Gender bias in academic medicine: a resumé study

BACKGROUND

Minimising the effects of unconscious bias in selection for clinical academic training is essential to ensure that allocation of training posts is based on merit. We looked at the effect of anonymising applications to a training programme for junior doctors on the scores of the applications and on gender balance; and whether female candidates were more likely to seek gender-concordant mentors.

METHODS

Applications to the training programme were reviewed and scored independently by reviewers who received either an anonymised or named copy. Scores were compared using a paired t-test, and differences in scores compared by gender. The gender of named supervisors for male and female candidates was compared.

RESULTS

Scores of 101 applications were reviewed. When their identity was known, male candidates scored 1.72% higher and female candidates scored 0.74% higher, but these findings were not statistically significant (p value = 0.279 and 0.579). Following introduction of anonymisation, the proportion of successful female candidates increased from 27 to 46%. Female candidates were more likely to name a female supervisor compared to male (41% vs. 25% of supervisors).

CONCLUSIONS

Anonymising applications did not significantly change scores, although gender balance improved. Gender-concordant mentoring initiatives should consider effects on mentors as well as mentees.

Application of a convolutional neural network to the quality control of MRI defacing

Large-scale neuroimaging datasets present unique challenges for automated processing pipelines. Motivated by a large clinical trials dataset with over 235,000 MRI scans, we consider the challenge of defacing - anonymisation to remove identifying facial features. The defacing process must undergo quality control (QC) checks to ensure that the facial features have been removed and that the brain tissue is left intact. Visual QC checks are time-consuming and can cause delays in preparing data. We have developed a convolutional neural network (CNN) that can assist with the QC of the application of MRI defacing; our CNN is able to distinguish between scans that are correctly defaced and can classify defacing failures into three sub-types to facilitate parameter tuning during remedial re-defacing. Since integrating the CNN into our anonymisation pipeline, over 75,000 scans have been processed. Strict thresholds have been applied so that ambiguous classifications are referred for visual QC checks, however all scans still undergo an efficient verification check before being marked as passed. After applying the thresholds, our network is 92% accurate and can classify nearly half of the scans without the need for protracted manual checks. Our model can generalise across MRI modalities and has comparable performance when tested on an independent dataset. Even with the introduction of the verification checks, incorporation of the CNN has reduced the time spent undertaking QC checks by 42% during initial defacing, and by 35% overall. With the help of the CNN, we have been able to successfully deface 96% of the scans in the project whilst maintaining high QC standards. In a similarly sized new project, we would expect the model to reduce the time spent on manual QC checks by 125 h. Our approach is applicable to other projects with the potential to greatly improve the efficiency of imaging anonymisation pipelines.

Sharing individualised template MRI data for MEG source reconstruction: A solution for open data while keeping subject confidentiality

The increasing requirements for adoption of FAIR data management and sharing original research data from neuroimaging studies can be at odds with protecting the anonymity of the research participants due to the person-identifiable anatomical features in the data. We propose a solution to this dilemma for anatomical MRIs used in MEG source analysis. In MEG analysis, the channel-level data is reconstructed to the source-level using models derived from anatomical MRIs. Sharing data, therefore, requires sharing the anatomical MRI to replicate the analysis. The suggested solution is to replace the individual anatomical MRIs with individualised warped templates that can be used to carry out the MEG source analysis and that provide sufficient geometrical similarity to the original participants' MRIs. First, we demonstrate how the individualised template warping can be implemented with one of the leading open-source neuroimaging analysis toolboxes. Second, we compare results from four different MEG source reconstruction methods performed with an individualised warped template to those using the participant's original MRI. While the source reconstruction results are not numerically identical, there is a high similarity between the results for single dipole fits, dynamic imaging of coherent sources beamforming, and atlas-based virtual channel beamforming. There is a moderate similarity between minimum-norm estimates, as anticipated due to this method being anatomically constrained and dependent on the exact morphological features of the cortical sheet. We also compared the morphological features of the warped template to those of the original MRI. These showed a high similarity in grey matter volume and surface area, but a low similarity in the average cortical thickness and the mean folding index within cortical parcels. Taken together, this demonstrates that the results obtained by MEG source reconstruction can be preserved with the warped templates, whereas the anatomical and morphological fingerprint is sufficiently altered to protect the anonymity of research participants. In cases where participants consent to sharing anatomical MRI data, it remains preferable to share the original defaced data with an appropriate data use agreement. In cases where participants did not consent to share their MRIs, the individualised warped MRI template offers a good compromise in sharing data for reuse while retaining anonymity for research participants.

From 'Consent or Anonymise' to 'Share and Protect': Facilitating Access to Surplus Tissue for Research Whilst Safeguarding Donor Interests

There is significant research value in the secondary use of surplus human tissue which has been removed during clinical care and is stored in diagnostic archives. However, this value is limited without access to information about the person from whom the tissue was removed. As the research value of surplus tissue is often not realised until after the patient’s episode of care, it is often the case that no consent has been given for any surplus tissue to be used for research purposes. The Human Tissue Act 2004 does permit research use of surplus tissue without consent, but the researcher must not be in possession of information which could identify the person from whom the tissue was removed. Due to the commonly applied ‘consent or anonymise’ approach, linking tissue and data is challenging and full anonymisation would likely render much research on surplus tissue ineffectual. This article suggests that in recognising the value in surplus tissue linked with information about the person, a ‘share and protect’ approach which considers safeguards other than anonymisation, where obtaining consent for research use would not be feasible, would better balance the public benefit of health research with the protection of individual rights and interests than a requirement for either consent or anonymisation.

Use of visual media in the era of European Union's General Data Protection Regulation: A practice-oriented guideline

With the European Union's new General Data Protection Regulation, commonly known as 'GDPR', as the new framework for data protection across the European Union (EU), doctors will need to review how they collect and share personal data to ensure they meet the standards. The aim of this article is to raise awareness on the GDPR, and to provide an easy guideline to steer free from legal problems at the time of drafting papers, presenting lectures and sharing personal data and visual media in particular. To do so, we have analysed the most common situations where personal data, and above all visual media, can be collected, giving clear-cut answers and recommendations for all the scenarios.

Use of visual media in the era of European Union's General Data Protection Regulation: A practice-oriented guideline

With the European Union's new General Data Protection Regulation, commonly known as "GDPR", as the new framework for data protection across the European Union, doctors will need to review how they collect and share personal data to ensure they meet the standards. The aim of this article is to raise awareness on the General Data Protection Regulation, and to provide an easy guideline to steer free from legal problems at the time of drafting papers, presenting lectures and sharing personal data and visual media in particular. To do so, we have analysed the most common situations where personal data, and above all visual media, can be collected, giving clear-cut answers and recommendations for all the scenarios.

Data sharing in clinical trials - practical guidance on anonymising trial datasets

Background

There is an increasing demand by non-commercial funders that trialists should provide access to trial data once the primary analysis is completed. This has to take into account concerns about identifying individual trial participants, and the legal and regulatory requirements.

Methods

Using the good practice guideline laid out by the work funded by the Medical Research Council Hubs for Trials Methodology Research (MRC HTMR), we anonymised a dataset from a recently completed trial. Using this example, we present practical guidance on how to anonymise a dataset, and describe rules that could be used on other trial datasets. We describe how these might differ if the trial was to be made freely available to all, or if the data could only be accessed with specific permission and data usage agreements in place.

Results

Following the good practice guidelines, we successfully created a controlled access model for trial data sharing. The data were assessed on a case-by-case basis classifying variables as direct, indirect and superfluous identifiers with differing methods of anonymisation assigned depending on the type of identifier. A final dataset was created and checks of the anonymised dataset were applied. Lastly, a procedure for release of the data was implemented to complete the process.

Conclusions

We have implemented a practical solution to the data anonymisation process resulting in a bespoke anonymised dataset for a recently completed trial. We have gained useful learnings in terms of efficiency of the process going forward, the need to balance anonymity with data utilisation and future work that should be undertaken.

Electronic supplementary material

The online version of this article (doi:10.1186/s13063-017-2382-9) contains supplementary material, which is available to authorized users.

Resource implications of preparing individual participant data from a clinical trial to share with external researchers

BACKGROUND

Demands are increasingly being made for clinical trialists to actively share individual participant data (IPD) collected from clinical trials using responsible methods that protect the confidentiality and privacy of clinical trial participants. Clinical trialists, particularly those receiving public funding, are often concerned about the additional time and money that data-sharing activities will require, but few published empirical data are available to help inform these decisions. We sought to evaluate the activity and resources required to prepare anonymised IPD from a clinical trial in anticipation of a future data-sharing request.

METHODS

Data from two UK publicly funded clinical trials were used for this exercise: 2437 participants with epilepsy recruited from 90 hospital outpatient clinics in the SANAD trial and 146 children with neuro-developmental problems recruited from 18 hospitals in the MENDS trial. We calculated the time and resources required to prepare each anonymised dataset and assemble a data pack ready for sharing.

RESULTS

The older SANAD trial (published 2007) required 50 hours of staff time with a total estimated associated cost of £3185 whilst the more recently completed MENDS trial (published 2012) required 39.5 hours of staff time with total estimated associated cost of £2540.

CONCLUSIONS

Clinical trial researchers, funders and sponsors should consider appropriate resourcing and allow reasonable time for preparing IPD ready for subsequent sharing. This process would be most efficient if prospectively built into the standard operational design and conduct of a clinical trial. Further empirical examples exploring the resource requirements in other settings is recommended.

TRIAL REGISTRATION

SANAD: International Standard Randomised Controlled Trials Registry: ISRCTN38354748 . Registered on 25 April 2003.

MENDS

EU Clinical Trials Register Eudract 2006-004025-28 . Registered on 16 May 2007. International Standard Randomised Controlled Trials Registry: ISRCTN05534585 /MREC 07/MRE08/43. Registered on 26 January 2007.

Clinical records anonymisation and text extraction (CRATE): an open-source software system

Background

Electronic medical records contain information of value for research, but contain identifiable and often highly sensitive confidential information. Patient-identifiable information cannot in general be shared outside clinical care teams without explicit consent, but anonymisation/de-identification allows research uses of clinical data without explicit consent.

Results

This article presents CRATE (Clinical Records Anonymisation and Text Extraction), an open-source software system with separable functions: (1) it anonymises or de-identifies arbitrary relational databases, with sensitivity and precision similar to previous comparable systems; (2) it uses public secure cryptographic methods to map patient identifiers to research identifiers (pseudonyms); (3) it connects relational databases to external tools for natural language processing; (4) it provides a web front end for research and administrative functions; and (5) it supports a specific model through which patients may consent to be contacted about research.

Conclusions

Creation and management of a research database from sensitive clinical records with secure pseudonym generation, full-text indexing, and a consent-to-contact process is possible and practical using entirely free and open-source software.

GIFT-Cloud: A data sharing and collaboration platform for medical imaging research

OBJECTIVES

Clinical imaging data are essential for developing research software for computer-aided diagnosis, treatment planning and image-guided surgery, yet existing systems are poorly suited for data sharing between healthcare and academia: research systems rarely provide an integrated approach for data exchange with clinicians; hospital systems are focused towards clinical patient care with limited access for external researchers; and safe haven environments are not well suited to algorithm development. We have established GIFT-Cloud, a data and medical image sharing platform, to meet the needs of GIFT-Surg, an international research collaboration that is developing novel imaging methods for fetal surgery. GIFT-Cloud also has general applicability to other areas of imaging research.

METHODS

GIFT-Cloud builds upon well-established cross-platform technologies. The Server provides secure anonymised data storage, direct web-based data access and a REST API for integrating external software. The Uploader provides automated on-site anonymisation, encryption and data upload. Gateways provide a seamless process for uploading medical data from clinical systems to the research server.

RESULTS

GIFT-Cloud has been implemented in a multi-centre study for fetal medicine research. We present a case study of placental segmentation for pre-operative surgical planning, showing how GIFT-Cloud underpins the research and integrates with the clinical workflow.

CONCLUSIONS

GIFT-Cloud simplifies the transfer of imaging data from clinical to research institutions, facilitating the development and validation of medical research software and the sharing of results back to the clinical partners. GIFT-Cloud supports collaboration between multiple healthcare and research institutions while satisfying the demands of patient confidentiality, data security and data ownership.

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

BACKGROUND

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. 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.

DISCUSSION

Our key recommendations are as follows: 1. 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. 2. 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. This 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.

A standardised graphic method for describing data privacy frameworks in primary care research using a flexible zone model

PURPOSE

To develop a model describing core concepts and principles of data flow, data privacy and confidentiality, in a simple and flexible way, using concise process descriptions and a diagrammatic notation applied to research workflow processes. The model should help to generate robust data privacy frameworks for research done with patient data.

METHODS

Based on an exploration of EU legal requirements for data protection and privacy, data access policies, and existing privacy frameworks of research projects, basic concepts and common processes were extracted, described and incorporated into a model with a formal graphical representation and a standardised notation. The Unified Modelling Language (UML) notation was enriched by workflow and own symbols to enable the representation of extended data flow requirements, data privacy and data security requirements, privacy enhancing techniques (PET) and to allow privacy threat analysis for research scenarios.

RESULTS

Our model is built upon the concept of three privacy zones (Care Zone, Non-care Zone and Research Zone) containing databases, data transformation operators, such as data linkers and privacy filters. Using these model components, a risk gradient for moving data from a zone of high risk for patient identification to a zone of low risk can be described. The model was applied to the analysis of data flows in several general clinical research use cases and two research scenarios from the TRANSFoRm project (e.g., finding patients for clinical research and linkage of databases). The model was validated by representing research done with the NIVEL Primary Care Database in the Netherlands.

CONCLUSIONS

The model allows analysis of data privacy and confidentiality issues for research with patient data in a structured way and provides a framework to specify a privacy compliant data flow, to communicate privacy requirements and to identify weak points for an adequate implementation of data privacy.