Electronic Health Record (EHR) Data Structuring

This task sorts lives into lines—think “fever” tagged in a chart or “insulin” marked in a plan (e.g., “MRI result” noted, “allergy” flagged)—to train AI to read health like a doc. Our team structures these records, sharpening care with data that clicks.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are vital in orchestrating the structuring and annotation of data for Electronic Health Record (EHR) Data Structuring within healthcare AI workflows.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to label datasets that enhance AI’s ability to organize and analyze patient data effectively.

Training and Onboarding

PMs design and implement training programs to ensure workers master medical tagging, diagnosis annotation, and treatment labeling. For example, they might train teams to tag “hypertension” in a history or mark “blood sugar” in a test, guided by sample EHRs and clinical standards. Onboarding includes hands-on tasks like structuring patient files, feedback loops, and calibration sessions to align outputs with AI healthcare goals. PMs also establish workflows, such as multi-pass reviews for dense records.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., structuring 15,000 EHR entries) and set metrics like diagnosis accuracy, treatment precision, or data consistency. They track progress via dashboards, address annotation errors, and refine methods based on worker insights or evolving medical needs.

Collaboration with AI Teams

PMs connect structurers with machine learning engineers, translating technical requirements (e.g., high specificity for rare conditions) into actionable data tasks. They also manage timelines, ensuring structured datasets align with AI training and deployment schedules.

We Manage the Tasks Performed by Workers

The structurers, taggers, or medical analysts perform the detailed work of labeling and organizing EHR datasets for AI training. Their efforts are clinical and analytical, requiring precision and healthcare expertise.

Labeling and Tagging

For EHR data, we might tag items as “symptom” or “medication.” In complex tasks, they label specifics like “chronic pain” or “lab value.”

Contextual Analysis

Our team decodes records, tagging “stroke risk” in a note or marking “surgery date” in a plan, ensuring AI sees every patient detail.

Flagging Violations

Workers review datasets, flagging mislabels (e.g., “acute” as “chronic”) or incomplete data (e.g., missing tests), maintaining dataset quality and reliability.

Edge Case Resolution

We tackle complex cases—like vague notes or conflicting entries—often requiring deep review or escalation to clinical experts.

We can quickly adapt to and operate within our clients’ healthcare platforms, such as proprietary EHR systems or industry-standard tools, efficiently processing batches of data ranging from dozens to thousands of records per shift, depending on the complexity of the records and annotations.

Data Volumes Needed to Improve AI

The volume of structured EHR data required to enhance AI systems varies based on the diversity of patient profiles and the model’s complexity. General benchmarks provide a framework, tailored to specific needs:

Baseline Training

A functional EHR model might require 5,000–20,000 annotated records per category (e.g., 20,000 patient histories). For varied or rare conditions, this could rise to ensure coverage.

Iterative Refinement

To boost accuracy (e.g., from 85% to 95%), an additional 3,000–10,000 records per issue (e.g., missed diagnoses) are often needed. For instance, refining a model might demand 5,000 new annotations.

Scale for Robustness

Large-scale applications (e.g., hospital-wide analytics) require datasets in the hundreds of thousands to handle edge cases, unique cases, or new treatments. A structuring effort might start with 100,000 records, expanding by 25,000 annually as systems scale.

Active Learning

Advanced systems use active learning, where AI flags tricky records for further structuring. This reduces total volume but requires ongoing effort—perhaps 500–2,000 records weekly—to sustain quality.

The scale demands distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and clinical precision across datasets.

Multilingual & Multicultural Electronic Health Record (EHR) Data Structuring

We can assist you with EHR data structuring across diverse linguistic and cultural landscapes.

Our team is equipped to label and analyze patient data from global healthcare systems, ensuring accurate, contextually relevant datasets tailored to your specific AI objectives.

We work in the following languages: