Mendel AI: Enhancing Clinical Data Processing for Empowered Clinical Decision Making With Kristin Maloney, MS, BSN, RN, OCN®, and Waqas Haque, MD, MPH

Artificial Intelligence (AI) is revolutionizing healthcare, and oncology is no exception. In this episode of the Exploring AI in Oncology series, Kristin Maloney, MS, BSN, RN, OCN^®, a Certified Oncology Nurse and the Clinical Informatics Lead at Mendel AI, speaks with Dr. Waqas Haque on how AI can address some of the biggest challenges in oncology. She shares more about Mendel’s innovative platform, which leverages physician-level cognitive capabilities to enhance data processing and improve clinical decision making and patient outcomes.

This episode is also available as a podcast. Listen here!

The Evolution of Mendel’s AI Platform

Mendel, founded in 2017, is an AI platform that combines large language models (LLMs) with proprietary clinical hypergraphs to create a neuro-symbolic AI system. This approach addresses the limitations of traditional LLMs in healthcare, such as the lack of logical reasoning and domain-specific knowledge.

Ms. Maloney explained, “While generative AI has transformed many fields, healthcare demands deeper domain understanding and longitudinal reasoning, which our platform achieves by anchoring AI in medical knowledge through hypergraphs.” This innovative system enables Mendel to process complex, multi-source clinical data at scale, extracting actionable insights to aid clinicians.

Tackling Unstructured Data in Oncology

One of the most significant challenges in oncology is managing unstructured data from electronic medical records (EMRs). Mendel’s platform integrates structured and unstructured data into a single comprehensive record using advanced technologies like optical character recognition (OCR), named entity recognition, and machine learning.

According to Ms. Maloney, “Our clinical reasoning engine holistically analyzes data across entire patient records, enabling deeper insights into the longitudinal patient journey. This is a game-changer compared to traditional NLP tools that often miss critical nuances.” This approach streamlines data processing, improves accuracy, and reduces costs, significantly enhancing the utility of clinical data.

Improving Clinical Trial Recruitment

Recruiting patients for clinical trials is another persistent hurdle in oncology. Ms. Maloney emphasized the importance of Mendel’s Hypercube features, which facilitate low- or no-code cohort creation. This tool enables faster and more accurate patient identification for trials. She noted, “Cube cohorts can rapidly identify eligible patients, making it easier for research teams to pre-screen and backfill slots, ultimately improving trial accrual and feasibility assessments.”

By automating and refining cohort retrieval, Mendel’s platform helps clinicians and sponsors make data-driven decisions and optimize recruitment strategies.

Key Takeaways for Oncology Clinicians

1. AI Enhances Clinical Reasoning: Mendel’s neuro-symbolic AI system mimics clinical reasoning, integrating structured and unstructured data for a comprehensive view of the patient’s journey
2. Streamlined Data Processing: Advanced technologies like OCR and semantic search allow for faster, more accurate data processing, enabling real-time insights
3. Optimized Trial Recruitment: Tools like Hypercube simplify patient identification and feasibility assessments, addressing major bottlenecks in clinical trial recruitment

As AI continues to evolve, platforms like Mendel demonstrate how innovative technology can empower oncology clinicians to improve patient care, accelerate research, and transform the future of cancer treatment. By integrating explainable, clinically responsible AI into workflows, oncology professionals can achieve greater accuracy and efficiency, ultimately benefiting patients and advancing the field.

About the Speakers

Kristin Maloney, MS, BSN, RN, OCN^®, is a Certified Oncology Nurse and Clinical Informaticist with over 15 years of experience spanning bedside nursing, clinical research, and clinical informatics. Currently serving as the Clinical Informatics Lead at Mendel, she applies deep oncology expertise and healthcare informatics knowledge to advance clinical AI solutions. Ms. Maloney’s work focuses on improving data quality, ontologies, and AI models while driving real-world impact through innovative tools that enhance patient care and clinical research. She holds a Master of Science in Healthcare Informatics and is recognized for her expertise in clinical informatics, oncology practice, and product development. Passionate about leveraging data and technology to drive meaningful improvements in patient care, she brings a unique perspective to the intersection of clinical practice and AI innovation.

Waqas Haque, MD, MPH, is a Hematology/Oncology Fellow at the University of Chicago. He recently completed his Internal Medicine Residency at New York University (NYU) in a Clinical Investigator Track. Dr. Haque has balanced his patient care work with a variety of research projects. During his fellowship training at the University of Chicago, he plans to further his work in innovative clinical trial design, value-based care delivery to cancer patients, and clinical investigation.