By Kin Lee, MBA, MS, and Emily Wells, MSW, CSW
Over the past several years, we have been evaluating how The Joint Commission can further utilize innovative technology to improve the accreditation and certification process – ultimately helping our accredited health care organizations more efficiently address patient safety and quality concerns.
Earlier this year, we launched new artificial intelligence (AI) technology called “Machine Learning for Survey Consistency.” Several internal groups at The Joint Commission, including Information Technology (IT), Surveyor Management and Development, and the Standards Interpretation Group (SIG) – developed this technology to provide organizations with more consistent and accurate survey/review findings.
The technology uses AI to identify the correct standard or element of performance (EP) for a finding to be scored under during an accreditation survey or certification review. The technology has over an 80% efficacy rate – improving the consistency and accurate assignment of a survey/review finding against the appropriate standard or EP.
Previously, only a fraction of data was leveraged as “searchable” to associate a finding with a standard or EP. This limited leverage of data resulted in:
- incorrect assignment of findings against a standard or EP
- extensive process to discuss and correct findings
- numerous standards flagged for review
- lengthy survey/review process
With the new technology, more than 99% of vetted data is now “unlocked” and available to:
- provide instantaneous search matches
- enrich search by enabling sentences in addition to keywords
- offer predictive search text
- generate discussions for standards that have frequent inconsistencies
Even with the absence of a keyword, the “Machine Learning Translator” significantly increases correct matching. This has allowed field staff and Standards Interpretation Group (SIG) staff – who typically search slightly differently – to receive the same result.
Field staff and SIG staff are currently using the technology across all accreditation and certification programs. The technology is available on our mobile application (Mobile Survey Technology) for staff to use during an on-site, virtual or hybrid survey/review.
We have already begun to receive feedback about how the technology has helped improve the consistency and accuracy of survey/review findings. In addition, it has created time-saving efficiencies for field staff as they score findings.
Moving forward, we will continue to evaluate the effectiveness of the machine learning technology. We also will expand our efforts aimed at increasing survey consistency in other areas with additional data sources.
To learn more about our machine learning technology, listento this Take 5 podcast. Also, stay tuned as we will continue to keep you apprised of the latest technology advancements from The Joint Commission on this blog.
Kin Lee, MBA, MS, is the Chief Information and Enterprise Security Officer at The Joint Commission. In this role, he oversees technology strategies and operations that advance the enterprise’s high-performance operating environment. He has more than 20 years of extensive technology experience across a variety of industries, including technology, manufacturing, human resources and financial sectors.
Emily Wells, MSW, CSW, is a Project Director in the Department of Surveyor Management and Development in the Division of Accreditation and Certification Operations at The Joint Commission. In this role, she manages projects, processes and performance improvement initiatives that help support Joint Commission surveyors, reviewers and senior leadership. She also serves as surveyor for the Behavioral Health Care and Human Services Program.