MD-Staff was named the 2020 Category Leader in Credentialing by KLAS Research. MD-Staff was awarded an overall score of 93.6, making the software the highest-rated in the credentialing category for the second year in a row. MD-Staff is a cloud-based, AI-enabled and user-friendly credentialing software solution backed by 30 years of innovation.
Being named a KLAS Category Leader means that a vendor must receive top scores across six categories: Culture, Loyalty, Operations, Product, Relationship and Value. MD-Staff posted high scores in all six categories with its innovative yet easy-to-use product that is backed by strong support and training. Automated primary source verifications, drag-and-drop privileging and a “source of truth” database are all reasons why MD-Staff is top ranked in credentialing.
“Providers and payers demand better performance, usability, and interoperability from their vendor partners every year,” said Adam Gale, President of KLAS. “Best in KLAS winners set the standard of excellence in their market segment. It serves as a signal to providers that they should expect only the best from the winning vendors.”
The credentialing software industry is rapidly changing due to evolving client needs, vendor consolidation and advancing technologies. “Our singular focus is enabling our clients to advance patient safety initiatives using innovative technology and unmatched service,” said Nick Phan, Executive Vice President of ASM. “The 2020 KLAS Category Leader designation reaffirms our belief that the combination of great software and dedicated service yields very satisfied customers.”
The KLAS® Credentialing 2019 report can be accessed using the following URL: https://go.mdstaff.com/klas-category-leader-2020.
Applied Statistics & Management Inc. (ASM) empowers healthcare organizations to advance patient safety and quality objectives using cloud-based, AI-enabled software solutions. Over 1,200 facilities worldwide use our products, MD-Staff and MD-Stat, to automate and manage their credentialing, privileging, OPPE, FPPE and peer review processes.