Developing Next Generation EHR-supported Predictive Modeling: Developing The Johns Hopkins "E-ACG" System

Investigators: Jonathan Weiner, Dr.P.H., Hadi Kharrazi, Ph.D., and Suchi Saria, Ph.D.
Funders: Internal/ACG R&D Fund
Status: Ongoing

CPHIT (and the Johns Hopkins ACG R&D unit housed in CPHIT) has a major project underway to use new clinical digital data streams to enhance current predictive and analytic models. This project is being done in collaboration with faculty from the Johns Hopkins School of Medicine and the Department of Computer Science, along with Health Partners- MN, Group Health- WA , and Catholic Health Partners- OH. Some of the EHR elements that are being incorporated in advanced models include vital signs, lab values, cardiovascular data, clinician notes and patient reports. The goal of this project is to advance the state of the art of EHR based predictive modeling tools for high-risk case detection and management for populations. We will identify EHR and other HIT elements amenable to incorporation with traditional claims-based ACG Measures, and then test how to best integrate a combination of elements with the ACG System to enhance our predictive modeling ability. Over the various phases of this project, we will not only apply structured readily available EHR/clinical data sources, we will also apply Natural Language Processing (NLP) text mining approaches to capture information from unstructured data sources. We also will explore other types of “Machine Learning” techniques to develop prediction models that can be applied on a dynamic real time basis to augment clinical and population decision support systems.

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