CliniDeID® automatically de-identifies clinical notes according to the HIPAA Safe Harbor method. It accurately finds identifiers and tags or replaces them with realistic surrogates for better anonymity. It improves access to richer, more detailed, and more accurate clinical data for clinical researchers. It eases research data sharing, and helps healthcare organizations protect patient data confidentiality.
Concerned about exposing patients to privacy breaches? About HIPAA violations? Trying to share clinical data for research?
- Increase patient trust through the ethical protection of confidentiality.
- Magnify the impact of clinical data for research by providing the ability to reuse data without patient informed consent.
- Greatly reduce financial risks by avoiding fines and other penalties that could otherwise arise from a leak of patient data.
- Save money by reducing the cost and increasing the efficiency of clinical data de-identification.
- Expand and scale existing research opportunities by providing richer, more detailed clinical data.
- Uncover and facilitate new research opportunities through shareable, de-identified clinical data as well as provide larger, NIH-sponsored research with required data sharing capabilities.
- “Future-proof” clinical data for unforeseen research opportunities by allowing records to be accurately linked even after de-identification.
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Clinical Text Automatic De-Identification to Support Large Scale Data Reuse and Sharing: Pilot Results
Presented at 2018 AMIA Symposium
Ensemble-based Methods to Improve De-identification of Electronic Health Record Narratives
Presented at 2018 AMIA Symposium
Voting Ensemble Pruning for De-identification of Electronic Health Records
Presented at 2019 AMIA Summits
A Comparative Analysis of Speed and Accuracy for Three Off-the-Shelf De-Identification Tools
Presented at 2020 AMIA Summits
The adoption of Electronic Health Record (EHR) systems is growing at a fast pace in the U.S., and this growth results in very large quantities of patient clinical data 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 fulfill the potentials for high quality healthcare, improved healthcare management, and effective clinical research.
NIH expects that larger research projects share their research data in a way that protects the confidentiality of the research subjects. 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, 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.
Clinacuity, Inc. proposes a new system to automatically de-identify clinical notes found in the EHR, to then improve the availability of clinical text for secondary uses, as well as ameliorate the protection of patient data confidentiality: CliniDeID®
To strengthen patient information confidentiality protection, the HITECH Act heightened financial penalties incurred for breaches of PHI, even introducing criminal penalties. These new severe consequences for violation of patient information confidentiality render protection requirements even more obvious, and automatic high-accuracy clinical text de-identification, as offered by the system Clinacuity proposes, will strongly help healthcare and clinical research organizations avoid such consequences. CliniDeID improves access to richer, more detailed, and more accurate clinical data (in clinical text) for clinical researchers. It also eases research data sharing, and helps healthcare organizations protect patient data confidentiality.
Development of CliniDeID is funded by the National Institute of General Medical Sciences (NIGMS).