Mendel’s white-glove solution builds the foundation for deeper analysis
We make unstructured data machine readable, HIPAA compliant, and extract patient data with clinical intelligence
We provide a front end solution for human abstractors and a smart ontology driven search engine to drive efficiency
Our team includes expert manual clinical abstractors in the United States and abroad
Mendel is a researcher-trusted artificial intelligence built to understand clinical data
Built specifically for healthcare and trained on the largest medical data set in the industry
and domain expertise to the extraction and abstraction process
Leverages multiple AI disciplines to bring a greater level of accuracy, granularity and stability to results
Not a black box. Whether you use Carbon or ingest a JSON, you’ll always see the source evidence
Deliver high quality de-identified patient data sets to support a wide variety of commercial use cases
Improve efficiency of patient recruitment by rapidly narrowing down to patients most likely to qualify
Characterize broader patient network to identify target populations of interest for research
Mendel has validated its clinical AI technology with third party statistical verification.
Mendel is the only company to validate outcomes with AI in a clinical setting.
UPDATES & NEWS — 2 MIN READ
Mendel.ai is improving identification of suitable cancer patients and matching to clinical trials. Mendel.ai, a US-based company founded in 2016, uses AI tools to analyse clinical data, including medical history and genetic analysis, from cancer patients. Its goal is to facilitate clinical trials in oncology research by organising and analysing RWD, and matching patients to the right clinical trials.45 They take medical data from many structured and unstructured data sets and turn these into data digitised patient journeys aligned to specific treatments. Having collected data on over 1 million journeys, its algorithm can segment patient populations by their demographic, such as how African American women with breast cancer respond to a particular drug. In essence creating in silico or patient-less trials. The AI algorithm built on these data helps trial recruitment by finding eligible patients.
Within the real world evidence space, the generally accepted process for creating a regulatory grade data set is to have two human abstractors work with the same set of documents and bring in a third reviewer to adjudicate the differences. These datasets also serve a second purpose - as a reference standard against which the performance of human abstractors can be measured. Although this remains the industry standard, it is expensive, time consuming and difficult to scale.
From the Desk of the AI Team
AI projects have created tangible results for a wide range of industries. Despite the innovation, it is important to remember that AI is not a magic wand that will solve every problem in every industry with a single wave.
Before embarking on any new endeavor or enterprise, certain questions come to mind: How are we going to handle this? Does our team have the expertise, bandwidth, resources, and time to handle this undertaking on our own? When it comes to finding a scalable way to structure your unstructured healthcare data, the answers to these questions will impact when/whether you deliver a top-tier product for your clients.
Human abstraction has long been considered the gold standard for extracting high quality information from EHR data. With the rise of NLP and machine learning, how should we evaluate these new technologies and are human abstractors still the correct comparison?
PODCAST — 40 minutes
Leslie Lamport is known for his fundamental contributions to the theory and practice of distributed and concurrent systems, notably the invention of concepts such as causality and logical clocks, safety and liveness, replicated state machines, and sequential consistency. Full Youtube video: https://youtu.be/rNQFPz2KSzQ
PODCAST — 60 minutes
Eze Abosi is VP of New Products at Optum Life Sciences. Eze and Karim Galil, M.D. covered topics such as career background and the healthcare technical ecosystem. They also talked about creative solutions that entrepreneurs and companies are creating with access to data. The conversation also touched on unstructured data, the webinar with Guardant Health, clinical genomics, and NLP. Watch the full Youtube video here: https://youtu.be/95Kv64SyE0M
PODCAST — 45 minutes
Daniel Ciocîrlan is a Software Engineer, founder, and instructor at Rock the JVM. Watch the full Youtube video here: https://youtu.be/PUMCzgK02p8
PODCAST — 30 minutes
Hylton Kalvaria is the Chief Commercial Officer at Mendel.ai, bringing our technology to healthcare businesses.
PODCAST — 26 minutes
John is an experienced early-stage investor, focusing on companies across frontier technology, enterprise software, robotics, healthcare, and artificial intelligence. He gravitates towards startups that are using science and technology to break down barriers to productivity growth and enable a better future for the largest number of people.
PODCAST — 33 minutes
Jason LaBonte is an executive with over 15 years of experience in leading healthcare information companies. Accomplished manager at all levels, including analyst and production staff, product management and product development teams, and executive teams.
PODCAST — 23 minutes
Mark Goldstein is a full-time investor and advisor, he runs a lit' seed fund and he is a partner at a Series A venture fund. He is also a Founder of UCSF Health Hub, UCSF's growth studio.
PODCAST — 32 minutes
Melisa Tucker, VP Product Management & Operations, Real World Evidence at Flatiron Health was our guest on Patientless Podcast.