Abstract P6-13-04: Prospective comparison of cost, travel burden, and time to obtain multidisciplinary tumor board treatment plan through in-person visits vs. an AI enabled health technology
Authors: Badwe Rajendra , Anderson Benjamin , Feldman Nancy , Gupta Sudeep , Nag Shona , Pramesh CS 
Published: March 2020

Background: Navya is a validated online cancer informatics solution that combines artificial intelligence (AI) based analysis of the guidelines and evidence, and rapid review (2 mins/case) by organ specific tumor board experts at Academic Medical Centers to deliver multidisciplinary expert treatment plan to patients within 24 hours. Initially developed for bresat cancer patients in India without ready access to expertise, over 28,000 patients across 68 countries with all cancers have since reached out to Navya. Prior research (SABCS 2014-2018 and ASCO 2017) showed, 1) 97% concordance of Navya predictions with an academic medical center in India and in the US 2) 97% of patients experienced significant anxiety relief due to the rapid, 24 hours turnaround time at the time of making a critical decision. 3) 79% of patients received treatment concordant with Navya recommendations on the ground. Unlike synchronized 1 patient: 1 doctor virtual consults in telemedicine where multidisciplinary collaborations are difficult, Navya uses AI to summarize medical cases and predict treatment plans that can be rapidly modified/vetted by multidisciplinary experts in 1-2 minutes asynchronously and collaboratively on a mobile app. This scales access to expertise for patients around the world beyond the limited availability of experts' time for telemedicine and in-person consults.

Methods: Three patient centered outcomes (travel distance, cost and time to receive expert treatment plan) were studied. All consecutive breast cancer patients who reached out to Navya between 1/1/17-1/31/19 but ultimately opted for in-person visit to an academic medical center were contacted by prospective phone follow up. This was compared to a balanced random sample of patients who only used Navya to obtain treatment plans.

Results: 195 in-person patients were reached for a prospective phone follow-ups and 132 of them had completed their visit at the academic medical center. 335 Navya patients were analyzed in the control group. The groups did not differ significantly in demographics or disease characteristics. In-person patients and Navya patients differed significantly with respect to 1) median travel distance (838 miles, IQR (237 -1105 miles) vs. 0 miles (p &lt; 0.05)) 2) travel related costs of $1597 [95% CI +/- $240] vs $105 online processing fee 3) total time to receipt of treatment plan (7.23 days, IQR (0.45 - 22.35 days) vs. 0.51 days IQR (0.19-1.057) (p &lt; 0.05)).

Conclusions: Cancer informatics solutions like Navya leverage AI to summarize the case, and predict guidelines and evidence based options. Combined by design with expert vetting from academic medical centers, such solutions can generate multidisciplinary treatment plans tailored to an individual patient. This scales ready access to expertise around the world. For patients with limited access to academic medical centers, such solutions eliminate travel burden, and significantly reduce cost and wait time to obtain a treatment plan. For experts who have no time to engage in telephone, video or written remote consults, vetting recommendations from the AI system with concise case summaries only takes 1-2 minutes per case. This model has shown significant ability to create access to specialty expertise, reduce the movement of patients to obtain treatment opinions, save costs and tremendously reduce waiting time for expertise in breast cancer, globally.

Citation Format: Rajendra Badwe, Benjamin Anderson, Nancy Feldman, Sudeep Gupta, Shona Nag, CS Pramesh. Prospective comparison of cost, travel burden, and time to obtain multidisciplinary tumor board treatment plan through in-person visits vs. an AI enabled health technology [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-13-04.