9 research outputs found
An instrument to identify computerised primary care research networks, genetic and disease registries prepared to conduct linked research:TRANSFoRm International Research Readiness (TIRRE) survey
Get PDFPURPOSE: The Translational Research and Patients safety in Europe (TRANSFoRm) project aims to integrate primary care with clinical research whilst improving patient safety. The TRANSFoRm International Research Readiness survey (TIRRE) aims to demonstrate data use through two linked data studies and by identifying clinical data repositories and genetic databases or disease registries prepared to participate in linked research.
METHOD: The TIRRE survey collects data at micro-, meso- and macro-levels of granularity; to fulfil data, study specific, business, geographical and readiness requirements of potential data providers for the TRANSFoRm demonstration studies. We used descriptive statistics to differentiate between demonstration-study compliant and non-compliant repositories. We only included surveys with >70% of questions answered in our final analysis, reporting the odds ratio (OR) of positive responses associated with a demonstration-study compliant data provider.
RESULTS: We contacted 531 organisations within the Eurpean Union (EU). Two declined to supply information; 56 made a valid response and a further 26 made a partial response. Of the 56 valid responses, 29 were databases of primary care data, 12 were genetic databases and 15 were cancer registries. The demonstration compliant primary care sites made 2098 positive responses compared with 268 in non-use-case compliant data sources [OR: 4.59, 95% confidence interval (CI): 3.93–5.35, p < 0.008]; for genetic databases: 380:44 (OR: 6.13, 95% CI: 4.25–8.85, p < 0.008) and cancer registries: 553:44 (OR: 5.87, 95% CI: 4.13–8.34, p < 0.008).
CONCLUSIONS: TIRRE comprehensively assesses the preparedness of data repositories to participate in specific research projects. Multiple contacts about hypothetical participation in research identified few potential sites
Defining datasets and creating data dictionaries for quality improvement and research in chronic disease using routinely collected data: an ontology-driven approach
Get PDFBackground The burden of chronic disease is increasing, and research and quality improvement will be less effective if case finding strategies are suboptimal.
Objective To describe an ontology-driven approach to case finding in chronic disease and how this approach can be used to create a data dictionary and make the codes used in case finding transparent.
Method A five-step process: (1) identifying a reference coding system or terminology; (2) using an ontology-driven approach to identify cases; (3) developing metadata that can be used to identify the extracted data; (4) mapping the extracted data to the reference terminology; and (5) creating the data dictionary.
Results Hypertension is presented as an exemplar. A patient with hypertension can be represented by a range of codes including diagnostic, history and administrative. Metadata can link the coding system and data extraction queries to the correct data mapping and translation tool, which then maps it to the equivalent code in the reference terminology. The code extracted, the term, its domain and subdomain, and the name of the data extraction query can then be automatically grouped and published online as a readily searchable data dictionary. An exemplar online is: www.clininf.eu/qickd-data-dictionary.html
Conclusion Adopting an ontology-driven approach to case finding could improve the quality of disease registers and of research based on routine data. It would offer considerable advantages over using limited datasets to define cases. This approach should be considered by those involved in research and quality improvement projects which utilise routine data
The provision and impact of online patient access to their electronic health records (EHR) and transactional services on the quality and safety of health care: systematic review protocol
Get PDFBackground: Innovators have piloted improvements in communication, changed patterns of practice and patient empowerment from online access to electronic health records (EHR). International studies of online services, such as prescription ordering, online appointment booking and secure communications with primary care, show good uptake of email consultations, accessing test results and booking appointments; when technologies and business process are in place. Online access and transactional services are due to be rolled out across England by 2015; this review seeks to explore the impact of online access to health records and other online services on the quality and safety of primary health care.
Objective: To assess the factors that may affect the provision of online patient access to their EHR and transactional services, and the impact of such access on the quality and safety of health care.
Method: Two reviewers independently searched 11 international databases during the period 1999–2012. A range of papers including descriptive studies using qualitative or quantitative methods, hypothesis-testing studies and systematic reviews were included. A detailed eligibility criterion will be used to shape study inclusion .A team of experts will review these papers for eligibility, extract data using a customised extraction form and use the Grading of Recommendations Assessment, Development and Evaluation (GRADE) instrument to determine the quality of the evidence and the strengths of any recommendation. Data will then be descriptively summarised and thematically synthesised. Where feasible, we will perform a quantitative meta-analysis
Appreciation of structured and unstructured content to aid decision making - from web scraping to ontologies and data dictionaries in healthcare.
No full textA systematic approach to the extraction of data from disparate data sources is proposed. The World Wide Web is a most diverse dataset; identifying ways in which this large database provides means for data quality verification with concepts such as data lineage and provenance allows to follow the same approach as a means to aid decision-making in sensitive domains such as healthcare. Through lessons learned from research in the UK and internationally, we conclude that emphasis on interoperable and model-based support of the data syndication can enhance data quality, an issue still current (American Hospital Association, 2015) and with data barriers in healthcare due to governance concerns. To improve on the above, we start by proposing a system for solution-orientated reporting of errors associated with the extraction of routinely collected clinical data. We then explore key concepts to assess the readiness of data for research and define an ontology-driven approach to create data dictionaries for quality improvement in healthcare. Finally, we apply this research to facilitate the enablement of consistent data recording across a health system to allow for service quality comparisons. Work deriving from this research and built by the author commissioned and aided by the UK NHS, University of Surrey, Green Cross Medical, particularly in creating and testing software systems in real-world scenarios, has facilitated: quality improvement in healthcare data extraction from GP practices in the UK, a state-of-art system for Web-enabling Hospital Episode Statistics (HES) data for dermatology and, finally, an online system designed to enable cancer Multi-Disciplinary Teams (MDTs) to self-assess and receive feedback on how their team performs against the standards set out in ‘The Characteristics of an Effective MDT’ provided by NHS IQ, formerly part of National Cancer Action Team (NCAT), which in 2016 won the Quality in Care Programme’s “Digital Innovation in the Treatment of Cancer” award. Further experimentation shows there is potential for the methods proposed to be applicable in other sectors such as the investment sector (initial investigation has happened through the early stages of this research) but it is suggested that this potential be explored further
Appreciation of structured and unstructured content to aid decision making - from web scraping to ontologies and data dictionaries in healthcare.
No full textA systematic approach to the extraction of data from disparate data sources is proposed. The World Wide Web is a most diverse dataset; identifying ways in which this large database provides means for data quality verification with concepts such as data lineage and provenance allows to follow the same approach as a means to aid decision-making in sensitive domains such as healthcare.
Through lessons learned from research in the UK and internationally, we conclude that emphasis on interoperable and model-based support of the data syndication can enhance data quality, an issue still current (American Hospital Association, 2015) and with data barriers in healthcare due to governance concerns. To improve on the above, we start by proposing a system for solution-orientated reporting of errors associated with the extraction of routinely collected clinical data. We then explore key concepts to assess the readiness of data for research and define an ontology-driven approach to create data dictionaries for quality improvement in healthcare. Finally, we apply this research to facilitate the enablement of consistent data recording across a health system to allow for service quality comparisons.
Work deriving from this research and built by the author commissioned and aided by the UK NHS, University of Surrey, Green Cross Medical, particularly in creating and testing software systems in real-world scenarios, has facilitated: quality improvement in healthcare data extraction from GP practices in the UK, a state-of-art system for Web-enabling Hospital Episode Statistics (HES) data for dermatology and, finally, an online system designed to enable cancer Multi-Disciplinary Teams (MDTs) to self-assess and receive feedback on how their team performs against the standards set out in ‘The Characteristics of an Effective MDT’ provided by NHS IQ, formerly part of National Cancer Action Team (NCAT), which in 2016 won the Quality in Care Programme’s “Digital Innovation in the Treatment of Cancer” award.
Further experimentation shows there is potential for the methods proposed to be applicable in other sectors such as the investment sector (initial investigation has happened through the early stages of this research) but it is suggested that this potential be explored further
Defining datasets and creating data dictionaries for quality improvement and research in chronic disease using routinely collected data: An ontology-driven approach
No full textBackground: The burden of chronic disease is increasing, and research and quality improvement will be less effective if case finding strategies are suboptimal. Objective: To describe an ontology-driven approach to case finding in chronic disease and how this approach can be used to create a data dictionary and make the codes used in case finding transparent. Method: A five-step process: (1) identifying a reference coding system or terminology; (2) using an ontology-driven approach to identify cases; (3) developing metadata that can be used to identify the extracted data; (4) mapping the extracted data to the reference terminology; and (5) creating the data dictionary. Results: Hypertension is presented as an exemplar. A patient with hypertension can be represented by a range of codes including diagnostic, history and administrative. Metadata can link the coding system and data extraction queries to the correct data mapping and translation tool, which then maps it to the equivalent code in the reference terminology. The code extracted, the term, its domain and subdomain, and the name of the data extraction query can then be automatically grouped and published online as a readily searchable data dictionary. An exemplar online is: www.clininf.eu/qickd-datadictionary. html Conclusion: Adopting an ontology-driven approach to case finding could improve the quality of disease registers and of research based on routine data. It would offer considerable advantages over using limited datasets to define cases. This approach should be considered by those involved in research and quality improvement projects which utilise routine data
Defining datasets and creating data dictionaries for quality improvement and research in chronic disease using routinely collected data: An ontology-driven approach
No full textBackground: The burden of chronic disease is increasing, and research and quality improvement will be less effective if case finding strategies are suboptimal. Objective: To describe an ontology-driven approach to case finding in chronic disease and how this approach can be used to create a data dictionary and make the codes used in case finding transparent. Method: A five-step process: (1) identifying a reference coding system or terminology; (2) using an ontology-driven approach to identify cases; (3) developing metadata that can be used to identify the extracted data; (4) mapping the extracted data to the reference terminology; and (5) creating the data dictionary. Results: Hypertension is presented as an exemplar. A patient with hypertension can be represented by a range of codes including diagnostic, history and administrative. Metadata can link the coding system and data extraction queries to the correct data mapping and translation tool, which then maps it to the equivalent code in the reference terminology. The code extracted, the term, its domain and subdomain, and the name of the data extraction query can then be automatically grouped and published online as a readily searchable data dictionary. An exemplar online is: www.clininf.eu/qickd-datadictionary. html Conclusion: Adopting an ontology-driven approach to case finding could improve the quality of disease registers and of research based on routine data. It would offer considerable advantages over using limited datasets to define cases. This approach should be considered by those involved in research and quality improvement projects which utilise routine data
Key Concepts to Assess the Readiness of Data for International Research: Data Quality, Lineage and Provenance, Extraction and Processing Errors, Traceability, and Curation.:Contribution of the IMIA Primary Health Care Informatics Working Group.
No full textTo define the key concepts which inform whether a system for collecting, aggregating and processing routine clinical data for research is fit for purpose
Developing a survey instrument to assess the readiness of primary care data, genetic and disease registries to conduct linked research:TRANSFoRm International Research Readiness (TIRRE) survey instrument
Get PDFBackground Clinical data are collected for routine care in family practice; there are also a growing number of genetic and cancer registry data repositories. The Translational Research and Patient Safety in Europe (TRANSFoRm) project seeks to facilitate research using linked data from more than one source. We performed a requirements analysis which identified a wide range of data and business process requirements that need to be met before linking primary care and either genetic or disease registry data.Objectives To develop a survey to assess the readiness of data repositories to participate in linked research – the Transform International Research Readiness (TIRRE) survey.Method We develop the questionnaire based on our requirement analysis; with questions at micro-, meso- and macro levels of granularity, study-specific questions about diabetes and gastro-oesophageal reflux disease (GORD), and research track record. The scope of the data required was extensive. We piloted this instrument, conducting ten preliminary telephone interviews to evaluate the response to the questionnaire.Results Using feedback gained from these interviews we revised the questionnaire; clarifying questions that were difficult to answer and utilising skip logic to create different series of questions for the various types of data repository. We simplified the questionnaire replacing free-text responses with yes/no or picking list options, wherever possible. We placed the final questionnaire online and encouraged its use (www.clininf.eu/jointirre/info.html).Conclusion Limited field testing suggests that TIRRE is capable of collecting comprehensive and relevant data about the suitability and readiness of data repositories to participate in linked data research.</p
