Journal of Pathology Informatics Journal of Pathology Informatics
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TECHNICAL NOTE: SOFTWARE
Year : 2016  |  Volume : 7  |  Issue : 1  |  Page : 55

Generating unique IDs from patient identification data using security models


1 Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary; Department of Pathology and Laboratory Medicine, University of Calgary, Calgary Laboratory Services; Department of Family Medicine, University of Calgary, Calgary Laboratory Services, Calgary, AB, Canada
2 Department of Pathology and Laboratory Medicine, University of Calgary, Calgary Laboratory Services; Department of Family Medicine, University of Calgary, Calgary Laboratory Services, Calgary, AB, Canada

Correspondence Address:
Christopher T Naugler
Department of Pathology and Laboratory Medicine, University of Calgary, Calgary Laboratory Services; Department of Family Medicine, University of Calgary, Calgary Laboratory Services, Calgary, AB
Canada
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2153-3539.197203

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Background: The use of electronic health records (EHRs) has continued to increase within healthcare systems in the developed and developing nations. EHRs allow for increased patient safety, grant patients easier access to their medical records, and offer a wealth of data to researchers. However, various bioethical, financial, logistical, and information security considerations must be addressed while transitioning to an EHR system. The need to encrypt private patient information for data sharing is one of the foremost challenges faced by health information technology. Method: We describe the usage of the message digest-5 (MD5) and secure hashing algorithm (SHA) as methods for encrypting electronic medical data. In particular, we present an application of the MD5 and SHA-1 algorithms in encrypting a composite message from private patient information. Results: The results show that the composite message can be used to create a unique one-way encrypted ID per patient record that can be used for data sharing. Conclusion: The described software tool can be used to share patient EMRs between practitioners without revealing patients identifiable data.


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