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BI-RADS is a communication and data tracking system that has evolved since its inception as a brief mammography lexicon and reporting guide into a robust structured reporting platform and comprehensive quality assurance tool for mammography, ultrasound, and MRI. Consistent and appropriate use of the BI-RADS lexicon terminology and assessment categories effectively communicates findings, estimates the risk of malignancy, and provides management recommendations to patients and referring clinicians. The impact of BI-RADS currently extends internationally through six language translations. A condensed version has been proposed to facilitate a phased implementation of BI-RADS in resource-constrained regions. The primary advance of the 5th edition of BI-RADS is harmonization of the lexicon terms across mammography, ultrasound, and MRI. Harmonization has also been achieved across these modalities for the reporting structure, assessment categories, management recommendations, and data tracking system. Areas for improvement relate to certain common findings that lack lexicon descriptors and a need for further clarification of proper use of category 3. BI-RADS is anticipated to continue to evolve for application to a range of emerging breast imaging modalities.

The prevalence of multimorbidity has been increasing in recent years, posing a major burden for health care delivery and service. Understanding its determinants and impact is proving to be a challenge yet it offers new opportunities for research to go beyond the study of diseases in isolation. In this paper, we review how the field of machine learning provides many tools for addressing research challenges in multimorbidity. We highlight recent advances in promising methods such as matrix factorisation, deep learning, and topological data analysis and how these can take multimorbidity research beyond cross-sectional, expert-driven or confirmatory approaches to gain a better understanding of evolving patterns of multimorbidity. We discuss the challenges and opportunities of machine learning to identify likely causal links between previously poorly understood disease associations while giving an estimate of the uncertainty on such associations. We finally summarise some of the challenges for wider clinical adoption of machine learning research tools and propose some solutions., (Copyright © 2020 The Author(s). Published by Elsevier B.V. All rights reserved.)

The recent opening of massive health databases, as well as the development of methods and tools adapted to their data processing, questions the French model of "morbidity registry". In France in 2019, nearly 61 health registries were operating. As defined by law, these registries identify exhaustively all patients with a given disease in a given territory. Established several decades ago, these registries are part of the French surveillance system that is used for research and evaluation purposes. Since the advent of recent technological progress, large-scale databases are made available to researchers and it is possible with these databases to answer questions initially assigned to the registries. What is the place of such registries in this new context: are they obsolete or still useful? Should they be opposed to the new tools or are they complementary to them, and if so, what is their place in the new French public health ecosystem? The objective of this work was to assess the roles and missions of existing registries and to reflect on their positioning in this new environment. The French model of registry is sometimes questioned because of the complexity of its circuits, requiring a significant amount of human resources. However, the data that constitute them, validated by cross-checking information from several sources, are of very high quality, and make it possible to validate the data in the new databases (National Health Data System (NSDS) or Hospital Data Warehouses). Registries and new databases are in fact complementary, and far from jeopardizing this model, the recent opening of these databases represents an opportunity for registries to modernize their operations and respond to new missions., (Copyright © 2019 Elsevier Masson SAS. All rights reserved.)

Background: There is a growing interest in creating large-scale repositories that store genetic, behavioral, and environmental data for future, unspecified uses. The All of Us Research Program is one example of such a repository. Its participants will get access to their personal data and the results of the studies that used them. However, little is known about what researchers should return to participants and how they should do it in a way that is valuable and meaningful to participants. Methods: To better understand the concept of "return of value" and the practice of returning valuable study information, we conducted semi-structured telephone interviews with 44 stakeholders with diverse perspectives on this topic. All interviews have been transcribed and coded thematically to identify the most salient themes, to explore differences between returning different types of study results, and to describe differences and similarities in perspectives of different stakeholder groups. Results: We found that one size does not fit all when it comes to returning value to participants: the decisions about return of results are affected by participant preferences, researchers' concerns about feasibility, the types of data collected, their level of granularity, and available options for supporting result interpretation. Conclusions: Our findings suggest that the key to operationalizing return of value and to identifying ways to return valuable information to study participants may be to find a point of equilibrium between criteria that may affect usefulness and feasibility. The point of equilibrium may vary by study, by participants' backgrounds and preferences, by their health literacy and access to regular healthcare, and by the resources available to professionals controlling the data. Future studies should explore the factors that determine the point of equilibrium between feasibility and usefulness.

The 2019 virtual issue of the Health Information and Libraries Journal (HILJ) is published to link to the 2019 EAHIL Workshop taking place in Basel, Switzerland on 17-20 June 2019. The workshop is structured around six topics: (i) Roadmap of our Profession; (ii) Technology Uptake; (iii) Ecology of Scholarly Communications; (iv) Impact + Assessment; (v) Benchmarking + Advocacy; (vi) Evidence-Based Practice. These themes have been used to compile this virtual issue, which contains published articles selected from HILJ from the March 2019 issue through to June 2017. The virtual issue mirrors the format of a regular issue of HILJ, namely a review article, five original articles and articles from our three regular features: 'Dissertations into Practice', 'International Perspectives and Initiatives' and 'Teaching and Learning in Action'. The authors come from the UK, Canada, Australia, Italy, Iran and Belgium. All articles included in this virtual issue are available free online., (© 2019 Health Libraries Group.)

Steady changes in society present challenges to constructive cooperation between stakeholders in the diverse PH landscape of Germany through individualism, globalisation, medical progress, digitalisation, etc. Working group 8 therefore suggests that the PH community should build new internal structures, in order to be able to respond jointly to external challenges, facilitate networking amongst the actors and speak with one voice, when needed. The suggestion is to establish an office that has the task to organise further meetings, harmonize written joint statements and moderate the dialogue amongst peers., Competing Interests: Interessenkonflikt: Die Autoren geben an, dass kein Interessenkonflikt besteht., (© Georg Thieme Verlag KG Stuttgart · New York.)

Objective: With the evolution of patient medical records from paper to electronic media and the changes to the way data is sourced, used, and managed, there is an opportunity for health information management (HIM) to learn and facilitate the increasing expanse of available patient data. Methods: This paper discusses the emerging trends and lessons learnt in relation with the following four areas: 1) data and information governance, 2) terminology standards certification, 3) International Classification of Diseases, 11th edition (ICD-11), and 4) data analytics and HIM. Results: The governance of patient data and information increasingly requires the HIM profession to incorporate the roles of data scientists and data stewards into its portfolio to ensure data analytics and digital transformation is appropriately managed. Not only are terminology standards required to facilitate the structure and primary use of this data, developments in Canada in relation with the standards, role descriptions, framework and curricula in the form of certification provide one prime example of ensuring the quality of the secondary use of patient data. The impending introduction of ICD-11 brings with it the need for the HIM profession to manage the transition between ICD versions and country modifications incorporating changes to standards and tools, and the availability and type of patient data available for secondary use. Conclusions: In summary, the health information management profession now requires abilities in leadership, data, and informatics in addition to health information science and coding skills to facilitate the expanding secondary use of patient data., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (Georg Thieme Verlag KG Stuttgart.)

In today's era of aging society, people want to handle personal health care by themselves in everyday life. In particular, the evolution of medical and IT convergence technology and mobile smart devices has made it possible for people to gather information on their health status anytime and anywhere easily using biometric information acquisition devices. Healthcare information systems can contribute to the improvement of the nation's healthcare quality and the reduction of related cost. However, there are no perfect security models or mechanisms for healthcare service applications, and privacy information can therefore be leaked. In this paper, we examine security requirements related to privacy protection in u-healthcare service and propose an extended RBAC based security model. We propose and design u-healthcare service integration platform (u-HCSIP) applying RBAC security model. The proposed u-HCSIP performs four main functions: storing and exchanging personal health records (PHR), recommending meals and exercise, buying/selling private health information or experience, and managing personal health data using smart devices.

Background: Anesthesia information management systems (AIMS) are electronic health records that automatically import vital signs from patient monitors and allow for computer-assisted creation of the anesthesia record. When most recently surveyed in 2007, it was estimated that at least 16% of U.S. academic hospitals (i.e., with an anesthesia residency program) had installed an AIMS. At least an additional 28% reported that they were in the process of implementing, or searching for an AIMS. In this study, we updated the adoption figures as of May 2013 and examined the historical trend of AIMS deployment in U.S. anesthesia residency programs from the perspective of the theory of diffusion of technologic innovations., Methods: Questionnaires were sent by e-mail to program directors or their identified contact individuals at the 130 U.S. anesthesiology residency programs accredited as of June 30, 2012 by the Accreditation Council for Graduate Medical Education. The questionnaires asked whether the department had an AIMS, the year of installation, and, if not present, whether there were plans to install an AIMS within the next 12 months. Follow-up e-mails and phone calls were made until responses were obtained from all programs. Results were collected between February and May 2013. Implementation percentages were determined using the number of accredited anesthesia residency programs at the start of each academic year between 1987 and 2013 and were fit to a logistic regression curve using data through 2012., Results: Responses were received from all 130 programs. Eighty-seven (67%) reported that they currently are using an AIMS. Ten programs without a current AIMS responded that they would be installing an AIMS within 12 months of the survey. The rate of AIMS adoption by year was well fit by a logistic regression curve (P = 0.90)., Conclusions: By the end of 2014, approximately 75% of U.S. academic anesthesiology departments will be using an AIMS, with 84% adoption expected between 2018 and 2020. Historical adoption of AIMS has followed Roger's 1962 formulation of the theory of diffusion of innovation.

As our understanding of the neurobiology of Alzheimer Disease deepens, it has become evident that early intervention is critical to achieving successful therapeutic impact. The availability of diagnostic criteria for preclinical Alzheimer Disease adds momentum to research directed at this goal and even to prevention. The landscape of therapeutic research is thus poised to undergo a dramatic shift in the next 5-10 years, with clinical trials involving subjects at risk for Alzheimer Disease who have few or no symptoms. These trials will also likely rely heavily on genetics, biomarkers, and or risk factor stratification to identify individuals at risk for Alzheimer Disease. Here, we propose a conceptual framework to guide this next generation of pharmacological and non-pharmacological clinical pursuit, and discuss some of the foreseeable ethical considerations that may accompany them., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Policy makers are actively promoting the electronic exchange of health information to improve the quality and efficiency of health care. We conducted a national survey of organizations facilitating health information exchange, to assess national progress. We found that 30 percent of hospitals and 10 percent of ambulatory practices now participate in one of the 119 operational health information exchange efforts across the United States, substantial growth from prior surveys. However, we also found that 74 percent of health information exchange efforts report struggling to develop a sustainable business model. Our findings suggest that despite progress, there is a substantial risk that many current efforts to promote health information exchange will fail when public funds supporting these initiatives are depleted.