Premium Reports
Contact KLAS
 Download Report    Zoom in charts

Preferences

   Bookmark

Related Series

 No Related Series

Related Segments

 End chart zoom
LeanTaaS iQueue for Infusion Centers LeanTaaS iQueue for Infusion Centers
* A page refresh may be necessary to see the updated image

LeanTaaS iQueue for Infusion Centers
Emerging Technology Spotlight 2022

author - Niel Oscarson
Author
Niel Oscarson
author - Braden Taylor
Author
Braden Taylor
 
October 11, 2022 | Read Time: 1  minute

Operational limitations—such as variable staff and patient availability—can make it difficult for healthcare organizations to effectively manage infusion scheduling. This often leads to volatile workloads in which resources may be both under- and overutilized in the course of a single day. LeanTaaS iQueue for Infusion Centers leverages machine learning and predictive analytics to optimize scheduling templates, level load daily schedules, and flag future problem days for preventive action. This report examines customers’ experiences and satisfaction with iQueue for Infusion Centers.

to see the full dataset.

If you don't have a login, getting started is easy.

Provider Organizations
Fill out our registration form to verify your organization.
HIT Companies
Contact a KLAS representative for plans and pricing.

Key Findings:

  1. LeanTaaS iQueue for Infusion Centers Customer Experience: An Initial Look
  2. KLAS’ Points to Ponder
  3. LeanTaaS: Company Profile at a Glance
  4. Solution Technical Specifications (provided by LeanTaaS)
author - Elizabeth Pew
Writer
Elizabeth Pew
author - Jessica Bonnett
Designer
Jessica Bonnett
author - Mary Bentley
Project Manager
Mary Bentley
 Download Report

This material is copyrighted. Any organization gaining unauthorized access to this report will be liable to compensate KLAS for the full retail price. Please see the KLAS DATA USE POLICY for information regarding use of this report. © 2024 KLAS Research, LLC. All Rights Reserved. NOTE: Performance scores may change significantly when including newly interviewed provider organizations, especially when added to a smaller sample size like in emerging markets with a small number of live clients. The findings presented are not meant to be conclusive data for an entire client base.

​