Application programming interfaces for knowledge transfer and generation in the life sciences and healthcare.

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2020-01

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Abstract

Storing very large amounts of data and delivering them to researchers in an efficient, verifiable, and compliant manner, is one of the major challenges faced by health care providers and researchers in the life sciences. The electronic health record (EHR) at a hospital or clinic currently functions as a silo, and although EHRs contain rich and abundant information that could be used to understand, improve, and learn from care as part learning health system access to these data is difficult, and the technical, legal, ethical, and social barriers are significant. If we create a microservice ecosystem where data can be accessed through APIs, these challenges become easier to overcome: a service-driven design decouples data from clients. This decoupling provides flexibility: different users can write in their preferred language and use different clients depending on their needs. APIs can be written for iOS apps, web apps, or an R library, and this flexibility highlights the potential ecosystem-building power of APIs. In this article, we use two case studies to illustrate what it means to participate in and contribute to interconnected ecosystems that powers APIs in a healthcare systems.

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10.1038/s41746-020-0235-5

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Woody, Stephen K, David Burdick, Hilmar Lapp and Erich S Huang (2020). Application programming interfaces for knowledge transfer and generation in the life sciences and healthcare. NPJ digital medicine, 3(1). p. 24. 10.1038/s41746-020-0235-5 Retrieved from https://hdl.handle.net/10161/20581.

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Huang

Erich Senin Huang

Adjunct Assistant Professor in the Department of Surgery

Former Chief Data Officer for Quality, Duke Health
Former Director of Duke Forge
Former Director of Duke Crucible
Former Assistant Dean for Biomedical Informatics

Dr. Huang is currently Chief Science & Innovation Officer for Onduo by Verily, and Head of Clinical Informatics at Verily (Google's life sciences subsidiary), and is now adjunct faculty at Duke. Dr. Huang’s research interests span applied machine learning, research provenance and data infrastructure. Projects include building data provenance tools funded by the NIH’s Big Data to Knowledge program, regulatory science funded by the Burroughs Wellcome Foundation. Applied machine learning applications include “Deep Care Management” a highly interdisciplinary project with Duke Connected Care, Duke’s Accountable Care Organization, that integrates claims and EHR data for predicting unplanned admissions and risk stratifying patients for case management; CALYPSO, a collaboration with the Department of Surgery for utilizing machine learning to predict surgical complications. My team is also building the data platform for the Department of Surgery's "1000 Patients Project" an intensive biospecimen and biomarker study based around patients undergoing the controlled injury of surgery.

As Director of Duke Forge, Dr. Huang is working to build a data science culture and infrastructure across Duke University that focuses on actionable health data science. The Forge emphasizes scientific rigor, awareness that technology does not supersede clinicians’ responsibilities and human relationship with their patients, and the role of data science in society.


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