An eight-step method for assessing diagnostic data quality in practice: chronic obstructive pulmonary disease as an exemplar

Background Chronic obstructive pulmonary disease (COPD) is an important cause of mortality and morbidity. Its management is shifting from the secondary to the primary care setting. The quality of data is known to vary between practices, and individual practices need to be able to assess their data quality. Objectives To measure the quality of diagnostic data in COPD. Subjects 10 975 patients registered with a computerized general practice in the south of England, and 190 patients likely to have COPD. Methods An eight-step method was developed: (1) research the expected prevalence of the diagnosis and define audit criteria; (2) find out how the diagnosis might be coded – look at the terminology and the codes presented by the computer interface; (3) examine the characteristics of the practice population; (4) calculate the prevalence and infer its reliability; (5) investigate the completeness; (6) accuracy; (7) currency and consistency; and (8) calculate sensitivity and positive predictive value of the data. Results The prevalence of COPD in the literature ranges between 3% and 10%. The coding for bronchitis and COPD is complex and it is easy to select an incorrect code. The test population is younger but of similar social class to the national average. The prevalence of COPD in this study was 1.3%. The data were incomplete and some were inaccurate; patients with COPD had to be identified from additional searches. The sensitivity of the use of the diagnostic code was 79%, and the positive predictive value 75.3%. Conclusions The method provides a tool to help practices and localities assess their diagnostic data quality

Lessons from the implementation of a near patient anticoagulant monitoring service in primary care

Objective To evaluate the implementation of a primary care, nurse-led, near patient anticoagulant monitoring service. Design Action research workshops, supported by questionnaires and clinical audit, to define the strengths and weaknesses of the service and the effectiveness of the computerised decision support system used to set the dosage of anticoagulant and time interval to the next appointment. Setting 13 general practices that implemented anticoagulant monitoring in a primary care organisation in south east England. Participants 18 practice nurses, 72% of whom had over 20 years’ clinical experience; the universitybased investigators and managers from the primary care organisation. Main outcome measure The nurses felt that the patients preferred the practice-based service, finding it more personal and accessible. However, circumstances arose where the nurse’s intuition had to override the software’s advice. The nurses found it stressful when they were unclear whether their decision making represented acceptable variation or dangerous practice. An audit tool was developed to measure the extent to which there was variation from the software’s recommendation, and patterns of variation emerged. Most evident was that nurses responded to uncertainty by practising cautiously, shortening the interval until the next visit and slightly reducing the recommended dose of warfarin. Conclusions The group, by sharing their experiences through a structured series of workshops, developed an understanding of when it might be appropriate to vary from the decision support software’s recommendations and how this could be audited. The technological solution modelled on hospital practice proved hard to implement in primary care

Comprehensive computerised primary care records are an essential component of any national health information strategy: report from an international consensus conference

In many countries, primary care informatics has developed to the point that it is recognised as an important enabler of quality improvement; this has not occurred to date in the United States. With this conference, we aimed to build an international consensus as to whether primary care has unique characteristics that require an informatics subspecialty; and, if so, to establish the role of an international audience of 53 health informaticians, mostly working in primary care. There was consensus among the participants that primary care has many unique characteristics that justify the existence of an informatics subspecialty: primary care informatics (PCI). The conference identified principles and practical examples of: (1) the effective deployment of information technology to underpin the provision of records, communication and access to information; (2) the need to harness the extensive knowledge base about the practice of PCI; and (3) the contribution of the primary care informatics in improving patient care, and to enable its recognition in the national strategy. The conference was organised by the primary care informatics working groups of AMIA, EFMI, IMIA and Wonca and took place at Medinfo 2004 in San Francisco. It consisted of two plenary lectures, two small-group work sessions and a panel discussion to summarise the day. It was attended by experimental work and theory that underpins the science of PCI. These principles and examples of their practical application were largely derived from the extensive knowledge base which has been built up in countries that have developed PCI over the last one to two decades

Problems with primary care data quality: osteoporosis as an exemplar

Objective To report problems implementing a data quality programme in osteoporosis. Design Analysis of data extracted using Morbidity Information Query and Export Syntax (MIQUEST) from participating general practices’ systems and recommendations of practitioners who attended an action research workshop. Setting Computerised general practices using different Read code versions to record structured data. Participants 78 practices predominantly from London and the south east, with representation from north east, north west and south west England. Main outcome measures Patients at risk can be represented in many ways within structured data. Although fracture data exists, it is unclear which are fragility fractures. T-scores, the gold standard for measuring bone density, cannot be extracted using the UK’s standard data extraction tool, MIQUEST; instead manual searches had to be implemented. There is a hundredfold variation in data recording levels between practices. Therapy is more frequently recorded than diagnosis. A multidisciplinary forum of experienced practitioners proposed that a limited list of codes should be used. Conclusions There is variability in inter-practice data quality. Some clinically important codes are lacking, and there are multiple ways that the same clinical concept can be represented. Different practice computer systems have different versions of Read code, making some data incompatible. Manual searching is still required to find data. Clinicians with an understanding of what data are clinically relevant need to have a stronger voice in the production of codes, and in the creation of recommended lists

Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis.

OBJECTIVES: 1) To use data-driven method to examine clinical codes (risk factors) of a medical condition in primary care electronic health records (EHRs) that can accurately predict a diagnosis of the condition in secondary care EHRs. 2) To develop and validate a disease phenotyping algorithm for rheumatoid arthritis using primary care EHRs. METHODS: This study linked routine primary and secondary care EHRs in Wales, UK. A machine learning based scheme was used to identify patients with rheumatoid arthritis from primary care EHRs via the following steps: i) selection of variables by comparing relative frequencies of Read codes in the primary care dataset associated with disease case compared to non-disease control (disease/non-disease based on the secondary care diagnosis); ii) reduction of predictors/associated variables using a Random Forest method, iii) induction of decision rules from decision tree model. The proposed method was then extensively validated on an independent dataset, and compared for performance with two existing deterministic algorithms for RA which had been developed using expert clinical knowledge. RESULTS: Primary care EHRs were available for 2,238,360 patients over the age of 16 and of these 20,667 were also linked in the secondary care rheumatology clinical system. In the linked dataset, 900 predictors (out of a total of 43,100 variables) in the primary care record were discovered more frequently in those with versus those without RA. These variables were reduced to 37 groups of related clinical codes, which were used to develop a decision tree model. The final algorithm identified 8 predictors related to diagnostic codes for RA, medication codes, such as those for disease modifying anti-rheumatic drugs, and absence of alternative diagnoses such as psoriatic arthritis. The proposed data-driven method performed as well as the expert clinical knowledge based methods. CONCLUSION: Data-driven scheme, such as ensemble machine learning methods, has the potential of identifying the most informative predictors in a cost-effective and rapid way to accurately and reliably classify rheumatoid arthritis or other complex medical conditions in primary care EHRs

ADVANCE database characterisation and fit for purpose assessment for multi-country studies on the coverage, benefits and risks of pertussis vaccinations.

INTRODUCTION: The public-private ADVANCE consortium (Accelerated development of vaccine benefit-risk collaboration in Europe) aimed to assess if electronic healthcare databases can provide fit-for purpose data for collaborative, distributed studies and monitoring of vaccine coverage, benefits and risks of&nbsp;vaccines. OBJECTIVE: To evaluate if European healthcare databases can be used to estimate vaccine coverage, benefit and/or risk using pertussis-containing vaccines as an&nbsp;example. METHODS: Characterisation was conducted using open-source Java-based (Jerboa) software and R scripts. We obtained: (i) The general characteristics of the database and data source (meta-data) and (ii) a detailed description of the database population (size, representatively of age/sex of national population, rounding of birth dates, delay between birth and database entry), vaccinations (number of vaccine doses, recording of doses, pattern of doses by age and coverage) and events of interest (diagnosis codes, incidence rates). A total of nine databases (primary care, regional/national record linkage) provided data on events (pertussis, pneumonia, death, fever, convulsions, injection site reactions, hypotonic hypo-responsive episode, persistent crying) and vaccines (acellular pertussis and whole cell pertussis) related to the pertussis proof of concept&nbsp;studies. RESULTS: The databases contained data for a total population of 44 million individuals. Seven databases had recorded doses of vaccines. The pertussis coverage estimates were similar to those reported by the World Health Organisation (WHO). Incidence rates of events were comparable in magnitude and age-distribution between databases with the same characteristics. Several conditions (persistent crying and somnolence) were not captured by the databases for which outcomes were restricted to hospital discharge&nbsp;diagnoses. CONCLUSION: The database characterisation programs and workflows allowed for an efficient, transparent and standardised description and verification of electronic healthcare databases which may participate in pertussis vaccine coverage, benefit and risk studies. This approach is ready to be used for other vaccines/events to create readiness for participation in other vaccine related&nbsp;studies.</p

A case of swine influenza A(H1N2)v in England, November 2023.

Under International Health Regulations from 2005, a human infection caused by a novel influenza A virus variant is considered an event that has potential for high public health impact and is immediately notifiable to the World Health Organisation. We here describe the clinical, epidemiological and virological features of a confirmed human case of swine influenza A(H1N2)v in England detected through community respiratory virus surveillance. Swabbing and contact tracing helped refine public health risk assessment, following this unusual and unexpected finding