The Evidence Behind Karibu
Karibu is designed around two evidence disciplines: keeping clinical guidance aligned with approved sources, and helping care teams understand and apply that guidance during clinical work.
What we cite: peer-reviewed studies published in 2020 or later (covering the post-COVID reorientation of medical education and the LLM era), plus foundational meta-analyses that remain the most-cited sources in the field.
Two evidence pillars
Evidence informs both what Karibu delivers and how it delivers it
Policy alignment and clinical governance
Karibu structures review around conflicts within policies, conflicts across policies, regulatory blind spots, and gaps against current clinical evidence.Findings are routed to qualified clinical owners for interpretation and approval.
Learning and point-of-care use
Karibu applies research on microlearning, spaced repetition, situational awareness, just-in-time guidance, clinical AI, and cognitive load to help care teams use approved guidance in practice.
Policy alignment
A structured approach to policy quality
Internal consistency
Review whether instructions, definitions, and requirements agree within a policy.
Cross-policy consistency
Review whether related policies give compatible guidance for the same clinical workflow.
Regulatory coverage
Surface areas that may require review against applicable regulatory requirements.
Alignment with clinical evidence
Surface areas where approved policy may warrant review against current clinical evidence.
Karibu produces structured findings for qualified review. The organization retains authority over policy interpretation, revision, and approval.
Responsible AI
Responsible AI begins with defined boundaries
Karibu answers from organization-approved policies. When the approved content does not contain an answer, the system directs the user to escalate. Clinical owners determine permitted sources, approval requirements, and release decisions.
Approved source boundaries
Answers are drawn only from the organization's approved content, within boundaries clinical owners define.
Policy-silent escalation
When approved content does not contain an answer, Karibu says so and directs the user to the appropriate escalation path.
Human review
Findings and generated learning content remain subject to review by qualified clinical stakeholders before release.
Monitoring and feedback
Interactions and feedback support ongoing monitoring, evaluation, and improvement.
Learning and delivery
Six learning and delivery principles
Microlearning
Across a review of 40 studies, short, focused lessons have been associated with improved knowledge acquisition, retention, transfer, and task performance.In one single-blind RCT of 60 ICU nurses, mobile microlearning was associated with reduced reported stress and anxiety.
Karibu delivers focused, scenario-based learning in units of five minutes or less, during approved working and training time.
Knowledge retention for selected policies and user-rated usefulness across the evaluation period.
- Monib et al. Microlearning: a systematic review. Heliyon 2025.
- Khalili et al. mHealth microlearning RCT. BMC Med Educ 2024. Stress p=0.001; anxiety p<0.001.
Evaluation framework
How Karibu should be evaluated
Karibu measures its own performance in each clinical setting across defined operational, usability, accuracy, and learning measures.
A note on the evidence base
The studies on this page support Karibu’s design principles. They do not constitute product-specific clinical validation. Karibu evaluates product performance through defined operational, usability, accuracy, and learning measures in each clinical setting.
Karibu Pilot Blueprint & Clinical Framework
Evidence, governance principles, and a framework for evaluating Karibu in a defined clinical workflow.
Built on the evidence.See it work in your clinical workflow.
Copyright © 2026 Karibu Connect Inc. | info@karibu.ai | San Francisco, CA
Last updated: May 31, 2026.