Failure After Go-Live
AI does not fail at deployment. It degrades after.
Most programs monitor whether systems are running, not whether outputs remain accurate or aligned with real-world use.
Real-world conditions rarely stay static. Performance shifts as data and conditions change. Inputs evolve. Environments change. Models that perform well at launch begin to drift. Clinical practices and patient populations shift. These changes drive performance drift and reduce reliability over time.
This is especially true for the U.S. Department of Veterans Affairs (VA) and the Defense Health Agency (DHA). Drift affects clinical decisions, patient outcomes, and operational performance. Federal health systems support critical decisions where small performance shifts pose significant risk.
Systems may run. That doesn’t guarantee reliable results.
Monitoring Uptime Is Not Enough
Many programs treat deployment as the finish line. They track uptime, latency, and system availability. They do not track accuracy over time. They do not detect bias drift. They do not validate outputs against real-world needs. Many programs rely on static benchmarks. Those methods establish a baseline, but rarely predict how systems perform in changing environments.
This creates false confidence. Teams assume systems perform as expected because nothing seems broken. Meanwhile, outputs degrade. Decisions rely on results that fall outside of acceptable thresholds.
How HITS Prevents Post-Deployment Failure
Performance drift does not trigger obvious failures. It creates misalignment over time. Accuracy declines. Error rates increase. Bias patterns shift across populations.
In federal health systems, those risks affect clinicians and beneficiaries.
HITS prevents this by making system performance measurable, testable, and defensible over time. We understand that systems must produce accurate outputs, not just run without errors. Teams must confirm results stay aligned as expectations and needs change.
HITS establishes clear criteria, validation thresholds, and monitoring expectations before systems go live. We translate mission needs into requirements that define how teams detect performance changes, validate outputs, and respond when results fall outside thresholds.
Here’s what that looks like in practice:
Outcome-based validation criteria. We define measurable thresholds that confirm system outputs meet mission expectations. These thresholds provide clear standards for acceptance and performance.
Continuous performance checks. We establish how teams track accuracy, error rates, and output quality over time. This ensures teams detect performance changes as they occur.
Alignment to real-world use. We ensure outputs reflect real operational conditions, not controlled test scenarios. This prevents false confidence.
Ongoing accuracy. We define how teams confirm systems remain accurate over time. This ensures accurate performance after deployment, not just system uptime
The result: programs detect performance issues and risk early. Teams correct issues before they affect decisions, workflows, and outcomes.
Continuous Validation Protects Outcomes
AI systems require ongoing validation. Programs must confirm that systems remain accurate, reliable, and aligned with real-world use. For VA and DHA systems, continuous validation protects patients, supports clinicians, and maintains trust in AI-driven decisions.
HITS helps federal health programs ensure AI systems continue to perform after deployment, not just at launch.
Book a 15-minute fit call to discuss teaming or direct support: https://calendly.com/jhoyte-hits/teamfit
References
- https://www.fda.gov/medical-devices/digital-health-center-excellence/request-public-comment-measuring-and-evaluating-artificial-intelligence-enabled-medical-device#:~:text=AI%20system%20performance%20can%20be,changes%20in%20input%20and%20output.
- https://pmc.ncbi.nlm.nih.gov/articles/PMC10632090/#:~:text=D%2C%20Acquisition%20shift%20as%20a,access%20of%20protected%20health%20information.