Segmed

To aggregate diverse data, we have partnered with many US-based and international hospitals and imaging clinics - about 600 healthcare institutions spanning 5 continents, to be exact. We come, we integrate our healthcare ETL pipeline with a healthcare institution, and start de-identifying the institution’s data. We can do this for both historical and real-time data, as well as both text and images. We handle de-identification in a way ensuring that protected health information - or PHI - never leaves the premises of the institution. Once this de-identified data transfer is established, we can aggregate our partners’ data inside of our Segmed Insight platform in the cloud. Insight is a web-app that our customers can enroll onto. In Insight, users can search the data, and build patient cohorts appropriate for your research and development. For example, if you want to find chest CT scans of patients with lung cancer or pneumothorax, you can. Or, if you want to get down to the nitty-gritty and seek out knee MRIs of patients in the Eastern US aged 30-60 with a torn ACL who had their scans taken on a Siemens machine - you can do that too. Once a user has built out their desired dataset, they can request a sample for Q/A purposes. We will deliver them a ZIP file with both de-identified reports and DICOM files (which contain the imaging information). If they are happy with this, they can then place an order and pay for their entire dataset, which they can get in hand within days. We have ~100kLOC of Golang to do this fast (datasets are hundreds of GBs), and a bunch of Python to do ML at scale (classification, de-identification, OCR). We are on AWS. We are PostgreSQL / UNIX fans here, and we're looking for a lot of smart people (data engineers, data scientists, ML experts, hardcore software engineers and systems admins). Usual day is: how to extract million images without any PII out of the hospital and present it to clinical data scientists quickly.