In this QCast episode, co-hosts Jullia and Tom explore medical coding in clinical data management. They explain what medical coding is, how it translates clinical narratives into standardised terminology, and why it plays a central role in safety evaluation, regulatory submissions, and analysis-ready datasets. The discussion walks through how coding works in practice, where common challenges arise, and what sponsors and data teams can do to improve consistency, quality, and efficiency across studies.
What Medical Coding Is and Why It Matters
Medical coding is the process of mapping verbatim clinical data, such as adverse events, medical history, and concomitant medications, to controlled dictionaries including MedDRA and the World Health Organization Drug Dictionary. This standardisation allows data to be aggregated and interpreted consistently across sites, subjects, and studies. High-quality coding underpins safety signal detection, supports meaningful summaries, and enables regulators to review data with confidence. When coding is inconsistent or poorly controlled, important trends can be obscured and avoidable questions can arise during inspection or submission review.
How Medical Coding Works in Practice
The process begins with investigators entering free-text descriptions into the electronic data capture system. These verbatim terms are then processed through a coding tool linked to a specific dictionary and version. Auto-encoding can suggest matches for common terms, but many entries require manual review to ensure clinical relevance and correct classification. Trained coders assess context, apply predefined conventions, and escalate complex cases for medical review when needed. Clinical data management oversees this workflow, ensuring queries are raised for unclear entries and that the final coded data are version-controlled and ready for analysis and reporting.
The Role of Coding Conventions and Dictionary Control
Because medical dictionaries often allow multiple valid coding options for the same verbatim term, coding conventions are essential. Conventions define how common scenarios should be handled, such as coding diagnoses versus symptoms, multi-concept terms, or combination medications. They promote consistency across coders and over time, reducing rework and supporting audit readiness. Dictionary version control is equally important. Sponsors should define the dictionary and version at study start and maintain alignment across clinical and safety systems, carefully managing any updates to avoid introducing inconsistency into the dataset.
Common Challenges and How to Address Them
Many coding issues stem from poor-quality verbatim data, late batching of coding activity, or unclear ownership across systems and vendors. Vague site entries increase subjectivity and queries, while leaving coding until late in the study creates bottlenecks close to database lock. Over-reliance on auto-encoding without sufficient review can also lead to inappropriate classifications. These challenges can be mitigated through early planning, clear governance, ongoing coding throughout the study, and regular reconciliation between clinical and safety databases.
Practical Takeaways for Clinical Trials
Effective medical coding benefits from upfront strategy and steady oversight. Improving verbatim data quality at collection simplifies downstream work. Defining and documenting coding conventions and dictionary versions supports consistency and inspection readiness. Keeping coding current rather than deferring it to the end of the study reduces pressure and rework. When coding is treated as an integrated part of clinical data management, rather than a final formatting step, it delivers cleaner data, smoother database lock, and more reliable evidence for decision making.
Jullia
Welcome to QCast, the show where biometric expertise meets data-driven dialogue. I’m Jullia.
Tom
I’m Tom, and in each episode, we dive into the methodologies, case studies, regulatory shifts, and industry trends shaping modern drug development.
Jullia
Whether you’re in biotech, pharma or life sciences, we’re here to bring you practical insights straight from a leading biometrics CRO. Let’s get started.
Tom
Jullia, today we’re focusing on medical coding in clinical data management. People often treat it as a back-office task. What do we actually mean by medical coding here, and where does it sit in the wider data lifecycle?
Jullia
Thanks, Tom. So, in clinical data management, medical coding is the structured translation of reported clinical information into standardised terminology. In practice, that means mapping adverse events, medical history, and concomitant medications from free-text entries into controlled dictionaries. Common examples include the World Health Organization Drug Dictionary, often called WHO Drug and the Medical Dictionary for Regulatory Activities, known as MedDRA. The point is consistency. Sites describe the same concept in different ways, and coding aligns those descriptions so they can be aggregated, reviewed, and analysed. It also connects multiple functions. Coding touches data cleaning, medical review, safety reporting, and statistical outputs, so it is most effective when it is managed as part of the overall data quality process and not as a late-stage formatting step.
Tom
So it’s not just tidying language. Why does it matter so much for decision-making and regulatory confidence?
Jullia
Because coded data drive how safety and treatment patterns are understood at scale. For adverse events, consistent coding allows meaningful grouping, from preferred terms up to higher-level categories, which supports signal detection and reliable summaries. For medications, coding supports accurate classification and avoids hidden duplication where the same drug is recorded under different spellings or brand names. Regulators also expect transparency. They want the coded outputs to be traceable back to verbatim entries, produced using recognised dictionaries, and supported by role controls and audit trails. If coding is inconsistent or poorly controlled, the risk is not only messy outputs. You can miss trends, create noise in safety review, and trigger avoidable questions during inspection or submission review.
Tom
What does a typical coding workflow look like, from data entry through to analysis-ready coded data?
Jullia
It starts with verbatim entry in the electronic data capture system, where sites describe an event or medication in their own words. Those verbatim terms then flow into a coding tool that is configured to a specific dictionary and version. Many tools support auto-encoding, where an exact or close match is suggested. Auto-encoding helps with volume, but it cannot be the whole process, because context matters. When terms are unclear, incomplete, or could map to several options, trained coders review and select the most appropriate code. Clinical data management coordinates this process, including raising data queries when the verbatim does not support confident coding, and aligning with medical reviewers for clinically nuanced cases. The result is a coded dataset with controlled terminology that can be used reliably for listings, summaries, and statistical tables.
Tom
You mentioned multiple options for the same verbatim. That is where coding conventions come in. What are they, and what do they prevent?
Jullia
So, coding conventions are the study-specific rules that define how coding decisions should be made when there is more than one plausible choice. Dictionaries are rich, which is useful, but it also means judgement is involved. Conventions set expectations on common scenarios, such as whether to code to a diagnosis when it is available rather than a symptom, how to handle multi-concept phrases, and how to treat combination products. Their value is consistency across coders, sites, and time. Without conventions, you end up with avoidable variability that makes aggregation less reliable and forces rework late in the timeline. With conventions, decisions become repeatable, easier to quality check, and simpler to defend because there is a documented basis for how selections were made.
Tom
Dictionary versioning is another detail teams sometimes overlook. How should sponsors think about dictionary choice and updates?
Jullia
So, the core principle is version control. MedDRA and WHO Drug are updated regularly, and sponsors should define which dictionary and version will be used for a study, then keep that choice consistent through analysis and reporting unless there is a clear reason to change. Mid-study changes can shift coding outcomes, because new terms appear and existing structures may be refined. If an update is necessary, it should be planned, impact assessed, and documented so the dataset remains coherent. It is also important to align versions across connected systems, such as clinical and safety databases, otherwise reconciliation becomes harder and discrepancies can appear in outputs.
Tom
On that point, where does integration most often break down between clinical, safety, and vendors?
Jullia
Breakdowns usually come from unclear ownership and timing. If teams haven’t defined which system is the source of truth for coding, or how updates are synchronised, you can see mismatches between clinical listings and safety narratives. Timing matters too. If coding is left too late, you get a surge of queries and manual reconciliation close to database lock, and that can create pressure on safety reporting if events are still being assessed. Clear governance helps. That includes defined hand-offs, routine reconciliation checks, and agreement on how recoding or corrections are handled so changes are controlled rather than ad hoc.
Tom
Let’s talk quality control. What does good oversight of medical coding look like, and how do teams show it’s robust?
Jullia
Good oversight starts with a controlled process and the right expertise. High-risk data, such as serious adverse events and medically complex terms, typically needs closer review, and many teams use targeted second checks or medical review for those categories. Audit trails should show who coded what and when, and any changes should be traceable. It’s also helpful to maintain a decision log for recurring tricky terms, so similar verbatims are treated consistently. The goal isn’t to create heavy bureaucracy. It’s to ensure coding is reproducible, clinically sensible, and transparent, so downstream outputs can be trusted and questions can be answered quickly if they arise.
Tom
What are the most common reasons coding becomes painful during a study?
Jullia
Well, the biggest driver is poor verbatim quality. If the site entry is vague, contradictory, or missing context, coding becomes guesswork and queries multiply. Another driver is batching everything late, which creates a bottleneck and increases the chance of inconsistent decisions under time pressure. Over-reliance on auto-encoding can also contribute if suggested matches are accepted without enough review. Finally, disconnected workflows across systems create reconciliation work that nobody planned for. None of these are rare, which is why coding benefits from upfront design and steady monitoring rather than end-stage rescue work.
Tom
Thanks, Jullia. Now, if someone wants quick wins, what should they do this week to improve their coding approach?
Jullia
So I’d suggest three practical steps. First, tighten verbatim capture by reinforcing clear site entry expectations and querying ambiguous terms early. Second, lock in conventions and dictionary versioning so decisions are consistent and traceable. Third, keep coding current throughout the study rather than saving it for the end, and align with safety so both sides see the same picture. Those steps reduce rework and make outputs cleaner without adding unnecessary complexity.
Tom
To close, what are the two or three things you want listeners to remember about medical coding in clinical data management?
Jullia
Medical coding is the bridge between clinical narrative and analysable data, so quality and consistency matter. The work goes best when verbatim data are clear, conventions and dictionary control are in place, and coding is integrated with safety and data cleaning rather than treated as an isolated task. That combination supports reliable summaries, smoother database lock, and fewer surprises when outputs are reviewed.
Jullia
With that, we’ve come to the end of today’s episode on medical coding in clinical data management. If you found this discussion useful, don’t forget to subscribe to QCast so you never miss an episode and share it with a colleague. And if you’d like to learn more about how Quanticate supports data-driven solutions in clinical trials, head to our website or get in touch.
Tom
Thanks for tuning in, and we’ll see you in the next episode.
QCast by Quanticate is the podcast for biotech, pharma, and life science leaders looking to deepen their understanding of biometrics and modern drug development. Join co-hosts Tom and Jullia as they explore methodologies, case studies, regulatory shifts, and industry trends shaping the future of clinical research. Where biometric expertise meets data-driven dialogue, QCast delivers practical insights and thought leadership to inform your next breakthrough.
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