AI-Augmented Kidney Stone Composition Analysis with Auto-Release Improves Quality, Efficiency, Cost–Effectiveness, and Staff Satisfaction
The AI-augmented test system improved test quality, efficiency, cost-effectiveness and staff satisfaction related to this kidney stone composition test.
Authors
- Denise Rokke
- Rickey E. Carter (ORCID: https://orcid.org/0000-0002-0818-273X)
- Jillian Abbott
- Paul J. Jannetto (ORCID: https://orcid.org/0000-0002-5629-439X)
- Patrick L. Day (ORCID: https://orcid.org/0009-0008-5195-0037)
- Joshua A. Bornhorst (ORCID: https://orcid.org/0009-0003-9297-3271)
- Katie L. Kunze (ORCID: https://orcid.org/0000-0001-6850-6823)
- Mikolaj A. Wieczorek (ORCID: https://orcid.org/0000-0002-7879-9727)
- Patrick W. Johnson (ORCID: https://orcid.org/0000-0001-8365-1375)
- Britt A. Holmén (ORCID: https://orcid.org/0000-0003-3020-4293)
- Lennart Schneider (ORCID: https://orcid.org/0000-0003-4152-5308)
Institutions
- WinnMed (US)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- The Journal of Applied Laboratory Medicine
- Published
- 2024-12-19
- DOI
- https://doi.org/10.1093/jalm/jfae146
- Citations
- 2
- Primary Topic
- Kidney Stones and Urolithiasis Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.84