Provenance-aware pervasive computing in clinical applications

Kovalchuk, Yevgeniya; Chen, Yuhui; Miles, Simon; Liang, Shao Fen; Taweel, Adel

doi:10.1109/wimob.2013.6673376

Cited by 3 publications

(12 citation statements)

References 12 publications

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“…All 17 studies were found in the SLR process together with the snowball technique, which are [ 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 ] and are focused on managing provenance data in HISs. In Table 10 , the similarities of the 17 studies are observed in the following characteristics that contribute to the management of provenance data in HISs: use of models from the W3C PROV family; use of different models from the W3C PROV family; use of provenance techniques with blockchain; and use of provenance techniques with middleware.…”

Section: Similarities Of the Selected Studiesmentioning

confidence: 99%

“…We highlight the data provenance models indicated by the W3C implemented through computational tools in different types of HISs. The models indicated by the W3C that appeared the most were as follows: PROV [ 91 , 96 , 98 , 99 , 103 , 105 , 106 ] applied in HISs (EHRs, PHRs, the LHS, the CRIS, and the HIS); PROV-O [ 91 , 95 , 96 , 98 , 99 ] applied in EHRs, PHRs, the LHS, and the CRIS; OPM [ 97 , 98 , 100 , 101 ] applied in HISs (PHR and LHS); PROV-DM [ 91 , 96 , 99 , 101 ] applied in HISs (HER, PHRs, and the CRIS); and PROV-N [ 98 , 99 ] applied in HISs (PHRs and the LHS). These models appear more frequently because they are application-specific models of data provenance recommended by the W3C, capable of describing the entities and processes involved in the production of a resource.…”

Section: Systematic Literature Review Reportmentioning

confidence: 99%

“…It is important to emphasize that the types of HISs shown in Figure 5 have different characteristics, as detailed in this study. Moreover, based on the information in Figure 5 , the authors who mentioned the types of HISs in their studies in relation to the management of provenance data in the selected studies are as follows: EHRs [ 93 , 94 , 95 , 96 , 103 , 106 ]; PHRs [ 90 , 97 , 99 , 100 , 101 , 102 , 104 ]; the LHS [ 98 ]; HMSs [ 92 ]; the CRIS [ 91 ]; and the HIS [ 105 ].…”

Section: Systematic Literature Review Reportmentioning

confidence: 99%

“…The main technologies found and the respective authors that relate them are as follows: (i) The use of Extract Transform and Load (ETL), software tools whose function is to extract data from different systems, transform these data according to business rules, and finally load the data. The studies [ 96 , 98 ] present this technology; (ii) The studies [ 91 , 92 , 93 , 94 , 99 , 102 ] present mobile technologies (smartphones, tablets, notebooks, sensors for data collection) and Personal Digital Assistants (PDAs) for monitoring medical data from a distance [ 90 ]; (iii) In order to create an efficient way to represent data on the World Wide Web in order to build a global database of connected data, using the strategies of the Semantic Web Extensible Markup Language (XML) [ 90 , 94 , 101 , 105 , 106 ], Ontology Web Language (OWL) [ 91 , 96 , 98 , 105 ], and Resource Description Framework (RDF) [ 91 , 96 , 98 , 105 ], and the use of semantic web languages such as SPARQL Protocol and RDF Query Language [ 91 , 98 , 104 ]; (iv) Cloud computing structures are presented in the studies [ 90 , 94 , 99 , 100 , 101 , 102 , 103 ] as a way to guarantee the amount of resources needed for their operations to occur without errors or bottlenecks; (v) The studies [ 90 , 91 , 92 , 94 , 95 , 97 , 100 , 101 , 102 , 104 ] present private networks for...…”

Section: Systematic Literature Review Reportmentioning

confidence: 99%

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Provenance Data Management in Health Information Systems: A Systematic Literature Review

Sembay

Macedo

Júnior

et al. 2023

JPM

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Aims: This article aims to perform a Systematic Literature Review (SLR) to better understand the structures of different methods, techniques, models, methodologies, and technologies related to provenance data management in health information systems (HISs). The SLR developed here seeks to answer the questions that contribute to describing the results. Method: An SLR was performed on six databases using a search string. The backward and forward snowballing technique was also used. Eligible studies were all articles in English that presented on the use of different methods, techniques, models, methodologies, and technologies related to provenance data management in HISs. The quality of the included articles was assessed to obtain a better connection to the topic studied. Results: Of the 239 studies retrieved, 14 met the inclusion criteria described in this SLR. In order to complement the retrieved studies, 3 studies were included using the backward and forward snowballing technique, totaling 17 studies dedicated to the construction of this research. Most of the selected studies were published as conference papers, which is common when involving computer science in HISs. There was a more frequent use of data provenance models from the PROV family in different HISs combined with different technologies, among which blockchain and middleware stand out. Despite the advantages found, the lack of technological structure, data interoperability problems, and the technical unpreparedness of working professionals are still challenges encountered in the management of provenance data in HISs. Conclusion: It was possible to conclude the existence of different methods, techniques, models, and combined technologies, which are presented in the proposal of a taxonomy that provides researchers with a new understanding about the management of provenance data in HISs.

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Section: Similarities Of the Selected Studiesmentioning

confidence: 99%

Section: Systematic Literature Review Reportmentioning

confidence: 99%

Section: Systematic Literature Review Reportmentioning

confidence: 99%

Section: Systematic Literature Review Reportmentioning

confidence: 99%

See 2 more Smart Citations

Provenance Data Management in Health Information Systems: A Systematic Literature Review

Sembay

Macedo

Júnior

et al. 2023

JPM

View full text Add to dashboard Cite

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“…Different examples of successful implementations of the single source concept are described in the literature. A generic solution was proposed by Kovalchuk et al [ 17 ], formed by different connected components: a centralized clinical data management system (CDMS) to support clinical research, a central resource registry that managed the information collected from pervasive devices (Central Resource Management System—CRMS), and healthcare information systems, such as the EHR. Another similar solution was the one proposed by Lu [ 18 ], which is a basis for the XIENCE global post marketing study, carried out in the United States and India.…”

Section: Introductionmentioning

confidence: 99%

A SOA-Based Platform to Support Clinical Data Sharing

Gazzarata

Giannini

2017

Journal of Healthcare Engineering

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The eSource Data Interchange Group, part of the Clinical Data Interchange Standards Consortium, proposed five scenarios to guide stakeholders in the development of solutions for the capture of eSource data. The fifth scenario was subdivided into four tiers to adapt the functionality of electronic health records to support clinical research. In order to develop a system belonging to the “Interoperable” Tier, the authors decided to adopt the service-oriented architecture paradigm to support technical interoperability, Health Level Seven Version 3 messages combined with LOINC (Logical Observation Identifiers Names and Codes) vocabulary to ensure semantic interoperability, and Healthcare Services Specification Project standards to provide process interoperability. The developed architecture enhances the integration between patient-care practice and medical research, allowing clinical data sharing between two hospital information systems and four clinical data management systems/clinical registries. The core is formed by a set of standardized cloud services connected through standardized interfaces, involving client applications. The system was approved by a medical staff, since it reduces the workload for the management of clinical trials. Although this architecture can realize the “Interoperable” Tier, the current solution actually covers the “Connected” Tier, due to local hospital policy restrictions.

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Analysis of diagnoses extracted from electronic health records in a large mental health case register

et al. 2017

Self Cite

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The UK government has recently recognised the need to improve mental health services in the country. Electronic health records provide a rich source of patient data which could help policymakers to better understand needs of the service users. The main objective of this study is to unveil statistics of diagnoses recorded in the Case Register of the South London and Maudsley NHS Foundation Trust, one of the largest mental health providers in the UK and Europe serving a source population of over 1.2 million people residing in south London. Based on over 500,000 diagnoses recorded in ICD10 codes for a cohort of approximately 200,000 mental health patients, we established frequency rate of each diagnosis (the ratio of the number of patients for whom a diagnosis has ever been recorded to the number of patients in the entire population who have made contact with mental disorders). We also investigated differences in diagnoses prevalence between subgroups of patients stratified by gender and ethnicity. The most common diagnoses in the considered population were (recurrent) depression (ICD10 codes F32-33; 16.4% of patients), reaction to severe stress and adjustment disorders (F43; 7.1%), mental/behavioural disorders due to use of alcohol (F10; 6.9%), and schizophrenia (F20; 5.6%). We also found many diagnoses which were more likely to be recorded in patients of a certain gender or ethnicity. For example, mood (affective) disorders (F31-F39); neurotic, stress-related and somatoform disorders (F40-F48, except F42); and eating disorders (F50) were more likely to be found in records of female patients, while males were more likely to be diagnosed with mental/behavioural disorders due to psychoactive substance use (F10-F19). Furthermore, mental/behavioural disorders due to use of alcohol and opioids were more likely to be recorded in patients of white ethnicity, and disorders due to use of cannabinoids in those of black ethnicity.

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Provenance-aware pervasive computing in clinical applications

Cited by 3 publications

References 12 publications

Provenance Data Management in Health Information Systems: A Systematic Literature Review

Provenance Data Management in Health Information Systems: A Systematic Literature Review

A SOA-Based Platform to Support Clinical Data Sharing

Analysis of diagnoses extracted from electronic health records in a large mental health case register

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