RheumCode: Creating a Common Rheumatology Language in EHRs

Electronic health record (EHR) systems have changed the practice of medicine in myriad and profound ways over the past 10–15 years, and it is clear they are here to stay. Although EHRs excel at data aggregation, they often lack clarity in clinical insights, instead primarily focusing on administrative aspects. An important new initiative from the ACR, RheumCode, offers a solution to this problem.

“In broad strokes, RheumCode wants to rewrite our knowledge of rheumatology in a language that EHR systems can understand,” explains Thomas Grader-Beck, MD, co-chair of RheumCode and associate professor of Clinical Medicine, Rheumatology Division, Johns Hopkins School of Medicine, Baltimore. “EHR systems were primarily an initiative to simplify administrative billing purposes, so we know all about the financial aspects of patients, but we don’t know the clinical aspects equally well.”

“Further, even though EHR systems have made huge strides in aggregating data, these data are often poorly organized or even incomplete,” adds Meera Subash, MD, co-chair of RheumCode and assistant professor, Department of Clinical and Health Informatics, University of Texas, Houston. “We as healthcare providers have to assemble the pieces rapidly to provide a care plan for the patient who is in front of us. RheumCode aims to standardize key data elements or pieces of a patient’s healthcare history that are essential for continuing high-quality rheumatology care.”

Translating Data

When working with EHRs, clinicians and researchers must deal with different types of data. “Structured data” includes clearly defined elements, such as joint counts, in which specific numeric data are recorded with a clear type and definition. “Unstructured data” comprises free-text notes, imaging results and pathology reports that lack standardized formatting. One of the main goals of RheumCode is to define structured data elements, while recognizing that unstructured data can enhance these fields, potentially with the aid of artificial intelligence (AI).

However, the lack of consistency in data structures has made it difficult to compare patient data across systems. RheumCode aims to create a common language to define key data elements for rheumatology. A standardized way to represent rheumatology data across different EHR systems will facilitate improved patient outcomes and quality of care.

One expectation of RheumCode is that it will help establish parameters for treatment goals, such as “treat to target,” by defining the necessary data fields and structures for effective data collection and use within EHRs. In the long term, RheumCode may enable EHR systems to implement standardized pathways for tracking patient treatment effectively, thereby enhancing data accessibility and usability and supporting better clinical decision making.