What are the barriers to conducting international research using routinely collected primary care data? (original) (raw)
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Yearbook of Medical Informatics, 2011
SummaryTo define the key concepts which inform whether a system for collecting, aggregating and processing routine clinical data for research is fit for purpose.Literature review and shared experiential learning from research using routinely collected data. We excluded socio-cultural issues, and privacy and security issues as our focus was to explore linking clinical data.Six key concepts describe data: (1) Data quality: the core Overarching concept – Are these data fit for purpose? (2) Data provenance: defined as how data came to be; incorporating the concepts of lineage and pedigree. Mapping this process requires metadata. New variables derived during data analysis have their own provenance. (3) Data extraction errors and (4) Data processing errors, which are the responsibility of the investigator extracting the data but need quantifying. (5) Traceability: the capability to identify the origins of any data cell within the final analysis table essential for good governance, and alm...
BMC Medical Informatics and Decision Making, 2017
Background: Primary care data gathered from Electronic Health Records are of the utmost interest considering the essential role of general practitioners (GPs) as coordinators of patient care. These data represent the synthesis of the patient history and also give a comprehensive picture of the population health status. Nevertheless, discrepancies between countries exist concerning routine data collection projects. Therefore, we wanted to identify elements that influence the development and durability of such projects. Methods: A systematic review was conducted using the PubMed database to identify worldwide current primary care data collection projects. The gray literature was also searched via official project websites and their contact person was emailed to obtain information on the project managers. Data were retrieved from the included studies using a standardized form, screening four aspects: projects features, technological infrastructure, GPs' roles, data collection network organization. Results: The literature search allowed identifying 36 routine data collection networks, mostly in English-speaking countries: CPRD and THIN in the United Kingdom, the Veterans Health Administration project in the United States, EMRALD and CPCSSN in Canada. These projects had in common the use of technical facilities that range from extraction tools to comprehensive computing platforms. Moreover, GPs initiated the extraction process and benefited from incentives for their participation. Finally, analysis of the literature data highlighted that governmental services, academic institutions, including departments of general practice, and software companies, are pivotal for the promotion and durability of primary care data collection projects. Conclusion: Solid technical facilities and strong academic and governmental support are required for promoting and supporting long-term and wide-range primary care data collection projects.
BMC Medical Informatics and Decision Making, 2022
Background: Most people receive most of their health care in in Australia in primary care, yet researchers and policymakers have limited access to resulting clinical data. Widening access to primary care data and linking it with hospital or other data can contribute to research informing policy and provision of services and care; however, limitations of primary care data and barriers to access curtail its use. The Australian Health Research Alliance (AHRA) is seeking to build capacity in data-driven healthcare improvement; this study formed part of its workplan. Methods: The study aimed to build capacity for data driven healthcare improvement through identifying primary care datasets in Australia available for secondary use and understand data quality frameworks being applied to them, and factors affecting national capacity for secondary use of primary care data from the perspectives of data custodians and users. Purposive and snowball sampling were used to disseminate a questionnaire and respondents were invited to contribute additional information via semi-structured interviews. Results: Sixty-two respondents collectively named 106 datasets from eclectic sources, indicating a broad conceptualisation of what a primary care dataset available for secondary use is. The datasets were generated from multiple clinical software systems, using different data extraction tools, resulting in non-standardised data structures. Use of non-standard data quality frameworks were described by two-thirds of data custodians. Building trust between citizens, clinicians, third party data custodians and data end-users was considered by many to be a key enabler to improve primary care data quality and efficiencies related to secondary use. Trust building qualities included meaningful stakeholder engagement, transparency, strong leadership, shared vision, robust data security and data privacy protection. Resources to improve capacity for primary care data access and use were sought for data collection tool improvements, workforce upskilling and education, incentivising data collection and making data access more affordable. Conclusions: The large number of identified Australian primary care related datasets suggests duplication of labour related to data collection, preparation and utilisation. Benefits of secondary use of primary care data were many, and strong national leadership is required to reach consensus on how to address limitations and barriers, for example accreditation of EMR clinical software systems and the adoption of agreed data and quality standards at all stages of
IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2014
Primary care research databases provide a significant resource for health services and epidemiological research. However since data are recorded primarily for clinical care their suitability for research may vary widely according to the research application or recording practices of individual general practitioners. A methodological approach for characterising data quality is required. We describe a one-day workshop entitled "Towards a common protocol for measuring and monitoring data quality in European primary care research databases". Researchers, database experts and clinicians were invited to give their perspectives on data quality and to exchange ideas on what data quality metrics should be made available to researchers. We report the main outcomes of this workshop, including a summary of the presentations and discussions and suggested way forward.
Amia Annual Symposium Proceedings Amia Symposium Amia Symposium, 2011
The practice-based research network (PBRN) is a resource to recruit research participants; conduct developmental and pilot studies; and coordinate multicentre research, teaching, clinical care and quality assurance programs. It is a community-based laboratory for translational, clinical and health services research. The mining of clinical information systems of PBRNs can be used to monitor performance at the service unit level. However, are the routinely collected data of ePBRNs fit for the abovementioned purposes? We describe the establishment and governance of an ePBRN which included general practice and community health and hospital units, The general practice data quality was examined, using diabetes as the context, for completeness, correctness and consistency and assessed on its fitness for research, audit and quality assurance purposes. The quality of social determinants data was generally good while risk factors data were variable. Issues and strategies for improving data quality are discussed.
Family Practice, 2005
de Lusignan S and van Weel C. The use of routinely collected computer data for research in primary care: opportunities and challenges. Family Practice 2006; 23: 253-263. Introduction. Routinely collected primary care data has underpinned research that has helped define primary care as a specialty. In the early years of the discipline, data were collected manually, but digital data collection now makes large volumes of data readily available. Primary care informatics is emerging as an academic discipline for the scientific study of how to harness these data. This paper reviews how data are stored in primary care computer systems; current use of large primary care research databases; and, the opportunities and challenges for using routinely collected primary care data in research.
Sustaining the Effective Use of Health Care Data: A Message from the Editors
eGEMs (Generating Evidence & Methods to improve patient outcomes), 2014
Introduction: Over the past decade, several initiatives have funded large projects to develop clinical research data infrastructures totaling several hundred million dollars. While most of this funding has ended or is expected to end soon, the projects themselves must struggle to continue operations beyond the initial funding. Examples of sustained research-data infrastructures are lacking, and recommended approaches to improve sustainability of developing infrastructures are even rarer. Early on, the Electronic Data Methods (EDM) Forum-and the Agency for Healthcare Research and Quality (AHRQ) as its sponsor-recognized the need to study strategies for sustainability.
Studies in health technology and informatics, 2005
There are large numbers of schemes that collect and aggregate data from primary care computer systems into large databases. These data are then used for market and academic research. How the data is aggregated, cleaned and processed is usually opaque. Making the method transparent allows researchers to compare methods, and users of the output to better understand the strengths and weaknesses of the data.Objectives To define the stages of the process of aggregating, processing and cleaning clinical data from multiple data sources. Identify errors in design, collection, staging, integration and analysis. An eight step process defined: (1) Design (2) DATA: entry, (3) Extraction, (4) Migration, (5) Integration, (6) Cleaning, (7) Processing, and (8) Analysis. This eight step method provides a taxonomy to enable researchers to compare their methods of data process and aggregation.