Between clinical, behavioral, IOT devices, and genomics, data is growing at much faster rate than humans can comprehend. We use advanced data analysis and machine learning techniques to transform data into actionable insight.
Our platform uses micro-services architecture to support high-scale and distributed workloads over large volume of data.
Domain name system for custom URL lookup
Edge servers (CDN) for cached content and faster access
Elastic content store on Amazon S3 or Azure Blobs
Load balancer to distribute and fail-over traffic
Web front-end and services back-end nodes supporting multi-tenancy
Automatically scale front-end and back-end based on load
Multi-shard transactional databases with high availability and disaster recovery
Big data store for searching, machine learning, and aggregations
In order to achieve maximum flexibility, rapid solution design and development, and a high-degree of solution customization, we opted for a late-binding modeling approach that is based multiple layers of abstraction.
The first tier is a storage model the defines that physical structure of our healthcare data store.
The second tier is an entity model. This is a logical over physical representation of all the key healthcare related entities used by solutions on our platform including their properties, relationships, and CRUD methods.
The third tier is a data expression, correlation, and filtering model. This is a logical flow of data capturing key business concepts and rules.
The fourth tier is a visual representation leveraging the logical flow and rules through binding to physical data via the logical entity model.
The fifth and final tier encapsulates the user experience, flow, and business logic of the application. This includes auditing and role-based access control to views, operations, and content.
Our platform provides a common set of capabilities to all solutions on our platform.
Build a highly flexible and configurable solution with a responsive and adaptable user experience based on situation and available medium.
Define advanced processes and schedules with tasks that can be manual (human) or automated (system). Tasks includes invoking methods as well as sending emails, texts, and voice notifications.
Define visual rules on various data elements to generate conditional events which can trigger mitigation workflows.
Elevate various conditional events generated by the solution into notifications that is surfaced to the user for awareness or to take manual actions.
Perform advanced data redaction and interface restriction based on individuals and roles. Support single sign-on and audit all data access.
Share knowledge among users through public channels and private (group-based) feeds.
Secure one on one or group-based chats and notes between users as well as care team.
Identifying high-risk patients allows a providers to prioritize these patients for care coordination and targeted interventions. Predictive analytics avoids unnecessary hospitalizations incidents, and adverse health events.
Using modern technology it's now easier to aggregate and correlate clinical data with other data sources, then apply deep learning models to predict risk and improve outcomes.
Marketplace will focus entirely on PHM and bioinformatics apps and services.
Users would be able to contribute new clinical content and models such as dashboards, reports, workflows, clinical pathways, risk measures and opportunity measures which can be plugged into new or existing solutions.
Developers would be able to contribute new solutions, new data visualizations, new solution services (APIs), deep learning modules, performance models, machine learning models, and new research data sets.
Entrepreneurs and students can leverage our hosted (AWS or Azure) platform and services to build web and mobile solutions leveraging our extensible application framework, data analytics, rich visualization, and data management capabilities.
Business model may be a combination of revenue share and/or equity stake. Funding grants are also available for select ventures.