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.
