KenSci

KenSci’s AI Platform for Digital Health is designed with scalable, enterprise-ready data architecture that automates the ingestion, data preparation, processing and transformation of data into business intelligence (BI) and artificial intelligence (AI) ready formats within the Azure cloud.

The service allows quick, repeatable data ingestion from EMR, claims, devices and other public and custom data sources into a Azurean basedAzure-based data lake, offering migration from on-premise or cloud assets.

Pre-built data connectors integrate data into industry standardindustry-standard schema and data formats with connections to FHIR, Common Data Model, Synapse, and Databricks for downstream analytics applications.

KenSci’s AI Platform comes with an integrated analytics development platform that enables in-house analytics on top of data pipelines, giving clients access to a drag-and-drop report builder. AnalyticsThe analytics team don’tdoesn't have to source data and prep the data to create new reports, because the data pipeline is managed for quality and application-readinessapplication readiness.

The web-based analytics portal uses a PowerBI-based report building interface, and offers access to over 100 health features for creating new reports and dashboards. Auto-generated KPIs and system-wide metrics enable out-of-the-box reports and dashboards that can identify ROI opportunities based on system levelsystem-level insights.

KenSci's feature library holds hundreds of clinically-validatedclinically validated healthcare attributes. These attributes are auto-generated by underlying data pipelines, providing easy-to-develop and use tags for AI and ML model usage.

The platform's clinically and research validatedresearch-validated phenotypes enable the segmentation of underlying data into common use-case applications. These phentotypesphenotypes accelerate variation analysis, agile experimentation, and usage in model development and testing.​

KenSci’s data pipeline aggregates streaming and batch data into aan ML pipeline for model development, testing and deployment. This enables a wide variety of real-time data use-cases​, with support to HL7 and IOMT real-time data.

The AI model management platform allows for model development, testing, deployment, monitoring and maintenance across the lifecycle of AI and ML models. This enables data science teams to bring AI basedAI-based insights into production workflows.

AI platform allows for training datasets that reference the ingested data pipeline as well as public data setsdata sets. Training data sets are available in the AI model development workspace and allows sharing, monitoring and tracking during model development and testing.​

The company was founded in December 2015 by Ankur Teredsai, David Hazel and Samir Majure in Seattle, Washington, United States. KenSci was acquired by Providence-based healthcare AI company TegriaTegria on June 24, 2021.

The company was founded in December 2015 by Ankur Teredsai, David Hazel and Samir Majure in SeattleSeattle, Washington, United States. KenSci was acquired by Providence-based healthcare AI company Tegria on June 24, 2021.

The service allows quick, repeatable data ingestion from EMR, claims, devices and other public and custom data sources into a Azure based data lakedata lake, offering migration from on-premise or cloud assets.