Artificial Intelligence-Driven Call Center Operations in a High-Volume Neurology Practice: Impact on Access, Efficiency, Revenue Growth, and Cost Containment.

Background Healthcare systems face increasing administrative burdens, workforce shortages, rising labor costs, and growing patient expectations for rapid communication. Telephone call management remains one of the most labor-intensive nonclinical functions in outpatient medicine. Artificial intelligence (AI)-driven voice assistants have emerged as a potential solution to improve patient access while reducing operational costs. Beyond cost savings, AI communication platforms may increase revenue by improving the capture of new patient consultations and diagnostic referrals otherwise lost to communication delays. Objective To evaluate the operational, financial, and revenue impact of implementing an AI-powered telephone answering and patient communication system in a high-volume outpatient neurology practice, emphasizing patient access, referral capture, and new patient acquisition. Methods A retrospective, observational pre-post cohort study was conducted at Espinosa Neuroscience Institute, a practice of three neurologists and four advanced practice providers. After deploying an AI-powered call center platform, inbound communications - including appointment scheduling, demographic intake, insurance collection, FAQs, message routing, and patient triage - were managed through a Health Insurance Portability and Accountability Act (HIPAA)-compliant automated voice interface. The study evaluated operational metrics, staffing requirements, call volume, communication backlog, referral capture, and estimated labor costs by comparing a 90-day pre-implementation period (December 2025-February 2026) to an 86-day post-implementation period (March-May 2026). As a retrospective analysis of de-identified data for quality improvement, it was exempt from Institutional Review Board (IRB) approval. Results The practice receives 400-500 inbound calls daily. Before AI implementation, unresolved communication queues frequently exceeded 500 messages, with patients reporting prolonged wait times and delayed responses. Following deployment, all incoming calls could be answered simultaneously without queue limitations. During the first 86 days, the AI platform processed communications representing 5,484 unique patient encounters and managed an average of 63.8 new patient interactions per day. Communication backlogs were reduced by over 98%, with fewer than 10 active conversations open at any given time. Eliminating communication bottlenecks improved the capture of new patient consultations and referrals for neurological evaluations, EEG, EMG, and MRI. Requests previously lost to unanswered calls or excessive hold times were more consistently scheduled and completed, resulting in increased utilization of clinical and diagnostic services. Financial modeling estimated annual labor cost savings of approximately $216,500 while creating new revenue opportunities through improved referral conversion and patient acquisition. Conclusions Implementing an AI-powered call center significantly improved patient access, reduced communication backlogs, increased operational efficiency, and lowered staffing costs in a high-volume neurology practice. Beyond cost containment, the system enhanced new patient acquisition and referral capture, generating new lines of business through increased utilization of neurological and diagnostic services. AI-assisted communication represents a scalable strategy for improving financial sustainability and patient access in outpatient healthcare.