Advancing Practice, Instruction, and Innovation Through Informatics (APIII 2003): Scientific Session and E-Poster Abstracts

Archives of Pathology & Laboratory Medicine, Oct 2004 by Becich, Michael J, Crowley, Rebecca

3. Each institution takes a patient identifier (a name, a social security number, a birth date, or some combination of identifiers) and sums it with RandA B. The result is a patient random character string that is identical across institutions when the patient is identical in both institutions.

Optional Implementation: At this point, RandA B can be destroyed at both institutions and RandA and RandB can be destroyed by institutions A and B, respectively, leaving only the patient random character string at each institution. The destruction of these random numbers makes it impossible to recompute the original identifier from the patient random character string.

Optional Implementation: At this point, institutions may provide the patient random character string to a data broker. Having only the patient random character strings, the broker has zero patient-related information.

4. Institutions A and B compare their patient random character strings.

Optional Implementation: Institution A sends the first character of the patient random character string to Institution B. If the first character is not identical in both institutions, the protocol ends. The 2 patients are not the same person. If the first character is identical in both institutions, Institution B sends the second character. The process is repeated until a sufficient number of transactions have occurred to convince the institutions that they have the same patient random character string. This strategy ensures that the patient random character strings are never actually exchanged between institutions.

Results: The protocol can be implemented so that no information about the patient is transmitted across institutions. The protocol can be executed at high computational speed. A PERL script (zero.pl) compared the speed of creating de-identified information using the zero-check protocol and with the MD_5 1-way hash. There was no significant difference in computational speed. The PERL script is available at http://65.222.228.150/jjb/ zero, txt.

Conclusions: A zero-knowledge protocol for reconciling patients across institutions is described. This protocol is an example of a computational approach to data sharing, designed to help medical researchers comply with newly enacted US medical privacy regulations.

Extracting Genetic Conditions That Predispose to Cancer, Using the Online Mendelian Inheritance in Man

http://65.222.228.150/ijb/omimpre.txt

Jules J. Berman, MD, PhD (bermanj@mail.nih.gav). National Cancer Institute, National Institutes of Health, Bethesda, Md.

Context: A complete terminology of lesions/conditions related to cancer would contain (1) a comprehensive nomenclature of tumors, (2) a comprehensive nomenclature of precancers (morphologically identifiable lesions that precede the development of cancer), (3) a comprehensive nomenclature of acquired conditions that increase the risk of cancer (eg, acquired immunodeficiency syndrome and radiation exposure), and (4) a comprehensive nomenclature of genetic conditions that predispose to cancer (such as Li-Fraumeni syndrome and xeroderma pigmentosum). A complete cancer terminology is currently unavailable to researchers and pathologists. The purpose of such a nomenclature would be to facilitate the integration of biomedical data with lesions of interest to cancer researchers. Data integration enables researchers to discover the medical relevance of heterogeneous data elements. The author has published informatics techniques used to compile nomenclatures 1 and 2. This abstract describes a way of compiling nomenclature 4, using the Online Mendelian Inheritance in Man (OMIM).