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PRECISE:PRivacy-prEserving Cloud-assisted quality Improvement Service in hEalthcare
Feng Chen1, Shuang Wang2, Noman Mohammed3
1School of Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, 74135 USA.
IEEE International Conference on Systems Biology : [Proceedings]. IEEE International Conference on Systems Biology
|July 7, 2015
Summary
Healthcare providers can now compare quality improvement data across institutions securely. The PRECISE framework uses advanced cryptography to protect sensitive information, enabling collaborative healthcare quality enhancement without privacy risks.
Area of Science:
- Health Informatics
- Cryptography
- Healthcare Quality Improvement
Background:
- Quality improvement (QI) in healthcare necessitates systematic data comparison between institutions.
- Sharing institutional data for QI is often hindered by privacy concerns and potential financial repercussions.
- Existing methods lack robust privacy-preserving mechanisms for cross-institutional healthcare data analysis.
Purpose of the Study:
- To introduce a novel framework, PRivacy-prEserving Cloud-assisted quality Improvement Service in hEalthcare (PRECISE), for secure cross-institution healthcare statistics comparison.
- To enable healthcare providers to identify areas for improvement by benchmarking against peer institutions without compromising data privacy.
- To demonstrate the feasibility of a privacy-preserving cloud-assisted service for healthcare quality improvement.
Main Methods:
- Utilizing advanced cryptographic protocols, including homomorphic encryption and Yao's garbled circuit schemes.
- Developing a cloud-assisted service that securely pools encrypted data from multiple healthcare institutions.
- Implementing a system for ranking encrypted healthcare statistics to facilitate comparative analysis.
Main Results:
- Demonstrated the feasibility of the PRECISE framework through experimental validation using the MIMIC II database.
- Successfully enabled secure comparison of healthcare statistics across simulated institutions.
- Showcased the ability to rank encrypted data, facilitating QI insights.
Conclusions:
- The PRECISE framework effectively addresses privacy concerns in healthcare quality improvement initiatives.
- Cloud-assisted cryptographic methods can facilitate secure cross-institutional data analysis for QI.
- PRECISE offers a viable solution for enhancing healthcare quality through collaborative, privacy-preserving data sharing.
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