Intelligent Medical Coding (IMC)

Solutions / INTELLIGENT MEDICAL CODING

Raise Your Coding Accuracy Without Raising Your Workload

As clinical programs grow in volume and complexity, traditional medical coding workflows reach practical limits: a 70–80% accuracy ceiling and 20–30% of terms requiring manual review, rework, and coordination overhead. CluePoints Intelligent Medical Coding (IMC) introduces a governed, deep learning decision-support layer that strengthens consistency and scalability within established coding workflows, without replacing the expert oversight that regulated clinical environments require.

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Accurate, Consistent Medical Coding Across Your Entire Portfolio

CluePoints IMC applies a deep learning model trained on MedDRA, WHODrug, and verbatim terms from over 100 clinical trials to generate ranked code suggestions within your existing coding workflow. Every suggestion is reviewed and approved by your coding team. Human validation, coding conventions, and regulatory dictionaries remain authoritative throughout. IMC strengthens the coding operation without replacing the expertise that sits at the center of it.

Up to 99% MedDRA & 95% WHODrug Accuracy

Generate ranked code suggestions for adverse events and concomitant medications at up to 99% MedDRA and 95% WHODrug accuracy, reducing the proportion of terms that require manual interpretation from first pass.

Approximately 50% Reduction in Manual Coding Effort

Reduce manual coding and quality control effort by approximately 50% by supporting routine coding decisions with high-confidence suggestions, decreasing rework between coders and approvers, and freeing expert capacity for higher-value review and oversight activities.

Consistent Coding Across Studies, Coders, and Time

Apply consistent coding logic across studies, programs, coders, and dictionary versions by drawing on validated historical coding patterns, reducing the variability that emerges when individual coders apply different interpretations to similar verbatim terms across an expanding portfolio.

Dictionary Upversioning Support

Handle MedDRA and WHODrug dictionary upversioning with up to 80% accuracy for newly introduced terms, reducing manual recoding effort, validation overhead, and the coordination burden that each dictionary update cycle typically requires.

Governed, Human-in-the-Loop Workflow

Operate within a governed, human-in-the-loop workflow where every code suggestion is presented transparently for human review and approval before finalization. The model proposes. Your team decides. Every suggestion, decision, and outcome is logged in a complete audit trail.

Full Audit Trail and Regulatory Traceability

Maintain a complete audit trail of every suggestion made, every decision taken, and every code approved or rejected, with full traceability and auditability designed for regulated clinical environments and inspection-ready documentation built in from day one.

How Teams Use CluePoints CMP

See Risk Signals Across Your Entire Portfolio, Not Study by Study

Most monitoring programs are configured to catch what their pre-set thresholds were designed to find. CluePoints CMP catches what they miss. By continuously applying multivariate statistical methods across all clinical, operational, safety, and lab data from every active study simultaneously, CMP surfaces co-occurring anomalies and systemic patterns across the portfolio. These are the signals that correlate with inspection findings but are often missed by single-domain KRI checks or manual study-level listing reviews. Every signal flows directly into a governed, traceable workflow.

Same Coding Standard, Every Study, Every Coder, Every Time

As clinical portfolios expand across studies, regions, and teams, sustaining consistent interpretation of similar verbatim terms becomes increasingly complex, particularly when alignment depends on individual expertise or locally maintained synonym lists. Coding variations across studies can undermine pooled analyses, introduce noise into safety data, and create inconsistencies that surface during regulatory review. CluePoints IMC embeds consistent coding logic into the workflow by drawing on validated historical coding patterns applied at scale, so similar clinical terms are categorized uniformly across every study, every coder, and every dictionary version.

Upversion Faster, with Less Recoding & Validation Overhead

MedDRA and WHODrug dictionary upversioning is one of the most resource-intensive recurring tasks in clinical data management. It involves impact assessment, identification of affected terms, manual review of reclassified codes, reconciliation of coding conventions, and documentation of every change for audit purposes. As portfolio size grows, the scale and coordination burden of each upversion cycle grows with it. CluePoints IMC reduces this burden by generating code suggestions with up to 80% accuracy for newly introduced terms, enabling faster adoption of new dictionary versions with less manual recoding and reduced validation overhead per cycle.

Focus QC Effort on Exceptions, Not Every Coded Term

Manual quality control in medical coding is prone to inconsistency because individual reviewers apply subjective thresholds to large volumes of data. At scale, this creates variability across studies that can affect signal detection and submission quality. CluePoints IMC enables risk-based quality control by surfacing the records most likely to require expert scrutiny and applying consistent logic to routine decisions. Approvers spend their time on exceptions and clinically nuanced cases rather than reviewing every coded term. Quality control effort decreases. Coding consistency improves across coders and studies without proportional increases in review overhead.

GOVERNED DECISION-SUPPORT FOR REGULATED ENVIRONMENTS

IMC Proposes. Your Team Decides. Every Output Is Transparent & Traceable.

CluePoints IMC is a governed decision-support tool designed specifically for regulated clinical environments. It doesn’t make coding decisions. It generates ranked code suggestions based on patterns learned from validated historical cases and regulated dictionary structures, and presents them transparently for your team to review, accept, or reject. The model doesn’t invent codes or finalize anything autonomously. Every suggestion, confirmation, and decision is logged in a complete audit trail consistent with regulatory traceability expectations. Human expertise and accountability remain at the center of every coding decision.

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MEDICAL CODING IN THE BROADER DATA QUALITY ECOSYSTEM

Consistent Coding Strengthens Every Downstream Data Quality Decision.

Medical coding doesn’t operate in isolation. The categorization of adverse events and concomitant medications directly influences safety monitoring, statistical analysis, cross-study comparisons, and the quality of regulatory submissions. Variability introduced at the coding stage propagates downstream, affecting signal detection, query generation, and inspection readiness. CluePoints IMC strengthens the reliability of data feeding into every downstream clinical data quality activity: clearer safety signals, more consistent cross-study comparisons, and greater traceability at every stage of the clinical data lifecycle.

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