Academic Radiologist Subspecialty Identification Using a Novel Claims-Based Classification System.

Area of Science:

• Health Services Research
• Radiology
• Medical Economics

Background:

• Accurate identification of radiologist subspecialties is crucial for performance metrics and value-based care.
• Current Medicare physician specialty identifiers are insufficient for precise subspecialty classification.

Purpose of the Study:

• To assess a novel claims-based classification system for identifying academic radiologist subspecialties.
• To evaluate the feasibility of using Medicare Part B data for subspecialty payer identification.

Main Methods:

• Mapped Medicare Part B services billed by radiologists (2012-2014) to subspecialties using the Neiman Imaging Types of Service (NITOS) system.
• Calculated subspecialty work relative value units (RVUs) as a percentage of total billed RVUs.
• Compared NITOS-based assignments with faculty website designations and existing Medicare classifications.

Main Results:

• The NITOS-based system achieved high concordance with designated subspecialties, ranging from 71.3% to 98.9%.
• A 50% work RVU threshold correctly classified 89.8% of radiologists.
• Existing Medicare codes identified only 46.7% of nuclear medicine and 39.4% of interventional radiologists.

Conclusions:

• Medicare claims data, utilizing the NITOS framework, can consistently identify academic radiologists by subspecialty.
• This claims-based method offers superior accuracy compared to current Medicare physician specialty identifiers.
• The system holds potential for facilitating appropriate performance metrics in value-based payment models.