Keeping AI Grounded in the Real World

In the years I have spent working with clinical, operational, and analytics teams, one issue has consistently appeared. The biggest challenge using advanced analytics and artificial intelligence in healthcare is not about creating models or deploying rules to an operational environment. The challenge begins after deployment, in keeping those analytics and AI systems in step with the reality of what actually happens when healthcare is delivered.

This is about drift. I will describe what it is, what causes it, how it manifests, and how a team can preserve what I will call signal integrity, the ongoing effort to make sure AI systems remain in step with the real world that they are designed to support.

What Makes AI Drift

AI systems lose their grounding over time. The systems learn behavior and pattern recognition from the data they are given. When that behavior undergoes a change, the system does not adapt to that change. The system continues to respond to the old question while society has moved on to asking a completely different question. That is where drift occurs.

There is a simple example to illustrate that. Consider a system that learns to identify high-risk patients based on how a healthcare unit documented high-risk patients a year ago. Now say that unit alters its documentation methods. For justifiable and completely valid reasons, the documentation methods of that unit undergo a change. Consequently, the same patients appear different in the documentation. The system has not failed. The system is actually responding to the old question. The system is confident that the old world is the current world. In the absence of proper oversight, the system will continue to answer that question, to its full extent, regardless of whether the world has changed.

In quiet ways, the drift reveals itself. None are dramatic enough to elicit a reaction. A recommendation feels slightly off. Something that used to fit cleanly no longer quite does. A clinician may ask why something happened. An analyst may observe behavior that is slightly different, but not enough to raise concern. Individually, any of these are easy to dismiss during a busy work week. Collectively, it is an indication that the tool is pulling away from the environment for which it was created.

AI does not need to be flawless, and it cannot be. What it does need is some way to stay in contact with the continuously evolving world of care, because nothing in the technology provides that contact on its own.

Keeping Tools in Step with Reality

Across a variety of contexts, the teams that keep their tools in step with reality tend to share certain habits. None are technical.

The first distinguishing characteristic is that they detect the early signs of drift. The first signs of drift are usually minor, and they are often comments made by clinicians or quiet observations made by analysts. Sometimes, these comments and observations are more accurate and truthful than several positive metrics on a dashboard. These weak signals do not stand out and must be identified. They should also be treated as early drift signals and not be ignored as one individual having a bad day.

The second characteristic is the ability to easily voice a concern. Highly effective processes and systems are characterized by simplicity and obvious steps. The process of raising concerns is effective when people believe a minor concern will be addressed. On the contrary, when people believe a minor concern will be ignored, the concern will not be raised and the opportunity to signal concern will be lost.

The third is the practice of regularly checking assumptions. All rules and models are based on assumptions, and some of those assumptions are bound to become obsolete over time. Regular reviews are not taken as a sign that a decision was made incorrectly. Rather, it keeps pace with the rapidly evolving field of healthcare.

Fourth, lived experiences must be treated as valid evidence. Observers closest to the work often sense that a tool is drifting out of step before any data or metrics indicate that this is the case. Observers are not throwing around soft anecdotes. These observations signal that a shift has occurred. The teams that consistently incorporate lived experiences are the teams that keep their tools grounded.

Why It Happens Even in Good Organizations

Drift should not be considered evidence of an ineffective team. It is most often seen in the better, more effective organizations. This is also true in healthcare. Drift is bound to happen. If a tool is no longer in step, capable individuals do what competent individuals do. They refine and adjust the tool and introduce an implicit workaround.

While these adaptations and workarounds are reasonable in the moment, they also hide the very signal that oversight needs. A workaround conceals the mismatch. The tool appears to be functioning. The people are absorbing the gap. AI makes this sharper, because it rests on logical structures that can fall out of step quickly, and without a clear path for feedback the assumptions that shaped it drift away from reality unnoticed.

Drift is a normal occurrence. The aim is not to prevent drift. Instead, the goal is to make drift visible early enough to resolve it while it is still small.

The Rhythm of Basic Oversight

This kind of oversight does not need to be elaborate or highly technical. The goal is to establish a cadence for checking in with the people and processes that are supported by the tool. Constructed with the following questions, oversight works best when conducted with simplicity and regularity.

Do the workflows that were established during construction still exist? Are the outputs still deemed helpful? Is anything about the tool causing confusion? Has the work the tool is intended to support changed? Has the feedback that was submitted been assessed? None of these questions is complex, and asking them regularly is the greatest safeguard to prevent a tool from slipping into obsolescence.

The reason this approach is effective is because of the predictable cadence it establishes. It becomes an integral part of how teams engage with their tools, rather than an unscheduled assessment that is called for due to unforeseen circumstances. By the time you are conducting an unscheduled assessment, the tool has likely been drifting and becoming less useful for a long time.

Grounding Artificial Intelligence

AI can positively impact clinical and operational teams, but it must remain grounded in the day-to-day realities that these teams encounter. The preservation of signal integrity ensures that AI remains connected. This work allows small comments to become early alerts. It narrows the gap between tool design and the work that leads to its eventual use. It builds and protects the trust that is needed to use the tool at its peak utility.

The ever-evolving nature of the healthcare industry and its infrastructure has revealed that grounding AI has become a primary rather than a secondary concern for making the technology safe and reliable. The integrity of the model was never the challenge. The real work is the never-ending struggle that is required to ensure that the technology remains grounded and honest about its perception of the world.

Christopher Hutchins Founder and CEO, Hutchins Data Strategy Consultants