The Risks to Patient Privacy from Publishing Data from Clinical Anesthesia Studies

Liam O'Neill; Franklin Dexter; Nan Zhang

doi:10.1213/ANE.0000000000001331

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The Risks to Patient Privacy from Publishing Data from Clinical Anesthesia Studies

Journal article

Peer reviewed

The Risks to Patient Privacy from Publishing Data from Clinical Anesthesia Studies

Liam O'Neill, Franklin Dexter and Nan Zhang

Anesthesia and analgesia, Vol.122(6), pp.2017-2027

06/2016

DOI: 10.1213/ANE.0000000000001331

PMID: 27172145

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Abstract

In this article, we consider the privacy implications of posting data from small, randomized trials, observational studies, or case series in anesthesia from a few (e.g., 1-3) hospitals. Prior to publishing such data as supplemental digital content, the authors remove attributes that could be used to re-identify individuals, a process known as "anonymization." Posting health information that has been properly "de-identified" is assumed to pose no risks to patient privacy. Yet, computer scientists have demonstrated that this assumption is flawed. We consider various realistic scenarios of how the publication of such data could lead to breaches of patient privacy. Several examples of successful privacy attacks are reviewed, as well as the methods used. We survey the latest models and methods from computer science for protecting health information and their application to posting data from small anesthesia studies. To illustrate the vulnerability of such published data, we calculate the "population uniqueness" for patients undergoing one or more surgical procedures using data from the State of Texas. For a patient selected uniformly at random, the probability that an adversary could match this patient's record to a unique record in the state external database was 42.8% (SE < 0.1%). Despite the 42.8% being an unacceptably high level of risk, it underestimates the risk for patients from smaller states or provinces. We propose an editorial policy that greatly reduces the likelihood of a privacy breach, while supporting the goal of transparency of the research process.

United States

Data Mining

Computer Security

Randomized Controlled Trials as Topic - methods

Ethics Committees, Research

Humans

Data Anonymization

Randomized Controlled Trials as Topic - legislation & jurisprudence

Information Dissemination - legislation & jurisprudence

Personally Identifiable Information

Inpatients

Research Design - legislation & jurisprudence

Observational Studies as Topic - legislation & jurisprudence

Observational Studies as Topic - methods

Texas

Editorial Policies

Confidentiality - legislation & jurisprudence

Health Insurance Portability and Accountability Act

Periodicals as Topic - legislation & jurisprudence

Databases, Factual

Details

Title: Subtitle: The Risks to Patient Privacy from Publishing Data from Clinical Anesthesia Studies
Creators: Liam O'Neill - University of North Texas Health Science Center
Franklin Dexter
Nan Zhang
Resource Type: Journal article
Publication Details: Anesthesia and analgesia, Vol.122(6), pp.2017-2027
Publisher: United States
DOI: 10.1213/ANE.0000000000001331
PMID: 27172145
ISSN: 0003-2999
eISSN: 1526-7598
Language: English
Date published: 06/2016
Academic Unit: Health Management and Policy; Anesthesia
Record Identifier: 9983806397802771

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25 Times Cited - Web of Science

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