us domestic health service prices vary greatly
Two patients can have the same procedure or diagnosis at the same hospital on the same day and pay very differently for these services. Macrohealth’s mission combines scaled connectivity and data analytics to reduce health prices and operating costs for the entities that pay for them.
platform 2.0 intended to take macrohealth through its next stage of growth
With success in the international healthcare market and limitations from its previous generation of products, Platform 2.0 intended to support our strategy to connect healthcare entities at scale.
Currently, healthcare entities (insurance providers, networks, claim management systems, clearing houses, 3rd party administrators) required large teams across multi-month or multi-year projects to connect their systems. Previous generation Macrohealth products required custom code or deep involvement from a team. While our customers were successful in reducing this to < 6 months, we believed we could do better. Platform had 3 goals
Reduce time to connect from months to hours
Provide enterprise grade security, compliance, scalability, performance and support
Act as a data platform that helps drive our analytics products that help our customers get informed on the best strategy to manage their claim spend
solving for scaled connectivity with a no-code integration platform
Platform 2.0 incorporated best practices of integration products to simplify connectivity. It required deep analysis into the integration customer journey for entities in US domestic healthcare along with consideration for HIPAA compliance at each level.
Platform 2.0 was able to support ingestion, processing and mapping transformations of several types of EDI files: 834 enrollments, ,837 claims and 835 adjudications. Automating the creation and management of connectivity mapping helped provide compliant data to analyze pricing models at scale, ultimately optimizing healthcare spend for benefits providers.
Technical challenges ranged from:
Working on a platform with high data compliance and data management requirements. Protecting PII, managing HIPAA and privacy controls between healthcare entities was key in decision making
EDI as a file format required understanding of file structures so they could properly be parsed into the correct data entities and insight can be extracted, while adhering to compliance requirements above.
Platform performance required forecasting of data ingestion and processing requirements for large and small customers across multiple file types. Searching for key information in nested document structures was pertinent to understanding claims to pricing breakdowns and crafting stories for how patient care was related to different grains (healthcare providers, payers, networks, etc.)
While usability challenges abound given the complexities of the industry.
Overall, the initial and subsequent launch of platform 2.0 met the performance needs of connectivity and security. It’s succeeding iterations, long after I had left, included support for additional data types (FHIR/HL7) and the long tail aspect of price and insight optimization within the customer base.