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Chris Hartfield

Sr. Data Platform Engineer, Clover Health

Chris has spent his career finding ways and building product to connect people to the content that they love. Before coming to Facebook to drive product development for creators, Chris worked at 20th Century Fox, Amazon, and Shazam. While at Facebook, Chris and his team launched the first Facebook products built specifically for creators in November of last year: the Facebook Creator App and the Facebook for Creators website.

Chris Hartfield

Sessions / 2018 / 1 talk

  • Democratizing Data with the Clover Transform Framework

    Clover cleans, normalizes, and aggregates data from a variety of healthcare systems, a vast domain of enormous complexity. But having engineers write all the data transforms for all this data is not sustainable.Clover has developed the “Clover Transform Framework” to enable people throughout the organization to write data transforms with documentation, monitoring, and testing built in. In this talk we'll discuss the balance between democratizing data while ensuring data quality, lineage, and control. We'll also cover some the extensions we've made since the initial release like machine learning and clinical submissions.

Sessions / 2017 / 1 talk

  • Parsing of Diverse Healthcare Data

    Healthcare data today remains a collection of quasi-structured data, ad-hoc sets of files, and customized file formats from hundreds of vendors and providers. While this data can be difficult to work with, to healthcare companies the value stored in this data is massive. Health insurance companies have typically used large numbers of staff to extract this data in a process that is prone with delays, errors, and incompleteness. At Clover, we have built an automated parsing framework to ingest the diversity of healthcare data and deliver improved efficiency to our insurance operations. Leveraging this parsing framework has enabled us to uncover ways to improve patient outcomes and decrease medical cost. In this presentation, we'll demonstrate the technologies used to build our parsing system that yields increasing value from complex healthcare data. We'll show how this a system balances both flexibility and strict quality controls to deliver a foundational data platform from which Data Science and Operation teams can build other data structures on top of. And finally, we'll discuss the strategies used to monitor and proactively respond to data quality issues.

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