Seamless Data Ingestion

Seamless data ingestion and quality monitoring across the data chain

The US healthcare system generates millions of medical records, healthcare payments, and related administrative data points every minute. Significant efforts to standardize formats, create clearinghouses, and apply interoperability tools have all failed to connect these datasets in a meaningful way. There is tremendous value in connecting these datasets and discovering the insights across them.

Given the variation and scale of data, this continued challenge can only be met by a solution that can learn and evolve on its own. The recent breakthroughs in machine learning and big data have created the opportunity to develop a practical, long-term solution. Standardizing and transforming different data sources through automated ML solutions that are architected to be source-agnostic, scale to billions of records, and to natively check quality and completeness will finally make interoperability a reality.

Use Cases

Roster ingestion

HiLabs Provider product reduces the turnaround time for provider updates to be reflected in a health plan’s provider database and provider directory by automating roster ingestion across all formats and sources to a custom, standardized format suitable for the health plan’s database consumption.

Clinical data monitoring

HiLabs Clinical product automates clinical data ingestion and monitors data quality at each data transition checkpoint as data moves through the clinical pipeline. The product proactively detects potential data errors and performs root cause analysis to discover both data and care gaps. This also ensures high-quality data flows downstream, enhancing the value the plan derives from clinical data ingested.

Single point for ingesting structured and unstructured data sources

HiLabs Provider product allows plans to compare the structured data in their provider databases against the unstructured text in their provider contracts by using ML and advanced NLP to ingest and compare data from both sources.

Our Capabilities
Ingestion of both structured, unstructured, and mixed data from a variety of sources

HiLabs platform supports the ingestion and analysis of any healthcare data stored, processed, or received. These range from structured data sources like SQL, Oracle, Hive, Snowflake, etc., completely unstructured data sources such as provider contracts, clinical charts, etc. and complex, semi-structured sources like HL7, CCDA, FHIR, provider rosters, etc.

Effortless integration with the most widely used data lakes in the healthcare industry

HiLabs data ingestion platform easily integrates with the most widely used data lakes in the healthcare industry. Built on top of popular, big data frameworks such as Hadoop and Spark, the platform provides near limitless data ingestion capabilities. Out-of-the-box industry standard data quality checks paired with built in analytics allows the data ingested to be used for deriving previously hidden, invaluable insights.

Semantic, lexical, and content value similarity analysis can standardize and map ANY source data to any output format desired

The Auto Mapping feature of our product provides freedom from human errors in manual analysis and mapping of files from various sources. Our platform’s ML automates data ingestion workflows by imitating an analyst’s thought process to map and transform any incoming data across multiple sources to a single, usable format.

Experience effortless data ingestion and integration with HiLabs’ automated, scalable platform
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