Free DICOM de-identification tools in clinical research:functioning and safety of patient privacy

To compare non-commercial DICOM toolkits for their de-identification ability in removing a patient's personal health information (PHI) from a DICOM header. Ten DICOM toolkits were selected for de-identification tests. Tests were performed by using the system's default de-identification profile and, subsequently, the tools' best adjusted settings. We aimed to eliminate fifty elements considered to contain identifying patient information. The tools were also examined for their respective methods of customization. Only one tool was able to de-identify all required elements with the default setting. Not all of the toolkits provide a customizable de-identification profile. Six tools allowed changes by selecting the provided profiles, giving input through a graphical user interface (GUI) or configuration text file, or providing the appropriate command-line arguments. Using adjusted settings, four of those six toolkits were able to perform full de-identification. Only five tools could properly de-identify the defined DICOM elements, and in four cases, only after careful customization. Therefore, free DICOM toolkits should be used with extreme care to prevent the risk of disclosing PHI, especially when using the default configuration. In case optimal security is required, one of the five toolkits is proposed. aEuro cent Free DICOM toolkits should be carefully used to prevent patient identity disclosure. aEuro cent Each DICOM tool produces its own specific outcomes from the de-identification process. aEuro cent In case optimal security is required, using one DICOM toolkit is proposed

Development and evaluation of a de-identification procedure for a case register sourced from mental health electronic records

Background: Electronic health records (EHRs) provide enormous potential for health research but also present data governance challenges. Ensuring de-identification is a pre-requisite for use of EHR data without prior consent. The South London and Maudsley NHS Trust (SLaM), one of the largest secondary mental healthcare providers in Europe, has developed, from its EHRs, a de-identified psychiatric case register, the Clinical Record Interactive Search (CRIS), for secondary research. Methods: We describe development, implementation and evaluation of a bespoke de-identification algorithm used to create the register. It is designed to create dictionaries using patient identifiers (PIs) entered into dedicated source fields and then identify, match and mask them (with ZZZZZ) when they appear in medical texts. We deemed this approach would be effective, given high coverage of PI in the dedicated fields and the effectiveness of the masking combined with elements of a security model. We conducted two separate performance tests i) to test performance of the algorithm in masking individual true PIs entered in dedicated fields and then found in text (using 500 patient notes) and ii) to compare the performance of the CRIS pattern matching algorithm with a machine learning algorithm, called the MITRE Identification Scrubber Toolkit – MIST (using 70 patient notes – 50 notes to train, 20 notes to test on). We also report any incidences of potential breaches, defined by occurrences of 3 or more true or apparent PIs in the same patient’s notes (and in an additional set of longitudinal notes for 50 patients); and we consider the possibility of inferring information despite de-identification. Results: True PIs were masked with 98.8% precision and 97.6% recall. As anticipated, potential PIs did appear, owing to misspellings entered within the EHRs. We found one potential breach. In a separate performance test, with a different set of notes, CRIS yielded 100% precision and 88.5% recall, while MIST yielded a 95.1% and 78.1%, respectively. We discuss how we overcome the realistic possibility – albeit of low probability – of potential breaches through implementation of the security model. Conclusion: CRIS is a de-identified psychiatric database sourced from EHRs, which protects patient anonymity and maximises data available for research. CRIS demonstrates the advantage of combining an effective de-identification algorithm with a carefully designed security model. The paper advances much needed discussion of EHR de-identification – particularly in relation to criteria to assess de-identification, and considering the contexts of de-identified research databases when assessing the risk of breaches of confidential patient information

Improving Medicaid Managed Care for Youth With Serious Behavioral Health Needs: A Quality Improvement Toolkit

Profiles successful initiatives by Medicaid managed care organizations in a collaboration to implement systems of care emphasizing early identification, coordination and management, and various services and supports in the least restrictive settings

Shaping the future for primary care education & training project. Education and training needs analysis (ETNA) toolkit: a resource kit and users’ guide

The Education and Training Needs Analysis (ETNA) Toolkit that has been developed as part of an inter university collaboration in the North West of England entitled the ‘Shaping the Future for Primary Care Education and Training’ project. The tool has been developed by the University of Bolton and Lancaster University in collaboration with key stakeholders including representatives from Primary Care Trusts and Social Services across the North Wes

STOP-IT: strategic, tactical, operational protection of water infrastructure against cyberphysical threats

Water supply and sanitation infrastructures are essential for our welfare, but vulnerable to several attack types facilitated by the ever-changing landscapes of the digital world. A cyber-attack on critical infrastructures could for example evolve along these threat vectors: chemical/biological contamination, physical or communications disruption between the network and the supervisory SCADA. Although conceptual and technological solutions to security and resilience are available, further work is required to bring them together in a risk management framework, strengthen the capacities of water utilities to systematically protect their systems, determine gaps in security technologies and improve risk management approaches. In particular, robust adaptable/flexible solutions for prevention, detection and mitigation of consequences in case of failure due to physical and cyber threats, their combination and cascading effects (from attacks to other critical infrastructure, i.e. energy) are still missing. There is (i) an urgent need to efficiently tackle cyber-physical security threats, (ii) an existing risk management gap in utilities’ practices and (iii) an un-tapped technology market potential for strategic, tactical and operational protection solutions for water infrastructure: how the H2020 STOP-IT project aims to bridge these gaps is presented in this paper.Postprint (published version

Protocol for a national monthly survey of alcohol use in England with 6-month follow-up: 'The Alcohol Toolkit Study'.

Timely tracking of national patterns of alcohol consumption is needed to inform and evaluate strategies and policies aimed at reducing alcohol-related harm. Between 2014 until at least 2017, the Alcohol Toolkit Study (ATS) will provide such tracking data and link these with policy changes and campaigns. By virtue of its connection with the 'Smoking Toolkit Study' (STS), links will also be examined between alcohol and smoking-related behaviour

Sharing Social Research Data in Ireland: A Practical Tool

Your data is valuable and has an importance outside your own original project. Allowing other researchers to reuse your data maximises the impact of your work, and benefits both the scholarly community and society in general. Sharing your data allows other researchers to use your material in ways you may not have thought of, or may not have been able to do within your research project. It allows other researchers to replicate your findings, to verify your results, test your instruments and compare with other studies. It also allows them to use your work to expand knowledge in important areas. It provides value for money by reducing duplication and advancing knowledge and also has a significant value in education, as it allows both graduate and under-graduate students to develop their skills in qualitative and quantitative research by using high-quality data in their studies, without having to conduct their own surveys.Archiving your data also guarantees its long-term preservation and accessibility. As many research teams are assembled only for individual projects, long-term preservation and access to research data collections can only be guaranteed if they are deposited in an archive which will manage them, ensure access and provide user-support. In addition, the archives will ensure that the datasets do not become obsolescent or corrupted.Finally, increasingly funders require that you make your research data available as a condition of their funding your research, so that other researchers can test your findings, and use your data to extend research in your area. Equally, publishers are also specifying access to research data as a condition for publication