AI Co-Pilots for Cancer Care: Support for Treatment Decisions

•Get clear, simplified explanations of your diagnosis and treatment plan

•Prepare specific questions to ask your doctor before appointments

•Track and summarize your medical information in a concise format

•Identify potential clinical trials or new treatment options relevant to your specific cancer

•Ensure you understand medical terminology and side effects

•Empower you with knowledge about the latest research and guidelines that your local oncologist might not be aware of What are the challenges in accessing the latest cancer tests and treatments for community oncologists and patients at community cancer centers?

•Oncologist shortage : There's a global shortage of oncologists, especially in underrepresented communities.

•Slow guideline updates : It takes 3-6 years for new research and treatment guidelines to be implemented in community cancer centers, compared to just three months in academic cancer centers.

•Information overload : Oncologists (and patients and caregivers) struggle to keep up with hundreds of new research results published monthly.

•Limited time: Community cancer centers see about three times more patients per oncologist compared to academic centers, making it difficult to stay current with the latest treatments.

•Diagnostic gaps: Community centers often miss identifying all necessary genetic markers or potential treatment targets, with only 32% of markers typically interpreted for patient care.

•Lack of precision : Community centers may not always provide the most effective treatment protocols due to limited resources and knowledge gaps. How can AI co-pilots overcome these barriers to accessing the latest cancer tests and treatments?

•Ensure you receive standard of care treatments and get early information about new tests and treatments you may want to consider before they are incorporated in your cancer center

•Bridge the knowledge gap between community centers and academic cancer centers, e.g., provide up-to-date treatment guidelines to those who may not have access to the latest research, identify potential clinical trials and genomic targets that might be missed

•Help patients advocate for themselves by giving them clear, personalized information about their specific cancer type and considering their comorbidities

•Offer real-time insights and recommendations based on the most recent clinical research and trial data

•Improve the patient experience and personalization, e.g., customize language complexity, gender of the AI agent, incorporate social determinants of health data How can you learn more about using AI to guide your cancer care?

•Conduct your own research to find emerging AI tools for navigating your care; participate in patient groups and webinars focused on AI in cancer care; then try an AI tool to answer some of your questions, e.g., your testing and treatment options, side effects, your care journey, questions you should ask your doctor

•Contact Kingsley Ndoh at kingsley@hurone.ai.

•See previous conversations at the Cancer Patient Lab on using AI for cancer patient navigation:

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•The information and opinions expressed on this website or platform, or during discussions and presentations (both verbal and written) are not intended as health care recommendations or medical advice by Cancer Patient Lab, its principals, presenters, participants, or representatives for any medical treatment, product, or course of action. You should always consult a doctor about your specific situation before pursuing any health care program, treatment, product or other course of action that might affect your health. For the video recording of this conversation, please see here. For a transcript, please see here. For the slides, please see here. Meeting Notes KEYWORDS AI chat bots, cancer patient navigation, precision cancer care, community cancer centers, oncologist workforce shortage, AI co-pilots, patient treatment options, comorbidities, clinical decision support, patient data, genomic testing, patient retention, clinical trial data, wearable integration, patient advocacy. SPEAKERS Kingsley Ndoh (72%), Brad Power (7%), Matthew DeAngleis (7%), Hurona Chatbot (5%), Russ Hollyer (3%), Jane Wilkinson (2%), Burt Rosen (2%), Raj (1%), Rick Davis (1%) CHAT CONTRIBUTORS Rick Davis, Russ Hollyer, David Plunkett, Raj, Jane Wilkinson, Alexander Lalov, Burt Rosern SUMMARY Kingsley Ndoh of Hurone AI discussed their AI-driven platform designed to enhance precision cancer care navigation. The platform, currently being validated at UCSF, personalizes treatment options for oncologists based on patient diagnostics and comorbidities. It aims to bridge the gap between community and academic cancer centers by providing up-to-date guidelines and improving patient navigation. The system is HIPAA compliant and interfaces with EHRs like Epic. Future plans include incorporating genomic data and clinical trial information. The platform is intended to empower patients as their own advocates while also benefiting community oncology centers by reducing litigation risks and improving patient retention and revenue. OUTLINE Introductions

•Hurone AI is a startup addressing gaps in precision cancer care using AI co-pilots.

•There is a global oncologist workforce shortage, particularly in underrepresented communities and community cancer centers.

•Amy, a cancer patient, received less effective treatment at a community cancer center compared to the Mayo Clinic.

•The challenges faced by community cancer centers include high patient loads, outdated guidelines, and the importance of 24/7 navigation for patients. Hurone AI's Clinician Platform and Patient Support

•Hurone AI's clinician platform makes guidelines personalized and available to oncologists based on diagnostic workups.

•The platform considers patient comorbidities and produces treatment options with pros and cons, aiding in joint decision-making.

•The platform also serves as a thought partner for oncologists, consulting with other specialists based on patient comorbidities.

•AI can help manage the complexities of cancer treatment, including diagnosis, treatment planning, insurance scheduling, survivorship, and side effect management. The Future of AI in Cancer Care

•There is a symbiotic relationship between data, AI, and humans in the future of cancer care.

•Hurone AI uses vast amounts of data, including imaging, side effect data, and real-time data from wearables, to create personalized precision navigation for patients.

•The company aims to accelerate access to precision cancer care by leveraging AI, working with partners like the Cancer Patient Lab and big tech companies.

•Hurone AI has multilateral partnerships, including a pilot at UCSF and data sharing agreements in Kenya. Integrations and EHR Access

•Hurone AI is HIPAA compliant for clinical decision support and communicates with Epic data for patient platforms.

•They are still figuring out the best way to integrate with Epic for patient platforms.

•Epic could be a potential acquirer given the importance of integrating with EHRs. Demo of Hurone AI's Patient Platform

•Kingsley Ndoh demonstrated the patient platform, explaining how it helps patients understand their treatment plans and potential side effects.

•The AI agent, Hurona, explains the treatment plan for inflammatory breast cancer, including chemotherapy, surgery, and radiation.

•Hurona provides detailed information on side effects, such as fatigue, nausea, hair loss, and neuropathy, and explains medical terms like Muga tests.

•The interaction is live and based on de-identified patient data. Data Ownership and Customization

•Matthew DeAngelis asked about data ownership and how the data is used for improving the system.

•Kingsley Ndoh explained that patients own their data and can consent to its use for system improvement.

•The system is customizable, allowing patients to choose the level of medical terminology and gender of the AI agent.

•Matthew DeAngelis asked if the system can summarize clinical trial data, and Kingsley Ndoh said that this feature is in development. Sales Strategy for Community Oncology Centers

•The Hurone target market is community oncology centers.

•Their go-to-market strategy involves clinically validating the platform first with academic cancer centers like UCSF, Johns Hopkins, and Mayo Clinic.

•They are also creating a pipeline and demoing to community oncology centers.

•Community centers need to adopt the latest guidelines and practices. Patient Advocacy and Community Centers

•The system is designed to make patients their own best advocates, providing them with the information they need to ask the right questions during doctor visits.

•The system can also help community centers avoid litigation and improve patient retention by providing the best care possible.

•Closing the knowledge gap between community centers and academic centers is important. Development Plans

•The system is developing the ability to incorporate genomic data to identify targets and match new clinical trials.

•The system is currently fine-tuned for the top five cancers but plans to expand to other cancers in the future.

•They are validating the tool with patient groups and getting feedback from users to improve its effectiveness. Key Thoughts

•Collaboration between users, doctors, and AI is important to ensure that technologies are humane and solve the biggest pain points.

•User involvement is needed in the development and improvement of AI tools in cancer care.