Healthcare & Medical AI

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are crucial in orchestrating the development and enhancement of Healthcare & Medical AI systems.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to curate the data that powers these life-critical systems.

Training and Onboarding

PMs design and implement training programs to ensure workers understand medical terminology, privacy standards (e.g., HIPAA), and annotation goals. For example, in surgical video annotation, PMs might train workers to recognize procedural steps, using sample footage and clinical guides. Onboarding includes hands-on tasks like structuring EHRs, feedback sessions, and calibration exercises to align worker outputs with AI needs. PMs also establish workflows, such as escalated reviews for sensitive bioinformatics data.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., annotating 10,000 EHR entries) and set metrics like accuracy, consistency, or diagnostic relevance. They monitor progress via dashboards, address inefficiencies, and refine guidelines based on worker feedback or evolving healthcare standards.

Collaboration with AI Teams

PMs connect data curators with machine learning engineers, translating technical requirements (e.g., precision in disease classification) into actionable tasks. They also manage timelines to ensure data delivery aligns with AI deployment cycles.

We Manage the Tasks Performed by Workers

The annotators, transcribers, or medical analysts perform the detailed work of preparing high-quality datasets for healthcare and medical applications. Their efforts are precise and clinically focused, requiring expertise and care.

Common tasks include:

Labeling and Tagging

For drug discovery, we might tag a sequence as “target protein.” In symptom classification, they label “cough” with “respiratory infection.”

Contextual Analysis

For surgical annotation, our team analyzes footage, tagging “clamp applied.” In EHR structuring, they assess records, tagging “diabetes” or “normal labs.”

Flagging Violations

In transcription, our employees and subcontractors flag unclear audio (e.g., muffled terms), ensuring reliability. In disease data, they mark ambiguous symptoms for review.

Edge Case Resolution

We handle complex cases—like rare disease symptoms or overlapping surgical actions—often requiring discussion or escalation to medical experts.

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

Data Volumes Needed to Improve AI

The volume of curated data required to train and refine Healthcare & Medical AI systems is substantial, driven by the diversity of medical data and the need for precision. While specifics vary by task and model, general benchmarks include:

Baseline Training

A functional model might require 5,000–20,000 labeled samples per category (e.g., 20,000 transcribed medical notes). For tasks like drug discovery, this could rise to 50,000 to cover biological variables.

Iterative Refinement

To improve accuracy (e.g., from 85% to 95%), an additional 3,000–10,000 samples per issue (e.g., misclassified symptoms) are often needed. For example, refining surgical annotation might demand 5,000 new frames.

Scale for Robustness

Large-scale systems (e.g., nationwide EHR analysis) require datasets in the hundreds of thousands to handle edge cases, rare conditions, or diverse populations. A disease classification model might start with 100,000 records, expanding by 25,000 annually.

Active Learning

Advanced systems use active learning, where AI flags uncertain data for review. This reduces volume but requires ongoing curation—perhaps 500–2,000 samples weekly—to maintain performance.

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

Multilingual & Multicultural Healthcare & Medical AI

We can assist you with your healthcare and medical AI needs across diverse linguistic and cultural contexts.

Our team is equipped to curate and process data for global medical applications, ensuring accurate and culturally relevant datasets tailored to your objectives.

We work in the following languages: