Gregory Jones

Equity Interest - Non-publicly traded entity ( e.g., stock, stock option, or other ownership interest)

Clinical Text Automatic De-Identification to Support Large Scale Data Reuse and Sharing

The adoption of Electronic Health Record systems is growing at a fast pace in the U.S., and this growth results in very large quantities of patient clinical daa becoming available in electronic format, with tremendous potentials, but also equally growing concern for patient confidentiality breaches. Secondary use of clinical data is essential to fulfil the potentials for high quality healthcare and effective clinical research. De-identification of patient data has been proposed as a solution to both facilitate secondary uses of clinical data, and protect patient data confidentiality. The majority of clinical data found in the EHR is represented as narrative text clinical notes that have been dictated and transcribed or directly typed in, and de-identification of clinical text is a tedious and costly manual endeavor. Automated approaches based on Natural Language Processing have been implemented and evaluated, allowing for much faster de-identification than manual approaches. The overall goal of this project is to develop a new system to automatically de-identify clinical narrative text in he Electronic Health Record, to then improve the availability of clinical text for secondary uses, as well as ameliorate the protection of patient data confidentiality.