring in a third reviewer to adjudicate the differences. These datasets also serve a second purpose - as a reference standard against which the performance of human abstractors can be measured. Although this remains the industry standard, it is expensive, time consuming and difficult to scale.
From the Desk of the AI Team
AI projects have created tangible results for a wide range of industries. Despite the innovation, it is important to remember that AI is not a magic wand that will solve every problem in every industry with a single wave.
Before embarking on any new endeavor or enterprise, certain questions come to mind: How are we going to handle this? Does our team have the expertise, bandwidth, resources, and time to handle this undertaking on our own? When it comes to finding a scalable way to structure your unstructured healthcare data, the answers to these questions will impact when/whether you deliver a top-tier product for your clients.
Human abstraction has long been considered the gold standard for extracting high quality information from EHR data. With the rise of NLP and machine learning, how should we evaluate these new technologies and are human abstractors still the correct comparison?
PODCAST — 40 minutes
Leslie Lamport is known for his fundamental contributions to the theory and practice of distributed and concurrent systems, notably the invention of concepts such as causality and logical clocks, safety and liveness, replicated state machines, and sequential consistency. Full Youtube video: https://youtu.be/rNQFPz2KSzQ
PODCAST — 60 minutes
Eze Abosi is VP of New Products at Optum Life Sciences. Eze and Karim Galil, M.D. covered topics such as career background and the healthcare technical ecosystem. They also talked about creative solutions that entrepreneurs and companies are creating with access to data. The conversation also touched on unstructured data, the webinar with Guardant Health, clinical genomics, and NLP. Watch the full Youtube video here: https://youtu.be/95Kv64SyE0M
PODCAST — 45 minutes
Daniel Ciocîrlan is a Software Engineer, founder, and instructor at Rock the JVM. Watch the full Youtube video here: https://youtu.be/PUMCzgK02p8