What’s The Problem?
You spend a lot of time and money implementing an MPI, EMPI or MDM to manage your patient, payer and provider data.
Unfortunately, they only do part of the job.
The"Determininstic" and "Probablistic" matching technologies at the heart of your MPI, EMPI and MDM have been used since the 60's.
Why Machine Learning Is
a Better Technology
Why Machine Learning is a better approach than the older traditional Deterministic and Probabilistic approaches:
As experts in match, we have experience in all 3 match technologies; deterministic, probabilistic , and machine learning and we can say that machine learning is by far the best approach to the match problem.
A custom match approach on traditional technologies (Deterministic and probabilistic) often requires complex logic that must be configured and understood by professionals in order to achieve good levels of accuracy. But these technologies still limit the total achievable accuracy because of their linear approach to the problem.
Goal: The number one goal to a match approach is its match accuracy. But how data is matched is seen differently by each customer which means a custom match approach, tailored to their needs, is an absolute requirement for the best accuracy. One-size fits all solutions will not achieve the same level of accuracy.
This makes the machine learning approach simple, fast and more accurate.
Machine learning is a non-linear technology that can apply match logic that mirrors a customer's requirement much closer and can do so through a simple use of data samples rather than configuring algorithms.
How do we know this is more accurate than traditional approaches? Our AI Steward helps resolve matches on other platforms where they cannot.
Other match platforms, IBM, Informatica, Epic, Cerner, Intersystems, Quadramed etc… create tasks, which are potential matches that are left by these platforms for humans to resolve. AI Steward steps in and resolves them, making the machine learning approach more accurate.