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.
In this article, we’ll explore the key factors needed to deliver high-quality, stable outputs from your unstructured data.
It is certainly possible to piece together your own pipeline for unstructured data processing using off-the-shelf offerings. To do so, a variety of tools will need to be purchased from multiple sources.These include:
All of these components must then be cobbled together into a reliable pipeline capable of processing unstructured clinical data that delivers results at the required scale and quality for maximum benefit.
There are two key characteristics absent from the list above: AI built for healthcare and an end-to-end solution. No matter how advanced the individual components of a DIY assemblage may be, it can never offer these two crucial elements that make all the difference when processing unstructured clinical data.
Unlike open-source options, AI designed for healthcare understands unstructured data with the mind of a physician. Natural Language Processing (NLP) was only designed to understand very short amounts of text, and was not built to decipher healthcare-specific language. Additionally, it lacks the common sense, reasoning, and cognition necessary to accurately decipher the often-idiosyncratic text found within unstructured healthcare data.
However, AI built specifically for healthcare has the ability to read hundreds of pages of documents about a single patient, put all the notes of medical jargon and information together (rather than losing all previous information after a page is turned), and understand it the way a clinician would. This simply doesn’t exist with open-source NLP. And while it’s possible to manipulate various AI components to adapt to certain healthcare considerations, the end result will not measure up to a system built specifically for healthcare from start to finish.
It’s a bit like trying to retrofit a sedan with an upgraded engine, brake job, steering wheel, and set of tires thinking the end result will be a Formula 1 car. Even with the most talented mechanic assembling the parts, the resultant sedan wasn’t designed to handle corners at 120 miles per hour–the chassis will buckle, and fail.
An end-to-end pipeline has high-quality components purposefully designed to function together. Since off-the-shelf assemblage uses parts from multiple sources, there are more opportunities for issues to arise between the various pieces and vendors. Each component must build off the previous one–so if an error occurs, it must not only be dealt with at the source, but throughout the entire pipeline. Presuming, of course, that the error can be detected and pinpointed.
By contrast, when each piece of the pipeline is built and designed under one roof with the same outcomes in mind, stability is the result. This also ensures that if issues do arise, they will be dealt with by one team that understands the system inside and out.
Mendel founders Karim Galil, MD, and Wael Salloum, PhD, had a shared vision to make medicine objective by developing an AI that can read records like a doctor, at scale.
We’ve spent years developing the ontology and models that set Mendel’s system apart, building hierarchical representations of data that allow for objective clinical decision making, and combining symbolic AI and machine learning to recreate the mind of a clinician.
Our built-for-healthcare solution makes unstructured data machine-readable and HIPAA-compliant, and it has the ability to extract patient data with clinical intelligence, all in a white-glove, end-to-end solution.
Download our whitepaper for an in-depth look at this complex problem, and see how Mendel is advancing innovation in this field.
Delays in clinical trial enrollment and difficulties enrolling representative samples continue to vex sponsors, sites, and patient populations. Here we investigated use of an artificial intelligence-powered technology, Mendel.ai, as a means of overcoming bottlenecks and potential biases associated with standard patient prescreening processes in an oncology setting.
The application of Artificial Intelligence (AI) in healthcare has been revolutionary, especially with the recent advancements in transformer-based Large Language Models (LLMs). However, the task of understanding unstructured electronic medical records remains a challenge given the nature of the records (e.g., disorganization, inconsistency, and redundancy) and the inability of LLMs to derive reasoning paradigms that allow for comprehensive understanding of medical variables. In this work, we examine the power of coupling symbolic reasoning with language modeling toward improved understanding of unstructured clinical texts. We show that such a combination improves the extraction of several medical variables from unstructured records. In addition, we show that the state-of-the-art commercially-free LLMs enjoy retrieval capabilities comparable to those provided by their commercial counterparts. Finally, we elaborate on the need for LLM steering through the application of symbolic reasoning as the exclusive use of LLMs results in the lowest performance.
We’ve changed our look. Our goal remains the same: make medicine objective. The new site highlights the way our proprietary AI enables organizations to achieve quality and scale when structuring unstructured data. It comes down supercharging your clinical abstraction. We’ve validated that our human in the loop abstraction approach can support a machine that understands medical context like a physician. In our own experiments, the number of variables needing correction decreased by 40%. High quality abstraction = high quality data for cohort selection, real-world evidence, and registries.
The customer, a key player in the genomics space, had a strategic initiative to build a clinic genomic database to support their life sciences customers.
One clinical trial organization was using manual chart review and was looking to reduce the time it takes to find eligible patients.
From the Desk of the AI Team
Organizations that use patient data for internal or external research need to take steps to prevent the exposure of PHI to those who are not authorized to view it. They do this by redacting specific categories of identifiers from every patient document. Once the identifiers are masked, the risk profile of these datasets is significantly reduced. But how do you ensure that redaction engines are working to the highest accuracy?
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.