Design and implementation of data collection and linkage of electronic health records in a large UK cluster-randomised trial of asthma management (ARRISA-UK).

Family practitioners and other staff working in primary care require comprehensive and accurate data on patients at the point-of-care if they are to provide high quality health services to their patients. Electronic patient records are an effective method of achieving this objective, by dispensing with the need to use difficult to access, and often illegible, paper-based records. Hence, the implementation of electronic patient records in primary care is a key objective of many health care systems, including both the USA and UK. This reflects a growing recognition of the potential benefits of electronic records on the safety, quality and efficiency of healthcare. Electronic patient records underpin many information technology initiatives in primary care, such as screening for identifying patients at high risk of cardiovascular disease, call–recall systems for asthma and other long-term disease management programmes, computerized decision support systems for prescribing, electronic ordering of tests and electronic referral systems to secondary care. These are all, however, dependant on comprehensive and accurate coded data. There are known to be large variations in the accuracy and completeness of the clinical information stored in electronic patient records. In a systematic review, Thiru et al. identified 52 studies that examined data quality in electronic primary care records. Quality of data was measured in different ways, most commonly by comparisons of rates derived from the electronic records with an external standard. Prescriptions had the highest rate of recording, probably because prescribing is a core function of many electronic patient record systems. The recording of diseases (i.e. diagnoses) varied, with completeness generally highest for diseases with clear diagnostic criteria. Lifestyle and socio-economic data had lower rates of recording than prescription or diagnostic data. In another systematic review, Jordan et al. identified 24 studies that examined morbidity coding in primary care. Recording of consultations was generally high (typically greater than 90%), but assigning a morbidity code during each consultation was more variable (66–99% complete). Coronary heart disease was the most commonly assessed disease register in previous studies and completeness of recording was generally moderate (typically around 70%). Positive predictive value of coronary heart disease registers was generally high (typically around 83–100%). Other diseases that were examined (such as asthma and epilepsy) showed similar patterns of completeness of recording and positive predictive value of recorded diagnoses, but rates were generally lower than for coronary heart disease. Two recent papers in Family Practice also look at the issues of recording and coding of data in primary care. Pascoe et al. identified major omissions in the cancer diagnoses held by five general practices in Leeds, UK. The recording of diagnoses in primary care was less complete and, when a diagnosis of cancer was recorded, it was generally less detailed than in the data held by the Regional Cancer Registry. Soler et al. describe the progress of the International Classification of Primary Care (ICPC) in the 21 years since its introduction. The classification, now endorsed by the World Health Organization, has been translated into 22 languages. The wide use of the ICPC facilitates international comparisons of clinical practice and coding in primary care. For the time being, however, the use of Read codes as the UK’s standard classification system in primary care makes comparisons with countries using ICPC difficult. One important conclusion of previous studies on the use of electronic patient records in primary care is that the completeness and accuracy of data entry relies mainly on the enthusiasm of family practitioners. There are currently no agreed reference standards for reporting data quality in primary care and this limits measurement of data quality in electronic patient records. Clinicians do understand the potential benefits from the use of electronic patient records in their practices, but also cite major barriers to their implementation. These include the capital cost of investment in information technology (this may be less of an issue in the UK where the capital costs are largely met by the NHS) and the workload implications. A second key area is the lack of standards that permit effective, accurate

To explore higher education institution (HEI) perspectives on the development and implementation of trainee nursing associates (NAs) in the primary care workforce in England. Current shortages of primary health care staff have led to innovative skill mix approaches in attempts to maintain safe and effective care. In England, a new level of nursing practice, NAs, was introduced and joined the workforce in 2019. This role was envisaged as a way of bridging the skills gap between health care assistants and registered nurses and as an alternative route into registered nursing. However, there is limited evidence on programme development and implementation of trainee NAs within primary care settings and HEI perspectives on this. This paper draws from a larger qualitative study of HEI perspectives on the trainee NA programme. Twenty-seven staff involved in training NAs, from five HEIs across England, were interviewed from June to September 2021. The interview schedule specifically included questions relating to primary care. Data relating to primary care were extracted and analysed using a combined framework and thematic analysis approach. Three themes were developed: 'Understanding the trainee role and requirements', 'Trainee support in primary care' and 'Skills and scope of practice'. It is apparent that a more limited understanding of the NA programme requirements can lead to difficulties in accessing the right support for trainees in primary care. This can create challenges for trainees in gaining the required competencies and uncertainty in understanding what constitutes a safe scope of practice within the role for both employers and trainees. It might be anticipated that as this new programme becomes more embedded in primary care, a greater understanding will develop, support will improve and the nature and scope of this new level of practice will become clearer.

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The author calls attention to developing changes in medical training that may provide an appropriate setting for overcoming the dichotomous view of mind and body, of psychological medicine and "scientific medicine." Primary care training programs that include liaison psychiatry may be the vehicle for solving this persistent problem.

The aim of this thesis was to examine the safety of maternal influenza vaccination with respect to major congenital malformations in live-born infants. UK electronic health records from the Clinical Practice Research Datalink were used and work was conducted using linked primary care, hospitalisation and mortality data. The first study systematically reviewed existing methods for identifying congenital malformations in UK electronic health records, and the results of any validation studies. Studies relied on stand-alone primary care or hospitalisation data to identify congenital malformations; none examined linkage between these. Overall, congenital malformations recorded in primary care data had a high positive predictive value (80-100%) but the validity in hospitalisation data was not explored. Methods from these studies informed the development of a comprehensive algorithm to identify major malformations in live-born infants. Using linked primary care, hospitalisation and mortality data, the second study in this thesis demonstrated that just 20% (95% CI, 19-21) of infants with a major malformation had evidence of their condition in both primary care and hospitalisation data. Almost 65% (95% CI, 64-66) only had evidence in hospitalisation data. The third study demonstrated that the overall prevalence of major malformations established in primary care data using this algorithm was slightly higher than published estimates from other studies using UK primary care records (Prevalence ratio, 1.2; 95% CI, 1.2-1.3). Comparisons of linked data with population-based registry data demonstrated a four-fold higher prevalence for major malformations overall in the linked electronic health records (Prevalence ratio, 4.3; 95% CI, 4.1-4.5). This was primarily driven by the high prevalence of some of these conditions in hospitalisation data, which could potentially be explained by nonspecific codes used to record certain malformations that could have related to either major or minor conditions. 5 The fourth study examined the association between the trivalent seasonal inactivated influenza vaccine and major malformations. Among 78,150 live-birth pregnancies, 6,872 (8.8%) were vaccinated in the first trimester whilst 46,669 (59.7%) were unvaccinated throughout pregnancy. There was no evidence to suggest an association between first-trimester vaccination and major malformations recorded in first year of infant life in models adjusted for confounding (HR, 1.06; 99% CI, 0.94-1.19; p=0.23). The fifth study, which examined the safety of the monovalent pandemic inactivated influenza vaccine, showed similar results (HR, 1.02; 99% CI, 0.72-1.46; p=0.86). However, although these vaccine safety studies did not find evidence for an association between vaccination and major malformations, terminations due to foetal anomaly were not included. Therefore, the possibility of an increased risk of the specific subtypes of major malformations typically detected during antenatal scans and subsequently terminated could not be discounted. These results provide additional evidence on the safety of maternal influenza vaccination but highlight the need for further explorations of major malformations among pregnancies that do not result in live-births. The component of this work relating to the methods used to identify major malformations highlights the potential to increase ascertainment through the use of linked data whilst underscoring the need for further studies, particularly in hospitalisation data, to establish the validity of codes used to record these conditions.

ObjectivesWe aimed to use a large dataset to compare self-reported and primary care measures of insomnia symptom prevalence in England and establish whether they identify participants with similar characteristics.DesignCross-sectional study...

Nordic countries have a long-standing tradition of using administrative healthcare data from specialist and hospital settings to study disease frequency and etiological associations. While similar data are increasingly available elsewhere, large-scale primary care data remain limited. Understanding how data source affects disease identification is therefore critical. We conducted a register-based study including all individuals aged ≥ 60 years residing in Region Stockholm, Sweden, from 2017 to 2022. ICD-10 codes from primary care, specialist outpatient, and hospital care were grouped into 60 disease categories. One-year cumulative incidence and prevalence were estimated and compared. Disease patterns were broadly similar across care settings, with hypertension being the most common diagnosis. However, underestimation was generally greater for incidence than for prevalence. Hospital and specialist care data primarily captured acute and severe conditions, e.g., stroke, ischemic heart disease, falls, and cancer, while primary care data more effectively identified chronic risk factors such as diabetes, hypertension, hyperlipidemia, and psychiatric disorders, particularly for incident cases. Age-related differences in underestimation varied by disease but showed no consistent pattern. Primary, specialist, and hospital care data each capture distinct aspects of the disease landscape in older adults. Excluding primary care data leads to systematic underestimation of many common and chronic conditions, especially for newly diagnosed cases. This study provides guidance for epidemiological research using administrative health registers and highlights the importance of integrating data from multiple levels of care to improve the accuracy of disease burden estimates in epidemiological research using administrative health registers.

BackgroundDistinct asthma phenotypes have previously been suggested, including benign asthma, atopic asthma and obese non-eosinophilic asthma. This study aims to establish if these phenotypes can be identified using data recorded in primary care clinical records and reports on patient characteristics and exacerbation frequency.MethodsA population-based cohort study identified 193,999 asthma patients in UK primary care from 2007 to 2017. We used linked primary and secondary care data from the Clinical Practice Research Datalink, Hospital Episode Statistics and Office for National Statistics. Patients were classified into predefined phenotypes or included in an asthma “not otherwise specified” (NOS) group. We used negative binomial regression to calculate the exacerbation rates and adjusted rate ratios. Rate ratios were further stratified by asthma treatment step.ResultsIn our cohort, 3.9% of patients were categorized as benign asthma, 28.6% atopic asthma and 4.8% obese non-eosinophilic asthma. About 62.7% of patients were asthma NOS, including asthma NOS without treatment (10.4%), only on short-acting beta agonist (6.1%) and on maintenance treatment (46.2%). Crude severe exacerbation rates per 1,000 person-years were lowest for benign asthma (106.8 [95% CI: 101.2–112.3]) and highest for obese non-eosinophilic asthma (469.0 [451.7–486.2]). Incidence rate ratios for all phenotype groups decreased when stratified by treatment step but remained raised compared to benign asthma.ConclusionEstablished phenotypes can be identified in a general asthma population, although many patients did not fit into the specific phenotypes which we studied. Phenotyping patients and knowledge of asthma treatment step could help anticipate clinical course and therefore could aid clinical management but is only possible in a minority of primary care patients based on current phenotypes and electronic health records (EHRs).

Demand for urgent and emergency health care in England has grown over the last decade, for reasons that are not clear. Changes in population demographics may be a cause. This study investigated associations between individuals' characteristics (including socioeconomic deprivation and long term health conditions (LTC)) and the frequency of emergency department (ED) attendances, in the Norfolk and Waveney subregion of the East of England. The study population was people who were registered with 91 of 106 Norfolk and Waveney general practices during one year from 1 April 2022 to 31 March 2023. Linked primary and secondary care and geographical data included each individual's sociodemographic characteristics, and number of ED attendances during the same year and, for some individuals, LTCs and number of general practice (GP) appointments. Associations between these factors and ED attendances were estimated using Poisson regression models. 1,027,422 individuals were included of whom 57.4% had GP data on the presence or absence of LTC, and 43.1% had both LTC and general practitioner appointment data. In the total population ED attendances were more frequent in individuals aged under five years, (adjusted Incidence Rate Ratio (IRR) 1.25, 95% confidence interval 1.23 to 1.28) compared to 15-35 years); living in more socioeconomically deprived areas (IRR 0.61 (0.60 to 0.63)) for least deprived compared to most deprived,and living closer to the nearest ED. Among individuals with LTC data, each additional LTC was also associated with increased ED attendances (IRR 1.16 (1.15 to 1.16)). Among individuals with LTC and GP appointment data, each additional GP appointment was also associated with increased ED attendances (IRR 1.03 (1.026 to 1.027)). In the Norfolk and Waveney population, ED attendance rates were higher for young children and individuals living in more deprived areas and closer to EDs. In individuals with LTC and GP appointment data, both factors were also associated with higher ED attendance.

Asthma diagnosis and treatment: Filling in the information gaps

ObjectiveTo validate primary and secondary care codes in electronic health records to identify people receiving chronic kidney replacement therapy based on gold standard registry data.DesignValidation study using data from OpenSAFELY...

ObjectivesDevelop a novel algorithm to categorise alcohol consumption using primary care electronic health records (EHRs) and asses its reliability by comparing this classification with self-reported alcohol consumption data obtained from...

Changing the health care workforce: lessons from foundation-sponsored programs.

Background. Quality management, evaluation, and surveillance of asthma may be enhanced by access to and utilization of an asthma electronic medical record (EMR) in primary care. Purpose. To describe the current status, support tools, and utility of asthma EMRs in primary care. Methods. An integrative review of the literature published between 1996 and 2008 was completed using Ovid MEDLINE, EMBASE, and CINAHL databases. Key search terms included asthma, medical records, computerized, primary health care, primary care, family physician, family practice, chronic disease, COPD, neoplasm, diabetes mellitus, and cardiovascular disease. Articles related to concepts, systems in development, and sources such as acute care and pharmacy EMRs were excluded. Each article was reviewed by two reviewers. Results. Of 309 articles identified, 76 met the inclusion criteria. Twenty-two percent were specific to asthma, 78% pertained to other chronic diseases and/or the overall status of an EMR in primary care. The literature varied in methodology, topics of discussion and value of data. Articles describing an asthma EMR most often reported on decision support tools (n = 3) and/or utility (n = 14), specifically the ability to predict mortality and assess severity and timeliness of diagnosis. A primary care EMR containing a validated asthma minimum data set was not found. Three themes emerged from the review: status (description of users, functionalities and adoption issues), tools (decision support tools to enhance knowledge uptake), and utility (data quality, extraction and outcomes). Conclusions. There is a paucity of asthma elements in EMRs in primary care, with the exception of discussion of decision support tools and utility. Integration of a more robust asthma EMR in primary care, including a minimum data set, standardized terminology, and validated indicators, may further enhance care and enable outcomes monitoring.

Moving Beyond Tools and Building Bridges: Lessons Learned From a CKD Decision Support in Primary Care