Data Discovery & Enrichment Solution
What we do.
For AI companies, Medcase leverages a network of global healthcare experts to identify, diagnose, and audit medical data according to your annotation and labeling guidelines.
For data holders, Medcase raises the value of raw medical data and makes it quickly available to AI algorithms, according to the customer's annotation and labeling guidelines.
How we do it.
Our experts will ensure high-quality data enrichment that can shorten Time2Algo™ (time-to-algorithm), align datasets, and remove biases in a compliant & cost-effective way.
Medcase enables access to the expertise and knowledge of healthcare professionals in any specialty, in 20+ languages, in 30+ countries, at any scale.
- Access to global experts
- Contracting and payment of experts
- Ongoing feedback and reports
- Support or expand staffing capabilities
- Expedited project deliverables
- Expert annotation ensures algorithm accuracy and prevents errors
- Dedicated clinical operations lead for quality assurance
- Workflow design and optimization
- Compliant data repositories
- Source potential aggregate cohorts
- Access to multiple medical imaging modalities
- Complementary solution for EHR/EMR data enablement
- Discoverable & anonymized medical imaging data for AI algorithms
- Reader Studies for clinical and regulatory approval
Medcase provides professional enrichment services so that AI companies can teach computers to process visual information the way human brains do.
How Do We Provide Access to Health Data?
Medical Device Startup
A medical device startup leveraged Medcase for access to four US Board Certified radiologists with 5+ years of experience reviewing MRI Segmentations.
- The medical device startup required Medcase's assistance in reviewing and marking 540 segmented MRI datasets provided by the company for FDA purposes.
- The client provided Medcase with the appropriate datasets, labeling tools, and training materials to onboard the radiologists.
- The radiologists reviewed the segmented MRI datasets for overall accuracy (boundaries of segmented organs) and correctly annotated the segmentation mark-ups.
- The project was split between four radiologists, took on average 45-min per data set review, and the overall project took 405 hours to complete.
Life Sciences Research Organization
A cancer diagnostics medical equipment manufacturing company leveraged Medcase for access to 6 pathologists within the United States and EU, with the primary objective of developing an AI model that can assist pathologists in identifying clinically significant areas in breast tissue slides.
- The Medcase team trained each pathologist utilizing materials provided by the cancer diagnostics company.
- Each pathologist was required to pass an alignment test (15 cases for each pathologist to be completed in one week), so the company could ensure each pathologist's quality and efficiency before kicking off the project.
- For quality assurance, each case was reviewed by three pathologists. Each case took between 20-40 minutes to complete. The overall project took 325 hours to complete.
An innovative AI startup needed annotated X-ray images to develop its product and effectively build its machine accurately.
MISSION: Ensuring that non-PHI, de-identified data is available for startups building the systems and products of tomorrow.
- OCT 2021 - Medcase partnered with our customer, identifying the desired data type and the enriching process.
- NOV 2021 - The customer signed a contract outlining the goal of enabling and enriching the X-ray images with our leading radiologists.
- DEC 2021 - Medcase delivered the enriched images to the customer during our agreed-upon timetable.
Life Sciences Research Organization
ORGANIZATION: Focused on using technology to better understand health, as well as prevent, detect, and mange disease.
MISSION: Helping to drive better surgical outcomes by building AI and ML systems to provide feedback on surgical videos.
- Partnered to provide 20+ surgeons in Europe, and 4 auditor-ranked surgeons, to drive quality and efficiency.
- Studied and annotated 1,000's of anonymized surgical videos to feed into the AI/ML mechanisms.