Real-world evidence (RWE) is built on the premise that data generated during routine clinical care can reveal how medicines and interventions actually perform outside the environment of controlled trials. Electronic health records (EHRs) and disease registries are often treated as the foundational infrastructure of this approach. To improve data quality, many systems increasingly rely on compulsory variables: fields that must be filled in before a visit can be closed or a record submitted. On paper, this looks like a solution to missing data. In reality, it introduces a different and more dangerous set of distortions that are rarely acknowledged when these datasets are later used for research, regulatory decisions, or health policy. What these systems capture is not simply “clinical truth,” but a version of reality that has already been filtered, structured, and constrained by the design of the software that clinicians are required to use.
This is an important and often underestimated issue in RWE.
When registries or EHR systems introduce compulsory variables, they do improve data completeness, but they also create several structural distortions that can quietly damage data quality and scientific validity.
The first problem is forced data fabrication. When clinicians are required to fill in a field to close a visit or submit a record, but the information is not clinically relevant at that moment, they do not leave it blank. They guess, approximate, or copy forward. This creates data that look complete but are not true. A disease activity score, a symptom severity, or a smoking status entered to satisfy a software requirement is often not a real measurement but a placeholder. Once these fields are used in RWE analyses, they contaminate effect estimates with noise that looks legitimate.
The second issue is measurement drift. Compulsory fields change the meaning of what is recorded. When something is optional, it tends to be entered when it is clinically meaningful. When it is mandatory, it is entered every time, even when it is irrelevant or unchanged. Over time, clinicians learn how to minimize friction by reusing old values or selecting defaults. The variable still exists, but it no longer tracks reality. This is why longitudinal data from mandatory fields often show suspicious stability or sudden step changes that reflect workflow rather than biology.
A third downside is clinical workflow distortion. Clinicians optimize their time. If the system forces them to fill many structured fields, they might compensate by spending less time on free-text notes, physical exams, or patient interaction. Ironically, the richest clinical information, which is usually in narrative form, becomes poorer as structured data requirements increase. From a data science perspective, the EHR looks better; from a clinical perspective, it often becomes worse.
There is also systematic bias introduced by missingness avoidance. When fields are compulsory, missingness disappears, but bias does not. It is simply hidden. Patients who are complex, unstable, or difficult to assess tend to generate more approximated entries than simple patients. This means the sickest patients often have the least reliable “complete” data. In RWE studies, this creates differential measurement error that standard statistical methods do not correct.
Another subtle effect is gaming and coding behavior. When certain variables are tied to quality metrics, reimbursement, or institutional reporting, clinicians and hospitals adapt how they record data. This is well documented for diagnoses, severity scores, and comorbidities. The variable stops being a neutral clinical observation and becomes a negotiated administrative artifact. Registries built on these variables inherit that distortion.
Finally, compulsory variables can lock a registry into outdated clinical models. Medicine evolves. New biomarkers, new endpoints, and new ways of classifying disease emerge. When a registry or EHR hard-codes mandatory fields, it becomes difficult to adapt. Researchers are then forced to keep using variables that no longer reflect how the disease is actually understood, simply because those are the ones the system collects.
The paradox is that compulsory data fields increase completeness but often reduce truthfulness. For RWE, truthfulness matters more. A smaller amount of data that reflects what clinicians really observe is far more valuable than a large dataset filled with numbers that were entered to satisfy a software requirement rather than to describe a patient.
To overcome these limitations, the goal should not be to eliminate structure, but to use it more intelligently. Registries and EHR systems might work best when they combine a small number of clinically meaningful structured fields with flexibility for clinicians to indicate uncertainty, change, or irrelevance. Optionality is not a weakness when it reflects real-world practice. Designing systems that allow “unknown,” “not assessed,” or “not applicable” to be valid entries preserves honesty in the data. Investing in better linkage to labs, imaging, and patient-reported outcomes can also reduce reliance on crude proxies. Most importantly, data collection should be driven by clinical and scientific needs rather than administrative convenience. When data systems respect how medicine is actually practiced, RWE becomes not just bigger, but genuinely better.
