Scottish Medical Imaging Service:Technical and Governance controls

Objectives The Scottish Medical Imaging (SMI) service provides linkable, population based, “research-ready” real-world medical images for researchers to develop or validate AI algorithms within the Scottish National Safe Haven. The PICTURES research programme is developing novel methods to enhance the SMI service offering through research in cybersecurity and software/data/infrastructure engineering. Approach Additional technical and governance controls were required to enable safe access to medical images. The researcher is isolated from the rest of the trusted research environment (TRE) using a Project Private Zone (PPZ). This enables researchers to build and install their own software stack, and protects the TRE from malicious code. Guidelines are under development for researchers on the safe development of algorithms and the expected relationship between the size of the model and the training dataset. There is associated work on the statistical disclosure control of models to enable safe release of trained models from the TRE. Results A policy enabling the use of “Non-standard software” based on prior research, domain knowledge and experience gained from two contrasting research studies was developed.  Additional clauses have been added to the legal control – the eDRIS User Agreement – signed by each researcher and their Head of Department.  Penalties for attempting to import or use malware, remove data within models or any attempt to deceive or circumvent such controls are severe, and apply to both the individual and their institution. The process of building and deploying a PPZ has been developed allowing researchers to install their own software. No attempt has yet been made to add additional ethical controls; however, a future service development could be validating the performance of researchers’ algorithms on our training dataset. Conclusion The availability to conduct research using images poses new challenges and risks for those commissioning and operating TREs. The Private Project Zone and our associated governance controls are a huge step towards supporting the needs of researchers in the 21st century

Risks of mining to salmonid-bearing watersheds

Mining provides resources for people but can pose risks to ecosystems that support cultural keystone species. Our synthesis reviews relevant aspects of mining operations, describes the ecology of salmonid-bearing watersheds in northwestern North America, and compiles the impacts of metal and coal extraction on salmonids and their habitat. We conservatively estimate that this region encompasses nearly 4000 past producing mines, with present-day operations ranging from small placer sites to massive open-pit projects that annually mine more than 118 million metric tons of earth. Despite impact assessments that are intended to evaluate risk and inform mitigation, mines continue to harm salmonid-bearing watersheds via pathways such as toxic contaminants, stream channel burial, and flow regime alteration. To better maintain watershed processes that benefit salmonids, we highlight key windows during the mining governance life cycle for science to guide policy by more accurately accounting for stressor complexity, cumulative effects, and future environmental change.This review is based on an October 2019 workshop held at the University of Montana Flathead Lake Biological Station (more information at https://flbs.umt.edu/ newflbs/research/working-groups/mining-and-watersheds/). We thank E. O’Neill and other participants for valuable contributions. A. Beaudreau, M. LaCroix, P. McGrath, K. Schofield, and L. Brown provided helpful reviews of earlier drafts. Three anonymous reviewers provided thoughtful critiques that greatly improved the manuscript. The views expressed in this article are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency. Our analysis comes from a western science perspective and hence does not incorporate Indigenous knowledge systems. We acknowledge this gap and highlight that the lands and waters we explore in this review have been stewarded by Indigenous Peoples for millennia and continue to be so. Funding: The workshop was cooperatively funded by the Wilburforce Foundation and The Salmon Science Network funded by the Gordon and Betty Moore Foundation. Author contributions: C.J.S. led the review process, writing, and editing. C.J.S. and E.K.S. co-organized the workshop. E.K.S. and J.W.M. extensively contributed to all aspects of the review conceptualization, writing, and editing. A.R.W., S.A.N., J.L.E., D.M.C., S.L.O., R.L.M., F.R.H., D.C.W., and J.W. significantly contributed to portions of the review conceptualization, writing, and editing. J.C., M.Ca., M.Co., C.A.F., G.K., E.D.L., R.M., V.M., J.K.M., M.V.M., and N.S. provided writing and editing and are listed alphabetically. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials.Ye

Assessment of the interactions of metabolically stressed macrophages with Escherichia coli (strain HB101)

Bibliography: p. 120-126Some pages are in colour

Application of natural language processing methods to extract coded data from administrative data held in the Scottish Prescribing Information System

ABSTRACT Objectives The Prescribing Information System (PIS) datamart, hosted by NHS National Services Scotland receives around 90 million electronic prescription messages per year from GP practices across Scotland. Prescription messages contain information including drug name, quantity and strength stored as coded, machine readable, data while prescription dose instructions are unstructured free text and difficult to interpret and analyse in volume. The aim, using Natural Language Processing (NLP), was to extract drug dose amount, unit and frequency metadata from freely typed text in dose instructions to support calculating the intended number of days’ treatment. This then allows comparison with actual prescription frequency, treatment adherence and the impact upon prescribing safety and effectiveness. Approach An NLP algorithm was developed using the Ciao implementation of Prolog to extract dose amount, unit and frequency metadata from dose instructions held in the PIS datamart for drugs used in the treatment of gastrointestinal, cardiovascular and respiratory disease. Accuracy estimates were obtained by randomly sampling 0.1% of the distinct dose instructions from source records, comparing these with metadata extracted by the algorithm and an iterative approach was used to modify the algorithm to increase accuracy and coverage. Results The NLP algorithm was applied to 39,943,465 prescription instructions issued in 2014, consisting of 575,340 distinct dose instructions. For drugs used in the gastrointestinal, cardiovascular and respiratory systems (i.e. chapters 1, 2 and 3 of the British National Formulary (BNF)) the NLP algorithm successfully extracted drug dose amount, unit and frequency metadata from 95.1%, 98.5% and 97.4% of prescriptions respectively. However, instructions containing terms such as ‘as directed’ or ‘as required’ reduce the usability of the metadata by making it difficult to calculate the total dose intended for a specific time period as 7.9%, 0.9% and 27.9% of dose instructions contained terms meaning ‘as required’ while 3.2%, 3.7% and 4.0% contained terms meaning ‘as directed’, for drugs used in BNF chapters 1, 2 and 3 respectively. Conclusion The NLP algorithm developed can extract dose, unit and frequency metadata from text found in prescriptions issued to treat a wide range of conditions and this information may be used to support calculating treatment durations, medicines adherence and cumulative drug exposure. The presence of terms such as ‘as required’ and ‘as directed’ has a negative impact on the usability of the metadata and further work is required to determine the level of impact this has on calculating treatment durations and cumulative drug exposure

Educating the Future Educators: the quest for professionalism in early childhood education

This article examines the implications of recent and proposed changes to the college-based training of practitioners in early childhood education (ECE). These changes will change the length of courses by shortening them and, therefore, the depth of teaching that students will experience on a college-based course. The links between level of education and improved outcomes for children are discussed. This colloquium explores how these changes may impact on the professionalization of the early childhood workforce. Definitions of professionalism found in the early childhood literature and the notion of the professional ECE student are explored. The current barriers to viewing ECE as a profession and the ongoing professionalism of the field are discussed, as are possible solutions to removing the barriers

SACRO:Semi-Automated Checking of Research Outputs

This project aimed to address a major bottleneck in conducting research on confidential data - the final stage of "Output Statistical Disclosure Control" (OSDC). This is where staff in a Trusted Research Environment (TRE) conduct manual checks to ensure that things a researcher wishes to take out - such as tables, plots, statistical and/or AI models- do not cause risk to any individual's privacy. To tackle this bottleneck, we proposed to:Produce a consolidated framework with a rigorous statistical basis that provides guidance for TREs to agree consistent, standard processes to assist in Quality Assurance.Design and implement a semi-automated system for checks on common research outputs, with increasing levels of support for other types such as AI.Work with a range of different types of TRE in different sectors and organisations to ensure wide applicability.Work with public and patients to explore what is needed for public trust, e.g., that any automation is acting as "an extra pair of eyes": supporting not supplanting TRE staff.Supported by funding from DARE UK (Data and Analytics Research Environments UK), we met these aims through production of documentation, open-source code repositories, and a 'Consensus' statement embodying principles organisations should uphold when deploying any sort of automated disclosure control.Looking forward, we are now ready for extensive user testing and refinement of the resources produced. Following a series of presentations to national and international audiences, a range of different organisations arein the process of trialling the SACRO toolkits. We are delighted that DARE UK has awarded funding to support a Community of Interest group (CoI). This will address ongoing support and the user-led creation of 'soft' resources (such as user guides, 'help desks', and mentoring schemes) to remove blocks to adoption: both for TREs, and crucially for researchers.There are two other areas where we are now ready to make significant advances: applying SACRO to allow principles-based OSDC for 'conceptual data spaces (e.g. via data pooling or federated analytics) and expanding the scope of risk assessment of AI/Machine Learning models to more complex models and types of data. This work is funded by UK research and Innovation, [Grant Number MC_PC_23006], as part of Phase 1 of the DARE UK (Data and Analytics Research Environments UK) programme, delivered in partnership with Health Data Research UK (HDR UK) and Administrative Data Research UK (ADR UK

SACRO:Semi-Automated Checking of Research Outputs

This project aimed to address a major bottleneck in conducting research on confidential data - the final stage of "Output Statistical Disclosure Control" (OSDC). This is where staff in a Trusted Research Environment (TRE) conduct manual checks to ensure that things a researcher wishes to take out - such as tables, plots, statistical and/or AI models- do not cause risk to any individual's privacy. To tackle this bottleneck, we proposed to:Produce a consolidated framework with a rigorous statistical basis that provides guidance for TREs to agree consistent, standard processes to assist in Quality Assurance.Design and implement a semi-automated system for checks on common research outputs, with increasing levels of support for other types such as AI.Work with a range of different types of TRE in different sectors and organisations to ensure wide applicability.Work with public and patients to explore what is needed for public trust, e.g., that any automation is acting as "an extra pair of eyes": supporting not supplanting TRE staff.Supported by funding from DARE UK (Data and Analytics Research Environments UK), we met these aims through production of documentation, open-source code repositories, and a 'Consensus' statement embodying principles organisations should uphold when deploying any sort of automated disclosure control.Looking forward, we are now ready for extensive user testing and refinement of the resources produced. Following a series of presentations to national and international audiences, a range of different organisations arein the process of trialling the SACRO toolkits. We are delighted that DARE UK has awarded funding to support a Community of Interest group (CoI). This will address ongoing support and the user-led creation of 'soft' resources (such as user guides, 'help desks', and mentoring schemes) to remove blocks to adoption: both for TREs, and crucially for researchers.There are two other areas where we are now ready to make significant advances: applying SACRO to allow principles-based OSDC for 'conceptual data spaces (e.g. via data pooling or federated analytics) and expanding the scope of risk assessment of AI/Machine Learning models to more complex models and types of data. This work is funded by UK research and Innovation, [Grant Number MC_PC_23006], as part of Phase 1 of the DARE UK (Data and Analytics Research Environments UK) programme, delivered in partnership with Health Data Research UK (HDR UK) and Administrative Data Research UK (ADR UK