Data quality and fitness for purpose of routinely collected data--a general practice case study from an electronic practice-based research network (ePBRN) (original) (raw)
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BMC Family Practice, 2015
Background: Electronic Medical Records (EMRs) are increasingly used in the provision of primary care and have been compiled into databases which can be utilized for surveillance, research and informing practice. The primary purpose of these records is for the provision of individual patient care; validation and examination of underlying limitations is crucial for use for research and data quality improvement. This study examines and describes the validity of chronic disease case definition algorithms and factors affecting data quality in a primary care EMR database. Methods: A retrospective chart audit of an age stratified random sample was used to validate and examine diagnostic algorithms applied to EMR data from the Manitoba Primary Care Research Network (MaPCReN), part of the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). The presence of diabetes, hypertension, depression, osteoarthritis and chronic obstructive pulmonary disease (COPD) was determined by review of the medical record and compared to algorithm identified cases to identify discrepancies and describe the underlying contributing factors. Results: The algorithm for diabetes had high sensitivity, specificity and positive predictive value (PPV) with all scores being over 90%. Specificities of the algorithms were greater than 90% for all conditions except for hypertension at 79.2%. The largest deficits in algorithm performance included poor PPV for COPD at 36.7% and limited sensitivity for COPD, depression and osteoarthritis at 72.0%, 73.3% and 63.2% respectively. Main sources of discrepancy included missing coding, alternative coding, inappropriate diagnosis detection based on medications used for alternate indications, inappropriate exclusion due to comorbidity and loss of data. Conclusions: Comparison to medical chart review shows that at MaPCReN the CPCSSN case finding algorithms are valid with a few limitations. This study provides the basis for the validated data to be utilized for research and informs users of its limitations. Analysis of underlying discrepancies provides the ability to improve algorithm performance and facilitate improved data quality.
Aims & rationale: Current health reform emphasise the importance of EHRs in supporting integration of care and quality improvement. Quantifying, understanding and improving the quality of routinely collected clinical information in EHRs is crucial if it is to support clinical care, monitor safety and quality and be used for research and quality improvement. The ePBRN in South Western Sydney routinely examines the quality of EHR information in participating general practices, including completeness and duplication of records. Feedback to the practices aims to improve the quality of information in the clinical record. Methods: Clinical information with no identifying information is extracted from EHRs to a secure data repository. Records are linked to identify duplicates and generate automated reports of clinical and data quality information for the practices every 3 months. The reports are also discussed with the researchers. Findings: Completeness of birth date (%) and gender (%)are...
The Electronic Primary Care Research Network (EPCRN): a New Era In Practice-Based Research
… Journal of the American Board of …, 2006
The electronic Primary Care Research Network (ePCRN) is an electronic infrastructure that facilitates the conduct of randomized controlled trials (RCTs) in primary care and promotes the translation of research findings into practice. It provides a highly secure, Internet-based electronic infrastructure that will enable primary care practices anywhere in the United States to link with researchers in academic centers or the National Institutes of Health (NIH) to facilitate recruitment, entry, and follow-up of participants in multidisciplinary RCTs. The ePCRN also establishes a standardized clinic-based registry using distributed database technology to promote the translation of research findings into practice and to facilitate the process of clinical trials recruitment. The overall goals of the ePCRN are to provide the ability to perform large national collaborative studies throughout the United States, improve efficiency and reduce costs for individual trials, provide easier access for data retrieval and analysis, and involve primary care practices in the discovery and the translation of research findings into practice. (J Am Board Fam Med 2006;19:93-7.
Journal of Nursing Care Quality, 2007
This research describes implementation strategies used by primary care practices using electronic medical records in a national quality improvement demonstration project, Accelerating Translation of Research into Practice, conducted within the Practice Partner Research Network. Qualitative methods enabled identification of strategies to improve 36 quality indicators. Quantitative survey results provide mean scores reflecting the integration of these strategies by practices. Nursing staff plays important roles to facilitate quality improvement within collaborative primary care practices.
HRB Open Research
Background: High quality data should be a key resource for research and planning of healthcare, but low quality general practice data has been documented internationally. This study assessed the feasibility of collecting reliable chronic disease data in Irish general practice, using a program of training and feedback to improve the quality of coding for chronic conditions in practice information systems. Methods: Training in chronic disease coding and reporting was provided to a purposive sample of general practices in Ireland. From July to December 2020, practices reported the number of patients receiving free medical care, and the number of patients coded with each of eight chronic conditions: type 2 diabetes mellitus (T2DM), asthma, chronic obstructive pulmonary disease (COPD), ischaemic heart disease (IHD), heart failure (HF), atrial fibrillation (ATF), transient ischaemic attack (TIA) and cerebrovascular accident/stroke (CVA). Calculated prevalences were compared with national ...
BMC Medical Informatics and Decision Making, 2020
Background: In the UK, several initiatives have resulted in the creation of local data warehouses of electronic patient records. Originally developed for commissioning and direct patient care, they are potentially useful for research, but little is known about them outside their home area. We describe one such local warehouse, the Whole Systems Integrated Care (WSIC) database in NW London, and its potential for research as the "Discover" platform. We compare Discover with the Clinical Practice Research Datalink (CPRD), a popular UK research database also based on linked primary care records. Methods: We describe the key features of the Discover database, including scope, architecture and governance; descriptive analyses compare the population demographics and chronic disease prevalences with those in CPRD. Results: As of June 2019, Discover held records for a total of 2.3 million currently registered patients, or 95% of the NW London population; CPRD held records for over 11 million. The Discover population matches the overall agesex distribution of the UK and CPRD but is more ethnically diverse. Most Discover chronic disease prevalences were comparable to the national rates. Unlike CPRD, Discover has identifiable care organisations and postcodes, allowing mapping and linkage to healthcare provider variables such as staffing, and includes contacts with social, community and mental health care. Discover also includes a consent-to-contact register of over 3000 volunteers to date for prospective studies. Conclusions: Like CPRD, Discover has been a number of years in the making, is a valuable research tool, and can serve as a model for other areas developing similar data warehouses.
einstein (São Paulo), 2019
Data collection for clinical research can be difficult, and electronic health record systems can facilitate this process. The aim of this study was to describe and evaluate the secondary use of electronic health records in data collection for an observational clinical study. We used Cerner Millennium®, an electronic health record software, following these steps: (1) data crossing between the study’s case report forms and the electronic health record; (2) development of a manual collection method for data not recorded in Cerner Millennium®; (3) development of a study interface for automatic data collection in the electronic health records; (4) employee training; (5) data quality assessment; and (6) filling out the electronic case report form at the end of the study. Three case report forms were consolidated into the electronic case report form at the end of the study. Researchers performed daily qualitative and quantitative analyses of the data. Data were collected from 94 patients. In the first case report form, 76.5% of variables were obtained electronically, in the second, 95.5%, and in the third, 100%. The daily quality assessment of the whole process showed complete and correct data, widespread employee compliance and minimal interference in their practice. The secondary use of electronic health records is safe and effective, reduces manual labor, and provides data reliability. Anesthetic care and data collection may be done by the same professional.
Information systems: the key to evidence-based health practice
Bulletin of the World Health Organization, 2000
Increasing prominence is being given to the use of best current evidence in clinical practice and health services and programme management decision-making. The role of information in evidence-based practice (EBP) is discussed, together with questions of how advanced information systems and technology (IS&T) can contribute to the establishment of a broader perspective for EBP. The author examines the development, validation and use of a variety of sources of evidence and knowledge that go beyond the well-established paradigm of research, clinical trials, and systematic literature review. Opportunities and challenges in the implementation and use of IS&T and knowledge management tools are examined for six application areas: reference databases, contextual data, clinical data repositories, administrative data repositories, decision support software, and Internet-based interactive health information and communication. Computerized and telecommunications applications that support EBP follow a hierarchy in which systems, tasks and complexity range from reference retrieval and the processing of relatively routine transactions, to complex "data mining" and rule-driven decision support systems.
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.