Electronic health record standards

Objectives: This paper seeks to provide an overview of the initiatives that are proceeding internationally to develop standards for the exchange of electronic health record (EHR) information between EHR systems.Methods: The paper reviews the clinical and ethico-legal requirements and research background on the representation and communication of EHR data, which primarily originates from Europe through a series of EU funded Health Telematics projects over the post thirteen years. The major concept that underpin the information models and knowledge models are summarised. These provide the requirements and the best evidential basis from which HER communications standards should be developed.Results. The main focus of EHR communications standardisation is presently occurring at a European level, through the Committee for European Normalisation (CEN). The major constructs of the CEN 13606 model ate outlined. Complementary activity is taking place in ISO and in HL7, and some of these efforts are also summarised.Conclusior: There is a strong prospect that a generic EHR interoperability standard can be agreed at a European (and hopefully international) level. Parts of the challenge of EHR i interoperability cannot yet he standardised, because good solutions to the preservation of clinical meaning across heterogeneous systems remain to be explored. Further research and empirical projects are therefore also needed

Summarisation and visualisation of e-Health data repositories

At the centre of the Clinical e-Science Framework (CLEF) project is a repository of well organised, detailed clinical histories, encoded as data that will be available for use in clinical care and in-silico medical experiments. We describe a system that we have developed as part of the CLEF project, to perform the task of generating a diverse range of textual and graphical summaries of a patient’s clinical history from a data-encoded model, a chronicle, representing the record of the patient’s medical history. Although the focus of our current work is on cancer patients, the approach we describe is generalisable to a wide range of medical areas

LIBER's involvement in supporting digital preservation in member libraries

Digital curation and preservation represent new challenges for universities. LIBER has invested considerable effort to engage with the new agendas of digital preservation and digital curation. Through two successful phases of the LIFE project, LIBER is breaking new ground in identifying innovative models for costing digital curation and preservation. Through LIFE’s input into the US-UK Blue Ribbon Task Force on Sustainable Digital Preservation and Access, LIBER is aligned with major international work in the economics of digital preservation. In its emerging new strategy and structures, LIBER will continue to make substantial contributions in this area, mindful of the needs of European research libraries

Digital Curation and the Citizen Archivist

The increasing array and power of personal digital recordkeeping systems promises both to make it more difficult for established archives to acquire personal and family archives and less likely that individuals might wish to donate personal and family digital archives to archives, libraries, museums, and other institutions serving as documentary repositories. This paper provides a conceptual argument for how projects such as the Digital Curation one ought to consider developing spinoffs for archivists training private citizens how to preserve, manage, and use digital personal and family archives. Rethinking how we approach the public, which will increasingly face difficult challenges in caring for their digital archives, also brings with it substantial promise in informing them about the nature and importance of the archival mission. Can the Digital Curation project provide tools that canbe used for working with the public

Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language Models

Large-scale clinical data is invaluable to driving many computational scientific advances today. However, understandable concerns regarding patient privacy hinder the open dissemination of such data and give rise to suboptimal siloed research. De-identification methods attempt to address these concerns but were shown to be susceptible to adversarial attacks. In this work, we focus on the vast amounts of unstructured natural language data stored in clinical notes and propose to automatically generate synthetic clinical notes that are more amenable to sharing using generative models trained on real de-identified records. To evaluate the merit of such notes, we measure both their privacy preservation properties as well as utility in training clinical NLP models. Experiments using neural language models yield notes whose utility is close to that of the real ones in some clinical NLP tasks, yet leave ample room for future improvements.Comment: Clinical NLP Workshop 201