Assessing Diversity, Equity, and Inclusion in Patient-Facing Websites in Neurosurgical Departments in the United States

BACKGROUND

Patient-facing websites serve as essential platforms for disseminating information, engaging with patients, and increasing access to neurosurgical resources and services. Diversity, Equity, and Inclusion are at the forefront of issues facing the field of neurosurgery, especially concerning race and gender disparities in regards to providers in the field.

METHODS

Data were collected in regards to the race and gender of patients and providers displayed on the neurosurgery department's patient-facing website in addition to accommodations for disabilities, decreased ability to pay, and language.

RESULTS

Patients who were White were depicted more commonly than those of color (69% vs. 31%, P < 0.00001). White patients also were over-represented when compared with the average demographics of the communities in which the hospitals served (P = 0.03846). Neurosurgical providers who were White outnumbered those of color (70% vs. 30%, P < 0.00001). The racial depiction of providers was comparable with racial disparities currently observed in neurosurgery (P = 0.59612). Female neurosurgery providers were seen less than male providers on patient-facing websites (P < 0.00001) but were seen more commonly on patient-facing websites than the percentage of practicing neurosurgeons they currently comprise (28% vs. 8%, P < 0.00001).

CONCLUSIONS

The results of this study suggest that patient-facing websites of neurosurgical departments are an area of improvement in regards to Diversity, Equity, and Inclusion in the field of neurosurgery. Disparities are noted in regards to the racial depiction of patients and further call to attention racial and gender disparities in the field of neurosurgery.

Can Publicly Available Artificial Intelligence Successfully Identify Current Procedural Terminology Codes for Common Procedures in Neurosurgery?

OBJECTIVE

Coding for neurosurgical procedures is a complex process that is dynamically changing year to year, through the annual introduction and removal of codes and modifiers. The authors hoped to elucidate if publicly available artificial intelligence (AI) could offer solutions for neurosurgeons with regard to coding.

METHODS

Multiple publicly available AI platforms were asked to provide Current Procedural Terminology (CPT) codes and Revenue Value Units (RVU) values for common neurosurgical procedures of the brain and spine with a given indication for the procedure. The responses of platforms were recorded and compared to the currently valid CPT codes used for the procedure and the amount of RVUs that would be gained.

RESULTS

Six platforms and Google were asked for the appropriate CPT codes for 10 endovascular, spinal, and cranial procedures each. The highest performing platforms were as follows: Perplexity.AI identified 70% of endovascular, BingAI identified 55% of spinal, and ChatGPT 4.0 with Bing identified 75% of cranial CPT codes. With regard to RVUs, the top performer gained 78% of endovascular, 42% of spinal, and 70% of cranial possible RVUs. With regard to accuracy, AI platforms on average outperformed Google (45% vs. 25%, P = 0.04236).

CONCLUSIONS

The ability of publicly available AIs to successfully code for neurosurgical procedures holds great promise in the future. Future development of AI should focus on improving accuracy with regard to CPT codes and providing supporting documentation for its decisions. Improvement on the existing capabilities of AI platforms can allow for increased operational efficiency and cost savings for practices.