Protecting patient privacy when sharing patient-level data from clinical trials.

Katherine Tucker,Paul Loughlin,Sally Hollis,Mark J Nixon,Maria Dilleen,Zoë Williams,Janice Branson

doi:10.1186/s12874-016-0169-4

Abstract

BackgroundGreater transparency and, in particular, sharing of patient-level data for further scientific research is an increasingly important topic for the pharmaceutical industry and other organisations who sponsor and conduct clinical trials as well as generally in the interests of patients participating in studies. A concern remains, however, over how to appropriately prepare and share clinical trial data with third party researchers, whilst maintaining patient confidentiality. Clinical trial datasets contain very detailed information on each participant. Risk to patient privacy can be mitigated by data reduction techniques. However, retention of data utility is important in order to allow meaningful scientific research. In addition, for clinical trial data, an excessive application of such techniques may pose a public health risk if misleading results are produced. After considering existing guidance, this article makes recommendations with the aim of promoting an approach that balances data utility and privacy risk and is applicable across clinical trial data holders.DiscussionOur key recommendations are as follows:Data anonymisation/de-identification: Data holders are responsible for generating de-identified datasets which are intended to offer increased protection for patient privacy through masking or generalisation of direct and some indirect identifiers.Controlled access to data, including use of a data sharing agreement: A legally binding data sharing agreement should be in place, including agreements not to download or further share data and not to attempt to seek to identify patients. Appropriate levels of security should be used for transferring data or providing access; one solution is use of a secure ‘locked box’ system which provides additional safeguards.SummaryThis article provides recommendations on best practices to de-identify/anonymise clinical trial data for sharing with third-party researchers, as well as controlled access to data and data sharing agreements. The recommendations are applicable to all clinical trial data holders. Further work will be needed to identify and evaluate competing possibilities as regulations, attitudes to risk and technologies evolve.

Highlights

Greater transparency and, in particular, sharing of patient-level data for further scientific research is an increasingly important topic for the pharmaceutical industry and other organisations who sponsor and conduct clinical trials as well as generally in the interests of patients participating in studies
Potential indirect identifiers which are important for data utility may be retained and could be recoded/grouped, otherwise they should be removed
Clinical trials should be planned and executed with eventual data sharing aims built in, in line with the Institute of Medicine [7] recommendation that ‘Stakeholders in clinical trials should foster a culture in which data sharing is the expected norm’, and the International Committee of Medical Journal Editors (ICMJE) proposal to require authors to share with others de-identified individual-patient data underlying the results presented in articles reporting clinical trials [34]

Summary

Discussion

This section outlines key recommendations in two areas: anonymisation/de-identification of patient-level data for third-party research and controlled access to data, including use of a DSA. The guidance is applicable to any holder of clinical trial data, with the aim of promoting an approach that balances data utility and privacy risk and is applicable across data holders In creating these recommendations, we considered existing legislation, guidance and common practices relevant to protecting patient privacy; the context in which data holders share data (e.g. approved research proposals, legal and data security controls); practical considerations related to the ability to efficiently and effectively prepare and deliver large volumes of data requests in a semiautomated fashion; ability to align/standardise processes across data holders; cost and resource implications, and how to maximise data utility and the integrity of resulting analyses and interpretation.

Background

Health plan beneficiary numbers