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Updated: Jun 14, 2026

Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management

Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management

Published on: November 30, 2022

Toward a fully de-identified biomedical information warehouse.

Jianhua Liu¹, Selnur Erdal, Scott A Silvey

¹The Ohio State University Medical Center, Columbus, Ohio, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|March 31, 2010

Summary

The Information Warehouse developed a de-identified data system to aid research and education. Performance evaluations of de-identification methods were conducted, addressing impacts on data integrity and regulatory compliance.

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Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management

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Published on: November 30, 2022

Biobanking of Human Aqueous and Vitreous Liquid Biopsies for Molecular Analyses

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Published on: September 11, 2023

Establishment of a Clinic-based Biorepository

Establishment of a Clinic-based Biorepository

Published on: May 29, 2017

Area of Science:

Biomedical Informatics
Health Data Management
Clinical Research Data

Background:

The Ohio State University Medical Center's Information Warehouse stores diverse clinical and research data.
Data use is regulated by federal privacy laws and Institutional Review Board (IRB) oversight.
The Information Warehouse acts as an "Honest Broker" for de-identified data.

Purpose of the Study:

To evaluate de-identification schemes for a new, directly accessible de-identified data warehouse.
To ensure regulatory compliance while facilitating data querying and updating.
To address the impact of date-shifting on other data elements.

Main Methods:

Performance evaluation of various de-identification techniques.
Analysis of the effects of date-shifting on diagnosis and procedure codes.
Development of solutions for data integrity issues caused by de-identification.

Main Results:

Findings on the performance of different de-identification schemes are reported.
The impact of date-shifting on data elements like diagnosis and procedure codes was analyzed.
Potential solutions for date-shifting challenges were considered.

Conclusions:

De-identification schemes require careful evaluation for research data usability.
Date-shifting can affect data integrity, necessitating specific mitigation strategies.
The developed de-identified data warehouse aims to support research and education effectively.