A research infrastructure for real-time evaluation of predictive algorithms for intensive care units

In the medical informatics, most algorithms for clinical settings are initially developed and evaluated using retrospective data. However, researchers often lack convenient methods to validate their algorithms with real-time patient data. To bridge the gap between research and clinical applications, we present a research infrastructure that grants researchers access to real-time clinical data without disturbing patient care. The infrastructure is based on the Health Level Seven (HL7) messaging standard. Admission/discharge/transfer and observation result messages (containing lab test results and vital signs) are de-identified in real-time on a hospital network and subsequently transmitted to a research network. Then the processing cluster on the research network can process incoming de-identified data for a wide variety of applications. In the research network, a translator for converting the HL7 data stream into MATLAB format is created for convenient algorithm evaluation. As an example, we evaluated a hypotension predictor using our realtime testing environment. Our infrastructure can easily be replicated in other institutions and has the potential to benefit many researchers in translational medicine.