Machine Learning
This is the second in a two-part series on the importance of measuring accuracy when it comes to provider data, and the challenges of doing exactly that. If you haven’t read the first post, you may want to start there. Here is that link. In our original post on the topic, Aaron Beach, Orderly’s…
This post kicks off a two-part series on provider data accuracy. Aaron Beach, Orderly’s VP of Data and Engineering, takes a technical look at one of the biggest challenges in healthcare data: how accuracy is actually measured. His deep dive explains why many industry accuracy claims don’t hold up under scrutiny. In a companion post,…
Imagine reaching out to a provider only to find their phone number is disconnected, their listed specialty is outdated, or they no longer practice at that location. These seemingly small inaccuracies cause big downstream problems: missed referrals, delayed care, failed marketing campaigns, billing rework, and more. Provider data powers every part of the healthcare…
The Problem Imagine this scenario: A patient named Emily has been suffering from severe migraines and finally gets a referral to a highly recommended neurologist. She calls the number listed online but it’s disconnected. Frustrated, she tries another number, but it routes her to a different department. Meanwhile, her condition worsens, causing her to miss…
In this episode of Bite the Orange, learn how Orderly uses AI and machine learning to automate and improve provider data workflows in healthcare.