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A Validated Method to Identify Neuro-Ophthalmologists in a Large Administrative Claims Database
Yilin Feng1, Chun Chieh Lin, Ali G Hamedani
1Department of Ophthalmology (YF), Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts; Department of Neurology (CCL, LBDL), University of Michigan Medical School, Ann Arbor, Michigan; Departments of Neurology and Ophthalmology (AGH), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; and Department of Ophthalmology and Visual Sciences (LBDL), Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan.
A new method using Medicare data can identify neuro-ophthalmologists by analyzing billing codes. This facilitates research into neuro-ophthalmic care quality and patient healthcare utilization.
Area of Science:
- Ophthalmology
- Neurology
- Health Services Research
Background:
- Validated methods to identify neuro-ophthalmologists in administrative data are lacking.
- This gap hinders research on neuro-ophthalmic care quality and utilization.
- Developing such a method is crucial for understanding patient care in the U.S.
Purpose of the Study:
- To develop and validate a method for identifying neuro-ophthalmologists using administrative healthcare data.
- To enable future research on the delivery and quality of neuro-ophthalmic care.
Main Methods:
- Utilized a 20% sample of Medicare carrier files from 2018.
- Identified neurologists and ophthalmologists based on office-based evaluation and management (E/M) outpatient visit claims.
- Linked National Provider Identifier numbers from the North American Neuro-Ophthalmology Society (NANOS) directory to Medicare data.
- Calculated the proportion of E/M visits with neuro-ophthalmology-specific codes (NSC) for each physician.
- Employed logistic regression to predict neuro-ophthalmology specialty designation.
Main Results:
- Identified 32,293 neurologists and ophthalmologists with outpatient E/M claims in 2018.
- Of 472 NANOS members, 399 (84.5%) had Medicare outpatient E/M visits.
- The proportion of E/M visits with NSC best predicted neuro-ophthalmology designation (AUROC = 0.91).
- Maximized predictiveness at 6% NSC, yielding sensitivity of 84.0% and specificity of 93.9%, but low PPV (14.9%).
- Limiting to physicians with cross-specialty claims increased PPV to 33.3%.
Conclusions:
- A validated method to identify neuro-ophthalmologists from administrative data has been developed.
- This method can be adapted for use in other databases.
- Facilitates future research on neuro-ophthalmic care delivery and quality in the United States.
