Leveraging EHR Data for Clinical Research
Clinical research has always lived on the boundary between what clinicians see and what researchers can measure. Electronic health records changed the terrain. They made large-scale data access feel possible, but they also exposed a messier truth: the data you can extract is not the same thing as the data you can trust. Turning EHR data into credible clinical research requires discipline, careful case definition, and a willingness to interrogate every assumption you bring to the table. I have seen teams move fast at the start, pull dozens of fields into a dataset, and then spend months trying to explain why the results looked implausible. Usually the culprit is not the EHR system itself. It is the interpretation layer: how diagnoses were coded, what “active medication” means in a chart, how labs were ordered versus performed, and how follow-up time was counted. If you want your study to withstand peer review, you build that discipline early. What “EHR data” really includes People often say “EHR data” as if it is one thing. In practice it is a bundle of record types that behave differently. You typically have coded diagnoses and procedures, medication orders and administrations, lab orders and results, vitals, encounter metadata, problem lists, imaging reports, clinical notes, and sometimes flowsheet-like measurements. Each stream has its own quirks. A diagnosis code may be entered for billing even when it does not match the clinical question of your study. A lab result might exist because it was ordered for another reason, not because the patient met your inclusion criteria in a meaningful way. Even within a single domain, the recording patterns can vary. Lab values can appear at different granularities depending on the institution. A creatinine measurement might be recorded every time it is drawn, but your study endpoint might require an estimate of kidney function. That means you must convert raw values into something clinically interpretable, and you must decide how to handle missingness and outliers. The first step in successful EHR research is to accept that your dataset will reflect documentation habits as much as biology. Once you accept that, the work becomes more manageable. You stop pretending the EHR is a passive record and start treating it like an instrument that needs calibration. Start with the question, then design around the EHR The most common project failure I see is designing the research workflow backward. Teams build a data extract first, then try to fit the study question later. This creates a mismatch that is hard to fix. A better approach is to translate the clinical question into data elements that exist in the record, with explicit rules. Consider something seemingly straightforward: “time to onset of sepsis.” In a chart, sepsis might show up as a billing code, an order set, an ICU transfer, a clinician’s note, or lab and vital sign patterns that support an algorithm. Each of those choices yields a different cohort and a different event definition. If you extract only codes, you might undercount because coding lags behind clinical recognition. If you use an algorithm, you might overcount because the algorithm triggers on patterns that resemble infection without meeting your clinical intent. Before touching the database, define: the population (who is eligible, at what anchor time, and how you handle transfers) the exposure (what counts as treatment, when it begins, and how you treat dose changes) the endpoint (what counts as the event, how you confirm it, and how you treat competing outcomes) Once those are defined, the EHR design decisions become more grounded. You can choose whether you need claims-based codes, clinician documentation, or derived phenotypes. You can decide how much you are willing to rely on structured data versus unstructured notes. You can plan for validation rather than hoping it works out. Cohort building: the quiet work that makes results credible Cohort definition is where EHR research either earns trust or loses it. It is also where the day-to-day judgment lives. Most EHR studies use an “index date” to anchor follow-up. That date can be as simple as the date of a diagnosis code or as complicated as the first occurrence of a lab abnormality that meets a threshold. The anchor matters because it defines exposure start time, baseline covariates, and time windows for outcomes. In my experience, the most subtle problems appear when the EHR offers multiple plausible anchors. Take chronic diseases. A patient might have elevated glucose readings for months before a formal diabetes diagnosis code appears. If you anchor at the diagnosis, your baseline period may miss earlier disease activity. If you anchor at the first lab abnormality, you might accidentally include people whose labs were drawn for a short-term illness and resolved quickly. A related issue is data leakage. If your outcome definition uses information that occurs after exposure begins, you can inadvertently encode future knowledge into baseline variables. That leads to biased results. It is not always obvious, especially when endpoints and covariates come from the same table with overlapping timestamps. You also have to handle the difference between what the EHR records and what the patient experiences. Medication lists can lag behind. Patients can get care outside the health system. Encounters may be incomplete. A patient can technically remain “in follow-up” in the database long after they have stopped receiving care there. That does not mean they stayed on treatment. It means the record stops. For all these reasons, cohort building benefits from explicit temporal rules and a patient-level audit mindset. Before analysis, you should be able to explain, for a handful of real patient examples, why they entered the cohort, when they were considered exposed, what counted as the event, and how censoring worked. If you cannot, your dataset probably has hidden logic errors. Feature engineering from messy clinical reality Once your cohort is defined, you need variables that represent the clinical constructs in your model. This is where EHR research often diverges from “just pull the columns.” Lab features illustrate the challenge. Suppose your study needs kidney function. The raw lab might be creatinine. Your clinical construct might be estimated glomerular filtration rate, a trend, a peak severity, or a change relative to baseline. Each choice changes the signal and the model’s interpretability. Medication features can be even trickier. Orders do not always reflect whether patients took the medication. Dose adjustments can occur. Some EHR systems record medication administrations during inpatient stays but only orders in outpatient settings. Persistence and adherence are often not directly observable. For comorbidities, you rarely get a perfect snapshot. The diagnosis might be listed as a problem, coded during visits, recorded during hospitalizations, or only appear in problem lists. If you use a single code-based rule, you may undercount comorbidities that were documented but not coded in time. If you use multiple windows and require confirmation, you may reduce noise but increase missingness in early years. A practical way to approach feature engineering is to distinguish between variables that represent baseline status and those that represent time-varying processes. If your exposure can change, you need methods that respect that change. If your endpoint depends on evolving risk, you need covariates that update in a way consistent with the clinical timeline. This is also where trade-offs show up. Time-varying modeling can be more defensible clinically, but it can introduce complexity in data extraction, increases the chance of mistakes in time alignment, and can strain computational workflows. Simpler baseline-only models are easier to implement but can fail when time-varying confounding is substantial. You usually do not need the most complicated approach. You need the approach that matches the clinical question and the data you truly have. Validation: proving your phenotype isn’t just a guess Phenotyping is where credibility is won. EHR-derived phenotypes can approximate clinical reality, but they can also drift based on documentation patterns, billing incentives, and local practice. The safest path is validation, even if it is limited. Full-scale chart review for every endpoint in a large study can be expensive, but you can validate key components. For example, you can sample a set of patients who meet the endpoint definition and a matched set who do not, then compare EHR-derived labels against a reference standard such as clinician adjudication or detailed chart review. Even if you cannot validate everything, validating the endpoint definition is usually high leverage. Validation does not have to be elaborate to be informative. A targeted review can reveal whether your endpoint definition triggers on true cases, near misses, or unrelated events. It can also show systematic misclassification. If misclassification differs by exposure group, you have a bigger problem than if it is nondifferential noise. I once worked on a study where an endpoint was defined using structured diagnostic codes. The results looked “reasonable,” but the event rate was lower than what clinicians expected for that setting. A quick chart review revealed that many cases were documented in clinical notes with a high specificity wording, but the codes were not consistently applied. The team had not coded the phenotype with notes in mind, so the dataset was missing real events. After updating the phenotype rules, the event rate aligned more closely with clinical intuition, and effect estimates stabilized. That kind of finding changes the story of the paper. It moves the work from “we used the EHR” to “we built and tested a measurement strategy.” Handling missing data without pretending it is random Missingness is unavoidable. It shows up in labs, medications, vital signs, and even the timestamps you rely on for temporal rules. The key question is whether missingness is informative. In EHRs, missingness often reflects clinical decision-making. A lab is typically ordered because a clinician wanted information, which can correlate with disease severity. Someone who is sicker might have more frequent labs. Someone discharged might stop having measurements. If you ignore missingness, you may bias estimates. If you impute blindly, you might create values that have no clinical support. The right handling depends on the variable and how it is used. For baseline covariates, you might treat missingness as a distinct category when missingness itself could be informative. For lab trends, you might use methods designed for irregular time series or use summary statistics that reflect observed periods only. For outcome definitions, you should ensure that missingness does not prevent event detection. For example, if your endpoint requires a lab threshold and labs are missing because the patient stabilized, you might misclassify event timing. Practical steps help. Before modeling, audit missingness by group. If missingness differs substantially between exposed and unexposed cohorts, you should plan carefully. You may need additional strategies, such as restricting to patients with adequate follow-up measurements for that endpoint, or using models that incorporate observation processes. A common mistake is to remove everyone with any missingness. That can shrink the cohort and distort representativeness, especially in multi-site datasets where documentation patterns vary. Better to be strategic: decide where missingness matters most to the scientific question. Bias and confounding in real-world data EHR data is observational, so confounding is the rule, not the exception. Treatment choice often correlates with severity, clinician preference, access to care, and unmeasured factors. When those factors also predict outcomes, you get bias. You can address confounding in multiple ways: multivariable adjustment, propensity score methods, instrumental variables when defensible, or marginal structural models for time-varying confounding. But the best method depends on your causal assumptions and the data structure. The hard part is unmeasured confounding. No amount of feature engineering can fully fix that. Your job is to reduce what you can measure, transparently define what you did, and test robustness with sensitivity analyses. One practical approach I find useful is to use “negative controls” when possible. For example, a code that should not be associated with the outcome can help detect systematic issues like immortal time bias or coding drift. Even if you cannot do formal negative control testing, you can reason about plausibility and check whether associations align with clinical expectations. Another bias source is “survival bias” and differential follow-up. If the exposed group has different patterns of return visits or health system engagement, the observed outcome time can shift. Censoring rules and follow-up definitions are not technical details; they are causal design. When you review your results, ask whether the pattern makes clinical sense across subgroups and time horizons. If the effect appears only in a narrow early window, consider whether it might reflect documentation timing or misaligned follow-up rather than biology. Data governance, privacy, and the reality of access Even when the science is solid, access and governance can shape what you can do. EHR data typically resides behind institutional safeguards. You may need data use agreements, IRB review, and a clear plan for de-identification. If you plan to use clinical notes, the risk profile changes. Notes are richer and often contain free text that can include personal information even after standard de-identification passes. In multi-institution research, governance also affects reproducibility and harmonization. Different sites may have slightly different data models, code sets, and timestamp conventions. You might not be able to use the same variables across all sites without mapping. A practical lesson: build your data dictionary and variable definitions early, and keep them aligned with governance constraints. If you decide late that you need a variable that is not available in your de-identified dataset, you might waste months. Conversely, if you design early with governance in mind, you can prevent rework. It is also worth planning for auditability. You should be able to reproduce your cohort extraction logic and share it internally. When a reviewer asks why a number is what it is, you need to trace it back to query logic, not just to a vague description. Scaling up without losing the measurement intent Large-scale EHR research can look impressive, but the measurement intent must remain consistent. When you scale, you multiply the ways things can go wrong. Different EHR systems structure data differently. Even within a single vendor ecosystem, local customization can influence which fields are populated. A lab test name might differ slightly, or reference ranges may not be available. Medication coding systems may represent the same drug differently. This is where standardized vocabularies help, but they do not solve everything. Mapping diagnoses and procedures to standard code sets can improve comparability, but you still need phenotype logic to match clinical intent. A code-based diagnosis is not automatically equivalent across sites if the coding practices differ. I have seen studies that scale too quickly get tripped up by subtle mapping inconsistencies. The impact can be uneven. If a phenotype relies on two codes, and one code maps imperfectly at a subset of sites, that subset can contribute biased estimates or inflate uncertainty. A disciplined scaling process includes site-level checks, distributional comparisons, and pragmatic harmonization rules. If you cannot ensure comparable measurement, you should either adjust modeling to account for site differences or consider limiting the scope. When clinical notes matter, and when they don’t Unstructured data in notes can improve phenotyping, especially for conditions where structured codes are incomplete or delayed. But notes add complexity. Notes require additional privacy handling, preprocessing pipelines, and a careful definition of what counts as evidence. Simple keyword extraction can be brittle, because clinicians phrase the same concept in different ways and sometimes use negation or uncertainty language. If you use natural language processing tools, you must treat them like measurement instruments too. Validate their outputs. Check false positives and false negatives. Understand how performance varies by clinician, specialty, or setting. That said, not every study benefits from notes. If your outcome is well captured by structured codes and medications and you validate them, you may get the best balance by focusing on structured data only. The more you rely on notes, the more you need a robust validation plan. A pragmatic middle ground is to use notes for validation of a structured phenotype, rather than for full endpoint labeling. That can tell you whether notes reveal systematic under-coding or over-coding in certain settings, without fully committing to a note-heavy pipeline. Practical workflow I’ve seen work A strong EHR research workflow tends to feel less like “extract data, run model” and more like “build measurement, test it, then analyze.” Here is what that often looks like in practice: align on the clinical question and specify the index date and event rules early, with temporal boundaries spelled out in plain language prototype cohort extraction on a small set of patients, then review patient timelines to catch time alignment errors define phenotypes for exposure, outcome, and key covariates, then validate at least the endpoint logic using a chart review sample audit missingness and measurement frequency, and decide how each missingness pattern will affect analysis rather than treating it as a nuisance run sensitivity checks for plausible failure modes like immortal time bias, alternative outcome definitions, or different baseline windows You can still move quickly. The difference is that “quick” is focused on fast feedback loops, not on rushing to final results with an untested measurement strategy. A short checklist before you trust the dataset If you only have time for EHR interoperability a few high leverage checks, these are the ones that prevent the most painful revisions later. Does the cohort entry date represent the clinical construct you intend, and is the timing consistent across patients? Does your outcome definition avoid using information that occurs after the exposure window begins? Have you confirmed that the event rate and baseline characteristics look plausible to someone who treats the condition? Did you audit missingness patterns and measurement frequency, especially for labs and covariates that drive the model? Have you validated the endpoint (or at least a sample of it) against a reference standard? How to report EHR-based studies so they hold up Reporting is not just a compliance task. It is part of scientific integrity. Reviewers will scrutinize your cohort definitions, data windows, and outcomes. If you cannot describe them clearly, your findings are harder to interpret and replicate. In EHR studies, the reporting details that matter most include: data source description at the level of structure and time coverage how you defined the cohort, including inclusion criteria and index date logic how exposure and outcomes were defined, including code lists or algorithm descriptions at a conceptual level handling of missing data, and whether missingness is treated as informative or handled via imputation or exclusion how you addressed confounding and what causal assumptions are being made censoring and follow-up definitions, including what “loss to follow-up” means in the database Strong reporting does not mean dumping every query. It means giving enough methodological detail that another team can recreate the measurement strategy, or at least understand where misclassification might originate. The future is not just bigger data, it is better measurement EHRs will continue to grow, and data access will likely improve. But the most meaningful progress in EHR clinical research is not sheer scale. It is measurement quality. Better phenotypes, more careful temporal rules, stronger validation, and transparent handling of missingness and bias will outperform “more features” almost every time. In my experience, the studies that age well in the literature are the ones where the authors did not hide behind the complexity of the data. They faced it directly, measured what they could defend, and showed how uncertainty was handled. If you treat EHR data as an instrument that needs calibration, rather than as a perfect mirror of clinical reality, your research becomes both more credible and more useful. That is what clinical research needs, whether it is answering a narrow question or trying to inform real-world practice.