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.
The Mendel team is still buzzing from our week-long retreat in Cairo. The theme of the retreat was “coming together” and it was the first time the American and other remote employees were united with their Egyptian counterparts. Although there were many adventures–missing flights, seeing the pyramids, haggling at Khan el-Khalili–the highlight of the trip was collaborating together, as one global organization.
Competence via comprehension
Artificial intelligence (AI) is playing an increasingly important role in the healthcare industry. But to fully leverage the potential of AI, it must be equipped with clinical reasoning skills - the ability to truly comprehend clinical data, or in other words, to read it as a doctor would. When it comes to data processing tools, only a tool capable of clinical reasoning can effectively process unstructured clinical data.
Sailu Challapalli, our Chief Product Officer, spoke at a recent Harvard Business School Healthcare panel. The event brought together different healthcare and AI experts to discuss large language models and their impact.
Manually abstracting patient data at scale is an herculean task for humans alone. It is slow, expensive, difficult, and requires extreme precision and accuracy. Organizations have to choose between breadth and depth when it comes to making data useful for decision making. Because of these challenges, the Mendel team created Carbon. Carbon is an easy to use workspace that allows clinical abstraction teams to efficiently curate high quality clinical datasets at scale. The foundation of Carbon is Mendel’s AI. Carbon pulls directly from Mendel’s AI platform to give abstractors a headstart in identifying relevant data elements within a patient’s chart.
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
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