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Healthcare AI 2022
Proven Outcomes with Data Science Solutions
Author
Ryan Pretnik
Author
Jennifer Hickenlooper
Current Time Inside Cache Tag Helper: 6/10/2023 5:24:37 AM and Model.reportId = 1799
The potential for artificial intelligence (AI) to transform healthcare has been both championed and challenged. In 2019, KLAS published our first research on healthcare AI data science solutions, exploring healthcare organizations’ clinical, financial, and operational use cases and the early outcomes they were achieving. An update to that research, this report examines how outcomes and customer satisfaction have evolved in the years since. Though progress has been somewhat hamstrung by the financial and operational constraints of COVID-19, many of the organizations interviewed for this research have found results by focusing on the right problems.if you don't have a login, getting started is easy.
This report includes two types of data: (1) customer satisfaction ratings for several key performance metrics, and (2) case studies from individual organizations detailing their use cases, outcomes, and lessons learned. Summaries of the case studies are shared on the following pages. The complete case studies can be found in the full report.
Key Findings
- Struggling Jvion Customers Leaving Due to Unmet Outcomes and Financial Constraints
- ClosedLoop.ai Provides Top-Shelf Experience; Satisfaction with Health Catalyst Surges Following Increase in Prescriptive Guidance
- Ease-of-Use Challenges Can Hinder Outcomes for Cerner and Epic Customers
- Case Study Summaries
- Recommendations from Successful Organizations
Writer
Elizabeth Pew
Project Manager
Mary Bentley
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. © 2023 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.