Healthcare Data Management
To meet that need, Seygen has used elements of our SMX API Integration platform to provide data integration, data transformation, and data analysis in the healthcare industry.
The solutions we create provide system interoperability and secure health data exchange. They also ensure all healthcare standards are being followed, including HL7 and CCD formats.
These two case studies demonstrate our capabilities with health information exchanges and domino-matching algorithms.
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DirectHIE is a health information exchange we created that allows patients, hospitals, doctors, labs, and insurance companies to safely and securely share patient and diagnostic information. It was developed as a solution to cut costs and expedite the process for diagnostics and referrals as part of the Affordable Care Act (“Obamacare”).
This health information exchange includes features such as:
Direct protocol implementation with secure SMTP combined with an Electronic Health Record (EHR) system. This feature uses public-private key mechanisms to secure emails between healthcare providers including physicians, hospitals, and pharmacies.
EHR-Edge server gateway supports asynchronous messaging and web services. This feature is used by external providers to obtain health information without impacting the EHR system’s performance. It allows patient query, discovery, and information retrieval using HL7 and CCD formats.
The solution integrates with a third-party records management system that maintains patient information securely while allowing users to query (retrieve) patient data and upload new data.
The user interface allows clinic and medical staff to refer patients to other physicians, labs, etc., display patient records, schedule patient visits, and message patients and providers.
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The Domino Organ Matching solution that Seygen created for an India-based healthcare company integrates an organ matching algorithm to identify and recommend matches with the best chance of success.
The complex algorithm was trained on a series of data variables to rank combinations of recipients and donors. With this ranking, the doctor or hospital can select the most appropriate donor matches.
It was built with the following features:
Implement both Hungarian and Blossom algorithms to derive donor-recipient matching score
Generate matching scores for multiple donor-recipient pairs that enable medical personnel to set up a domino transplant process
The parameters for the matching algorithm include:
Blood group
HLA antigen
Recipient sensitization (antibodies – CPRA/Previous crossmatch positive)
Waiting time on dialysis
Failed vascular access
Previous failed transplants
Period from registration
Kidney function of donor (GFR)
Age difference between donor-recipient
Location of patient in relation to donor
Presence of Hepatitis B/C and/or HIV
General health of donor (BMI & presence of hypertension)
Health of recipient (presence of diabetes and other complications)
A central database where multiple nodal agencies can enter and manage donor/recipient data
Ability to run a match at center, city, state or national levels
