Delays in new patient scheduling: A shift from paper to patient.

492 Background: Tennessee Oncology (TO) is a large community-based practice receiving 125 new patient referrals per day for hematologic and oncologic care throughout 35 clinics across the states of Tennessee and Georgia. Institutional guidelines dictate a three-business day window from time of referral to new patient encounter to mitigate the time-sensitive medical and psychological complexities patients with cancer face. There are patients and practice-specific factors considered to ensure that each patient is evaluated by the right physician in the right clinic at the right time. TO’s office located in Cleveland, TN demonstrated disproportionately higher new patient wait times and was the site for an initial data-collection and intervention pilot. Methods: Average new patient wait times from June 2024 through December 2024 were calculated for the Cleveland office using data from our referral management system and EHR. The new patient process for Cleveland was mapped, etiologies for delay were researched, and interventions were theorized. Three PDSA cycles were undertaken between January 2025 and June 2025 and wait time data were collected and analyzed. Results: From June 2024 to December 2024, new patient wait times at the Cleveland office averaged 10 days with a range of 5-17 days. The average wait time throughout TO during was 7 days. Reasons for delay were grouped across seven primary categories: Patient-driven (33%), clinic push-back (24%), backlog-driven (20%), scheduling constraints (10%), physician push-back (7%), inadequate records (3%), and insurance-mediated delays (3%). PDSA cycle 1 educated physicians and clinic-specific leadership on delays, brainstormed opportunities for improvement, and incorporated a more robust system of iterative data collection, dropping average wait times to 6 days. PDSA cycle 2 re-configured physician templates to provide protected new patient slots, dropping average wait times to 4.5 days. PDSA cycle 3 is currently ongoing, incorporating a new artificial intelligence (AI) tool that scans and amalgamates incoming fax data, allowing the scheduling teams to maximally address patient-driven delays. Conclusions: New patient wait times at TO’s Cleveland office were reduced by 55% with iterative PDSA cycles focused on stakeholder re-education, more comprehensive data collection, and procedural improvements. There is cautious optimism that the three-business day goal can be attained with fine-tuning of AI inflow management software. Further adjustment of the new patient process amidst AI-directed changes will lead to scaling of these interventions to the remainder of Tennessee Oncology clinic sites.