DataFramed podcast recap: Using AI to improve data quality in healthcare
Nate Fox and Sunna Jo join the DataFramed podcast to discuss why AI is the key to improving data quality in healthcare
There are billions of data points across the healthcare ecosystem. From provider data to electronic health records and insurance claims, how can we make sense of the massive amount of messy data and use it to positively impact the patient experience? Ribbon co-founder and CTO Nate Fox and senior data scientist Sunna Jo recently joined host Richie Cotton on the DataFramed podcast to discuss why AI is the key to scaling data quality in healthcare. Both share their unique perspectives - as a former pediatrician, Sunna vividly understands the challenges that patients face on a daily basis. She realized the potential data has to drive high impact solutions to some of the problems in our healthcare system, and was inspired to pivot her career to data science. Nate shares how and why machine learning is a critical part of Ribbon Health’s provider data platform, and offers advice on how others can use AI to scale and turn data into action.
Take a look at a few of the key takeaways + our favorite quotes, and listen to the full episode to learn more.
- Data Engineering is very valuable when it comes to the scalability of data cleaning. It’s essential to think creatively about how to solve data quality challenges so that your solutions work reliably at scale.
- It's helpful to understand the context of the data, such as learning why the data was produced in the first place, who sits behind it, and what their intentions are. That context can change the entire process, starting with how you clean the data, analyze it, and how you consider anomalies and edge cases.
- Having a strong and clear operating definition for what is considered good quality data can help you more effectively work with messy data, transform it into usable data, and draw meaningful insights from it.
- “I leverage my clinical experience daily which is both amazing and motivating. Because of my clinical experience, I am able to provide an additional lens on the data from the perspective of a healthcare provider, and give my team the context for the data so they can interpret and translate the data in a way that makes sense. For example, for one of our provider performance products, we work really closely with medical codes. These are designated codes that define certain diagnoses and procedures. My team is cleaning and building a model on these same codes that I used to bill for my own visits as a provider. Being able to recognize and understand the insights that we can get from these codes have just been a great reminder of the value of my experience.” - Sunna Jo
- “Data engineering is a huge part of making this data usable. I think it requires a lot of creativity to think about "How can you scalably ingest thousands of schemas?". For example, address data can be formatted a number of different ways, we need to standardize that data across all the different scales that we see across different data sources. We built a tool that helps with onboarding new data sources by mapping all different fields to our own standard fields. Before, it would take us 20-30 minutes in Python to code up just one new data source, so imagine the mountain of work that’s created when you have hundreds of sources. Now, we have a simple UI that even starts to guess some initial mappings for you, reducing a 20-to-30-minute data mapping process per new data source to just 10-15 seconds, which makes a lot of our operations and our data adjustment processes a lot smoother and far more scalable.” - Nate Fox
Listen to the full DataFramed podcast episode, “Using AI to Improve Data Quality in Healthcare”
About Nate Fox
Nate Fox is Ribbon’s co-founder and Chief Technology Officer. He got his start in engineering at MIT before going to Microsoft and eventually Unified as a lead special projects engineer. When he’s not busy coding at Ribbon, you can find him cycling through upstate New York and filing for patents on educational toys.
About Sunna Jo
Sunna is a senior data scientist at Ribbon, where she works with a cross-functional team to build and improve products that power care navigation and serves as a technical lead for the Focus Areas and Price Transparency products. Prior to joining Ribbon, Sunna practiced as a pediatrician for several years and was involved in clinical informatics and clinical research. She completed her medical training at the Albert Einstein College of Medicine and the Children’s Hospital at Montefiore.