scholarly journals Health informatics and EHR to support clinical research in the COVID-19 pandemic: an overview

2021 ◽  
Author(s):  
Arianna Dagliati ◽  
Alberto Malovini ◽  
Valentina Tibollo ◽  
Riccardo Bellazzi
Keyword(s):  
Health Informatics ◽  
Data Privacy ◽  
Lessons Learned ◽  
Data Governance ◽  
Data Interoperability ◽  
Data Stewardship ◽  
Data Infrastructures ◽  
Healthcare Processes ◽  
Day By Day ◽  
Open Issues

Abstract The coronavirus disease 2019 (COVID-19) pandemic has clearly shown that major challenges and threats for humankind need to be addressed with global answers and shared decisions. Data and their analytics are crucial components of such decision-making activities. Rather interestingly, one of the most difficult aspects is reusing and sharing of accurate and detailed clinical data collected by Electronic Health Records (EHR), even if these data have a paramount importance. EHR data, in fact, are not only essential for supporting day-by-day activities, but also they can leverage research and support critical decisions about effectiveness of drugs and therapeutic strategies. In this paper, we will concentrate our attention on collaborative data infrastructures to support COVID-19 research and on the open issues of data sharing and data governance that COVID-19 had made emerge. Data interoperability, healthcare processes modelling and representation, shared procedures to deal with different data privacy regulations, and data stewardship and governance are seen as the most important aspects to boost collaborative research. Lessons learned from COVID-19 pandemic can be a strong element to improve international research and our future capability of dealing with fast developing emergencies and needs, which are likely to be more frequent in the future in our connected and intertwined world.

scholarly journals Data Governance in the Dairy Industry

Animals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2981
Author(s):  
Roger Cue ◽  
Mark Doornink ◽  
Regi George ◽  
Benjamin Griffiths ◽  
Matthew W. Jorgensen ◽  
...  
Keyword(s):  
Data Sharing ◽  
Data Privacy ◽  
Dairy Farm ◽  
Legal Regulation ◽  
Data Governance ◽  
Codes Of Practice ◽  
The Status ◽  
Data Stewardship ◽  
Data Ecosystem ◽  
Diverse Data

Data governance is a growing concern in the dairy farm industry because of the lack of legal regulation. In this commentary paper, we discuss the status quo of the available legislation and codes, as well as some possible solutions. To our knowledge, there are currently four codes of practice that address agriculture data worldwide, and their objectives are similar: (1) raise awareness of diverse data challenges such as data sharing and data privacy, (2) provide data security, and (3) illustrate the importance of the transparency of terms and conditions of data sharing contracts. However, all these codes are voluntary, which limits their adoption. We propose a Farmers Bill of Rights for the dairy data ecosystem to address some key components around data ownership and transparency in data sharing. Our hope is to start the discussion to create a balanced environment to promote equity within the data economy, encourage proper data stewardship, and to foster trust and harmony between the industry companies and the farmers when it comes to sharing data.

Semantic Information Retrieval on Medical Texts

2022 ◽  
Vol 54 (7) ◽  
pp. 1-38
Author(s):  
Lynda Tamine ◽  
Lorraine Goeuriot
Keyword(s):  
Information Retrieval ◽  
Health Informatics ◽  
Medical Information ◽  
State Of The Art ◽  
Lessons Learned ◽  
Semantic Search ◽  
Future Research ◽  
Cross Model ◽  
Wide Range ◽  
Search Systems

The explosive growth and widespread accessibility of medical information on the Internet have led to a surge of research activity in a wide range of scientific communities including health informatics and information retrieval (IR). One of the common concerns of this research, across these disciplines, is how to design either clinical decision support systems or medical search engines capable of providing adequate support for both novices (e.g., patients and their next-of-kin) and experts (e.g., physicians, clinicians) tackling complex tasks (e.g., search for diagnosis, search for a treatment). However, despite the significant multi-disciplinary research advances, current medical search systems exhibit low levels of performance. This survey provides an overview of the state of the art in the disciplines of IR and health informatics, and bridging these disciplines shows how semantic search techniques can facilitate medical IR. First,we will give a broad picture of semantic search and medical IR and then highlight the major scientific challenges. Second, focusing on the semantic gap challenge, we will discuss representative state-of-the-art work related to feature-based as well as semantic-based representation and matching models that support medical search systems. In addition to seminal works, we will present recent works that rely on research advancements in deep learning. Third, we make a thorough cross-model analysis and provide some findings and lessons learned. Finally, we discuss some open issues and possible promising directions for future research trends.

Doing and Understanding

2013 ◽  
pp. 1-34 ◽  
Author(s):  
Cynthia LeRouge ◽  
Herman Tolentino ◽  
Sherrilynne Fuller ◽  
Allison Tuma
Keyword(s):  
Health Informatics ◽  
Teaching Experience ◽  
Lessons Learned ◽  
Case Method

This chapter provides an introduction to the pedagogy of using the case method particularly for instruction in the health informatics context. The thoughts and insights shared in this chapter are inspired by basic theories, published methods, and lessons learned from the authors’ collective experiences. They illustrate the case teaching experience by engaging the reader in an exercise to highlight the basic phases of the case method process and challenges of the process. The case referenced in this exercise (provided in the Appendix to this chapter) has been used on multiple occasions by authors of this chapter, and they draw on their experiences in using this case to illustrate points throughout the exercise. The authors close the chapter by providing the reader with strategies and considerations in using the case method.

Challenges of Data Management in Always-On Enterprise Information Systems

2010 ◽  
pp. 443-462
Author(s):  
Mladen Varga
Keyword(s):  
Information Systems ◽  
Data Management ◽  
Data Privacy ◽  
Data Governance ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Degradation Data ◽  
Association Data ◽  
Data Continuity ◽  
Integration Data

Data management in always-on enterprise information systems is an important function that must be governed, that is, planned, supervised, and controlled. According to Data Management Association, data management is the development, execution, and supervision of plans, policies, programs, and practices that control, protect, deliver, and enhance the value of data and information assets. The challenges of successful data management are numerous and vary from technological to conceptual and managerial. The purpose of this chapter is to consider some of the most challenging aspects of data management, whether they are classified as data continuity aspects (e.g., data availability, data protection, data integrity, data security), data improvement aspects (e.g., coping with data overload and data degradation, data integration, data quality, data ownership/stewardship, data privacy, data visualization) or data management aspect (e.g., data governance), and to consider the means of taking care of them.

scholarly journals Improving Bridging from Informatics Theory to Practice

2015 ◽  
Vol 06 (04) ◽  
pp. 748-756 ◽  
Author(s):  
R. Haux ◽  
S. Koch
Keyword(s):  
Health Informatics ◽  
Assistive Technologies ◽  
Lessons Learned ◽  
Board Members ◽  
Practical Implementation ◽  
Editorial Policies ◽  
Theory To Practice ◽  
Increasing Demand ◽  
Practical Impact

SummaryBackground: In 1962, Methods of Information in Medicine (MIM) began to publish papers on the methodology and scientific fundamentals of managing data, information, and knowledge in biomedicine and health care. Meeting an increasing demand for research about practical implementation of health information systems, the journal Applied Clinical Informatics (ACI) was launched in 2009. Both journals are official journals of the International Medical Informatics Association (IMIA).Objectives: Based on prior analyses, we aimed to describe major topics published in MIM during 2014 and to explore whether theory of MIM influenced practice of ACI. Our objectives were further to describe lessons learned and to discuss possible editorial policies to improve bridging from theory to practice.Methods: We conducted a retrospective, observational study reviewing MIM articles published during 2014 (N=61) and analyzing reference lists of ACI articles from 2014 (N=70). Lessons learned and opinions about MIM editorial policies were developed in consensus by the two authors. These have been influenced by discussions with the journal’s associate editors and editorial board members.Results: The publication topics of MIM in 2014 were broad, covering biomedical and health informatics, medical biometry and epidemiology. Important topics discussed were biosignal interpretation, boosting methodologies, citation analysis, health-enabling and ambient assistive technologies, health record banking, safety, and standards. Nine ACI practice articles from 2014 cited eighteen MIM theory papers from any year. These nine ACI articles covered mainly the areas of clinical documentation and medication-related decision support. The methodological basis they cited from was almost exclusively related to evaluation. We could show some direct links where theory impacted practice. These links are however few in relation to the total amount of papers published.Conclusions: Editorial policies such as publishing systematic methodological reviews and clarification of possible practical impact of theory-focused articles may improve bridging.

Challenges of Data Management in Always-On Enterprise Information Systems

2011 ◽  
pp. 1695-1714 ◽  
Author(s):  
Mladen Varga
Keyword(s):  
Information Systems ◽  
Data Management ◽  
Data Privacy ◽  
Data Governance ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Degradation Data ◽  
Association Data ◽  
Data Continuity ◽  
Integration Data

Data management in always-on enterprise information systems is an important function that must be governed, that is, planned, supervised, and controlled. According to Data Management Association, data management is the development, execution, and supervision of plans, policies, programs, and practices that control, protect, deliver, and enhance the value of data and information assets. The challenges of successful data management are numerous and vary from technological to conceptual and managerial. The purpose of this chapter is to consider some of the most challenging aspects of data management, whether they are classified as data continuity aspects (e.g., data availability, data protection, data integrity, data security), data improvement aspects (e.g., coping with data overload and data degradation, data integration, data quality, data ownership/stewardship, data privacy, data visualization) or data management aspect (e.g., data governance), and to consider the means of taking care of them.

No business as usual: a case for data ethics and data governance in the age of coronavirus

2020 ◽  
Vol 44 (6) ◽  
pp. 1217-1221
Author(s):  
Anca C Yallop ◽  
Omid Aliasghar
Keyword(s):  
Information And Communication Technologies ◽  
Data Privacy ◽  
Dynamic Capabilities ◽  
New Technologies ◽  
Business Practices ◽  
Data Governance ◽  
Content Type ◽  
Ethical Implications ◽  
Data Ethics ◽  
Business As Usual

PurposeThe purpose of this commentary is to reflect on the transformative changes organisations experience, in the form of increased use of emergent information and communication technologies (ICTs), as a significant factor in enabling the continuation of normal business practices during the COVID-19 pandemic, and subsequent key ethical considerations in the use of new technology by organisations.Design/methodology/approachThis commentary adopts a reflective approach and is based on a review of theories on diffusion of innovation, dynamic capabilities and data ethics and governance, as well as up-to-date business reports to reflect on the ethical implications of new technologies for organisations.FindingsOrganisations from different industries and sectors around the world have experienced major disruptive changes because of the COVID-19 pandemic. Adoption and integration of new ICTs occurred at an accelerated pace in a collective effort to maintain “business as usual”. The use of emergent technologies is not without risks. The commentary argues that, in times of crisis, it is vital that organisations address the growing concerns around privacy and security of personal data by designing effective data governance frameworks that go beyond a mere compliance with existing policies and prevailing data privacy and protection laws to ensure data security and protection for all stakeholders.Originality/valueThis commentary is making the case for more considered approaches to data governance and data ethics in business following the unprecedented challenges posed by the recent COVID-19 pandemic and suggests possible ways of moving forward from an ethical perspective.

scholarly journals The French Health Data Hub and the German Medical Informatics Initiatives: Two National Projects to Promote Data Sharing in Healthcare

2019 ◽  
Vol 28 (01) ◽  
pp. 195-202 ◽  
Author(s):  
Marc Cuggia ◽  
Stéphanie Combes
Keyword(s):  
Data Sharing ◽  
Medical Informatics ◽  
Data Privacy ◽  
Data Use ◽  
Health Data ◽  
The European Union ◽  
Global Approach ◽  
Data Governance ◽  
Academic Hospitals ◽  
Access Data

Objective: The diversity and volume of health data have been rapidly increasing in recent years. While such big data hold significant promise for accelerating discovery, data use entails many challenges including the need for adequate computational infrastructure and secure processes for data sharing and access. In Europe, two nationwide projects have been launched recently to support these objectives. This paper compares the French Health Data Hub initiative (HDH) to the German Medical Informatics Initiatives (MII). Method: We analysed the projects according to the following criteria: (i) Global approach and ambitions, (ii) Use cases, (iii) Governance and organization, (iv) Technical aspects and interoperability, and (v) Data privacy access/data governance. Results: The French and German projects share the same objectives but are different in terms of methodologies. The HDH project is based on a top-down approach and focuses on a shared computational infrastructure, providing tools and services to speed projects between data producers and data users. The MII project is based on a bottom-up approach and relies on four consortia including academic hospitals, universities, and private partners. Conclusion: Both projects could benefit from each other. A Franco-German cooperation, extended to other countries of the European Union with similar initiatives, should allow sharing and strengthening efforts in a strategic area where competition from other countries has increased.

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