Meta-audit: a novel approach to healthcare improvement through the integration of clinical audit and health data science techniques

2021 ◽  
Vol 10 (6) ◽  
pp. 3-5
Author(s):  
A. J. Hall ◽  
A. D. Duckworth ◽  
N. D. Clement ◽  
A. M. J. MacLullich ◽  
L. Farrow
2017 ◽  
Vol 26 (01) ◽  
pp. 59-67 ◽  
Author(s):  
P. J. Scott ◽  
M. Rigby ◽  
E. Ammenwerth ◽  
J. McNair ◽  
A. Georgiou ◽  
...  

Summary Objectives: To set the scientific context and then suggest principles for an evidence-based approach to secondary uses of clinical data, covering both evaluation of the secondary uses of data and evaluation of health systems and services based upon secondary uses of data. Method: Working Group review of selected literature and policy approaches. Results: We present important considerations in the evaluation of secondary uses of clinical data from the angles of governance and trust, theory, semantics, and policy. We make the case for a multi-level and multi-factorial approach to the evaluation of secondary uses of clinical data and describe a methodological framework for best practice. We emphasise the importance of evaluating the governance of secondary uses of health data in maintaining trust, which is essential for such uses. We also offer examples of the re-use of routine health data to demonstrate how it can support evaluation of clinical performance and optimize health IT system design. Conclusions: Great expectations are resting upon “Big Data” and innovative analytics. However, to build and maintain public trust, improve data reliability, and assure the validity of analytic inferences, there must be independent and transparent evaluation. A mature and evidence-based approach needs not merely data science, but must be guided by the broader concerns of applied health informatics.


Symmetry ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 374 ◽  
Author(s):  
Chi-Hua Chen ◽  
Eyhab Al-Masri ◽  
Feng-Jang Hwang ◽  
Despo Ktoridou ◽  
Kuen-Rong Lo

This editorial introduces the special issue, entitled “Applications of Internet of Things”, of Symmetry. The topics covered in this issue fall under four main parts: (I) communication techniques and applications, (II) data science techniques and applications, (III) smart transportation, and (IV) smart homes. Four papers on sensing techniques and applications are included as follows: (1) “Reliability of improved cooperative communication over wireless sensor networks”, by Chen et al.; (2) “User classification in crowdsourcing-based cooperative spectrum sensing”, by Zhai and Wang; (3) “IoT’s tiny steps towards 5G: Telco’s perspective”, by Cero et al.; and (4) “An Internet of things area coverage analyzer (ITHACA) for complex topographical scenarios”, by Parada et al. One paper on data science techniques and applications is as follows: “Internet of things: a scientometric review”, by Ruiz-Rosero et al. Two papers on smart transportation are as follows: (1) “An Internet of things approach for extracting featured data using an AIS database: an application based on the viewpoint of connected ships”, by He et al.; and (2) “The development of key technologies in applications of vessels connected to the Internet”, by Tian et al. Two papers on smart home are as follows: (1) “A novel approach based on time cluster for activity recognition of daily living in smart homes”, by Liu et al.; and (2) “IoT-based image recognition system for smart home-delivered meal services”, by Tseng et al.


Author(s):  
Zhaohao Sun ◽  
Andrew Stranieri

Intelligent analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores the nature of intelligent analytics. More specifically, this chapter identifies the foundations, cores, and applications of intelligent big data analytics based on the investigation into the state-of-the-art scholars' publications and market analysis of advanced analytics. Then it presents a workflow-based approach to big data analytics and technological foundations for intelligent big data analytics through examining intelligent big data analytics as an integration of AI and big data analytics. The chapter also presents a novel approach to extend intelligent big data analytics to intelligent analytics. The proposed approach in this chapter might facilitate research and development of intelligent analytics, big data analytics, business analytics, business intelligence, AI, and data science.


2019 ◽  
Vol 37 (6) ◽  
pp. 929-951 ◽  
Author(s):  
Laurent Remy ◽  
Dragan Ivanović ◽  
Maria Theodoridou ◽  
Athina Kritsotaki ◽  
Paul Martin ◽  
...  

Purpose The purpose of this paper is to boost multidisciplinary research by the building of an integrated catalogue or research assets metadata. Such an integrated catalogue should enable researchers to solve problems or analyse phenomena that require a view across several scientific domains. Design/methodology/approach There are two main approaches for integrating metadata catalogues provided by different e-science research infrastructures (e-RIs): centralised and distributed. The authors decided to implement a central metadata catalogue that describes, provides access to and records actions on the assets of a number of e-RIs participating in the system. The authors chose the CERIF data model for description of assets available via the integrated catalogue. Analysis of popular metadata formats used in e-RIs has been conducted, and mappings between popular formats and the CERIF data model have been defined using an XML-based tool for description and automatic execution of mappings. Findings An integrated catalogue of research assets metadata has been created. Metadata from e-RIs supporting Dublin Core, ISO 19139, DCAT-AP, EPOS-DCAT-AP, OIL-E and CKAN formats can be integrated into the catalogue. Metadata are stored in CERIF RDF in the integrated catalogue. A web portal for searching this catalogue has been implemented. Research limitations/implications Only five formats are supported at this moment. However, description of mappings between other source formats and the target CERIF format can be defined in the future using the 3M tool, an XML-based tool for describing X3ML mappings that can then be automatically executed on XML metadata records. The approach and best practices described in this paper can thus be applied in future mappings between other metadata formats. Practical implications The integrated catalogue is a part of the eVRE prototype, which is a result of the VRE4EIC H2020 project. Social implications The integrated catalogue should boost the performance of multi-disciplinary research; thus it has the potential to enhance the practice of data science and so contribute to an increasingly knowledge-based society. Originality/value A novel approach for creation of the integrated catalogue has been defined and implemented. The approach includes definition of mappings between various formats. Defined mappings are effective and shareable.


Author(s):  
Yi Liu ◽  
Benjamin Elsworth ◽  
Pau Erola ◽  
Valeriia Haberland ◽  
Gibran Hemani ◽  
...  

Abstract Motivation The wealth of data resources on human phenotypes, risk factors, molecular traits and therapeutic interventions presents new opportunities for population health sciences. These opportunities are paralleled by a growing need for data integration, curation and mining to increase research efficiency, reduce mis-inference and ensure reproducible research. Results We developed EpiGraphDB (https://epigraphdb.org/), a graph database containing an array of different biomedical and epidemiological relationships and an analytical platform to support their use in human population health data science. In addition, we present three case studies that illustrate the value of this platform. The first uses EpiGraphDB to evaluate potential pleiotropic relationships, addressing mis-inference in systematic causal analysis. In the second case study, we illustrate how protein–protein interaction data offer opportunities to identify new drug targets. The final case study integrates causal inference using Mendelian randomization with relationships mined from the biomedical literature to ‘triangulate’ evidence from different sources. Availability and implementation The EpiGraphDB platform is openly available at https://epigraphdb.org. Code for replicating case study results is available at https://github.com/MRCIEU/epigraphdb as Jupyter notebooks using the API, and https://mrcieu.github.io/epigraphdb-r using the R package. Supplementary information Supplementary data are available at Bioinformatics online.


2017 ◽  
Vol 26 (01) ◽  
pp. 59-67 ◽  
Author(s):  
P. J. Scott ◽  
M. Rigby ◽  
E. Ammenwerth ◽  
J. McNair ◽  
A. Georgiou ◽  
...  

Summary Objectives: To set the scientific context and then suggest principles for an evidence-based approach to secondary uses of clinical data, covering both evaluation of the secondary uses of data and evaluation of health systems and services based upon secondary uses of data. Method: Working Group review of selected literature and policy approaches. Results: We present important considerations in the evaluation of secondary uses of clinical data from the angles of governance and trust, theory, semantics, and policy. We make the case for a multi-level and multi-factorial approach to the evaluation of secondary uses of clinical data and describe a methodological framework for best practice. We emphasise the importance of evaluating the governance of secondary uses of health data in maintaining trust, which is essential for such uses. We also offer examples of the re-use of routine health data to demonstrate how it can support evaluation of clinical performance and optimize health IT system design. Conclusions: Great expectations are resting upon “Big Data” and innovative analytics. However, to build and maintain public trust, improve data reliability, and assure the validity of analytic inferences, there must be independent and transparent evaluation. A mature and evidence-based approach needs not merely data science, but must be guided by the broader concerns of applied health informatics.


2018 ◽  
Vol 6 (3) ◽  
pp. 261-269 ◽  
Author(s):  
Niels Peek ◽  
Pedro Pereira Rodrigues
Keyword(s):  

Author(s):  
Sebastian Porsdam Mann ◽  
Julian Savulescu ◽  
Barbara J. Sahakian

Advances in data science allow for sophisticated analysis of increasingly large datasets. In the medical context, large volumes of data collected for healthcare purposes are contained in electronic health records (EHRs). The real-life character and sheer amount of data contained in them make EHRs an attractive resource for public health and biomedical research. However, medical records contain sensitive information that could be misused by third parties. Medical confidentiality and respect for patients' privacy and autonomy protect patient data, barring access to health records unless consent is given by the data subject. This creates a situation in which much of the beneficial records-based research is prevented from being used or is seriously undermined, because the refusal of consent by some patients introduces a systematic deviation, known as selection bias, from a representative sample of the general population, thus distorting research findings. Although research exemptions for the requirement of informed consent exist, they are rarely used in practice due to concerns over liability and a general culture of caution. In this paper, we argue that the problem of research access to sensitive data can be understood as a tension between the medical duties of confidentiality and beneficence. We attempt to show that the requirement of informed consent is not appropriate for all kinds of records-based research by distinguishing studies involving minimal risk from those that feature moderate or greater risks. We argue that the duty of easy rescue—the principle that persons should benefit others when this can be done at no or minimal risk to themselves—grounds the removal of consent requirements for minimally risky records-based research. Drawing on this discussion, we propose a risk-adapted framework for the facilitation of ethical uses of health data for the benefit of society. This article is part of the themed issue ‘The ethical impact of data science’.


2022 ◽  
Vol 3 (1) ◽  
pp. 1-29
Author(s):  
Pietro Crovari ◽  
Sara Pidò ◽  
Pietro Pinoli ◽  
Anna Bernasconi ◽  
Arif Canakoglu ◽  
...  

With the availability of reliable and low-cost DNA sequencing, human genomics is relevant to a growing number of end-users, including biologists and clinicians. Typical interactions require applying comparative data analysis to huge repositories of genomic information for building new knowledge, taking advantage of the latest findings in applied genomics for healthcare. Powerful technology for data extraction and analysis is available, but broad use of the technology is hampered by the complexity of accessing such methods and tools. This work presents GeCoAgent, a big-data service for clinicians and biologists. GeCoAgent uses a dialogic interface, animated by a chatbot, for supporting the end-users’ interaction with computational tools accompanied by multi-modal support. While the dialogue progresses, the user is accompanied in extracting the relevant data from repositories and then performing data analysis, which often requires the use of statistical methods or machine learning. Results are returned using simple representations (spreadsheets and graphics), while at the end of a session the dialogue is summarized in textual format. The innovation presented in this article is concerned with not only the delivery of a new tool but also our novel approach to conversational technologies, potentially extensible to other healthcare domains or to general data science.


Sign in / Sign up

Export Citation Format

Share Document