Dataset Information

Data-driven discovery of changes in clinical code usage over time: a case-study on changes in cardiovascular disease recording in two English electronic health records databases (2001-2015).

ABSTRACT: OBJECTIVES:To demonstrate how data-driven variability methods can be used to identify changes in disease recording in two English electronic health records databases between 2001 and 2015. DESIGN:Repeated cross-sectional analysis that applied data-driven temporal variability methods to assess month-by-month changes in routinely collected medical data. A measure of difference between months was calculated based on joint distributions of age, gender, socioeconomic status and recorded cardiovascular diseases. Distances between months were used to identify temporal trends in data recording. SETTING:400 English primary care practices from the Clinical Practice Research Datalink (CPRD GOLD) and 451 hospital providers from the Hospital Episode Statistics (HES). MAIN OUTCOMES:The proportion of patients (CPRD GOLD) and hospital admissions (HES) with a recorded cardiovascular disease (CPRD GOLD: coronary heart disease, heart failure, peripheral arterial disease, stroke; HES: International Classification of Disease codes I20-I69/G45). RESULTS:Both databases showed gradual changes in cardiovascular disease recording between 2001 and 2008. The recorded prevalence of included cardiovascular diseases in CPRD GOLD increased by 47%-62%, which partially reversed after 2008. For hospital records in HES, there was a relative decrease in angina pectoris (-34.4%) and unspecified stroke (-42.3%) over the same time period, with a concomitant increase in chronic coronary heart disease (+14.3%). Multiple abrupt changes in the use of myocardial infarction codes in hospital were found in March/April 2010, 2012 and 2014, possibly linked to updates of clinical coding guidelines. CONCLUSIONS:Identified temporal variability could be related to potentially non-medical causes such as updated coding guidelines. These artificial changes may introduce temporal correlation among diagnoses inferred from routine data, violating the assumptions of frequently used statistical methods. Temporal variability measures provide an objective and robust technique to identify, and subsequently account for, those changes in electronic health records studies without any prior knowledge of the data collection process.

SUBMITTER: Rockenschaub P

PROVIDER: S-EPMC7045100 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Data-driven discovery of changes in clinical code usage over time: a case-study on changes in cardiovascular disease recording in two English electronic health records databases (2001-2015).

Rockenschaub Patrick P Nguyen Vincent V Aldridge Robert W RW Acosta Dionisio D García-Gómez Juan Miguel JM Sáez Carlos C

BMJ open 20200213 2

<h4>Objectives</h4>To demonstrate how data-driven variability methods can be used to identify changes in disease recording in two English electronic health records databases between 2001 and 2015.<h4>Design</h4>Repeated cross-sectional analysis that applied data-driven temporal variability methods to assess month-by-month changes in routinely collected medical data. A measure of difference between months was calculated based on joint distributions of age, gender, socioeconomic status and recorde ...[more]

PMID: 32060159

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Project description:BackgroundRoutine weight recording in electronic health records (EHRs) could assist general practitioners (GPs) in the identification, prevention, and management of overweight patients. However, the extent to which weight management is embedded in general practice in the Netherlands has not been investigated. The purpose of this study was to evaluate the frequency of weight recording in general practice in the Netherlands for patients who self-reported as being overweight. The specific objectives of this study were to assess whether weight recording varied according to patient characteristics, and to determine the frequency of weight recording over time for patients with and without a chronic condition related to being overweight.MethodsBaseline data from the Occupational and Environmental Health Cohort Study (2012) were combined with data from EHRs of general practices (2012-2015). Data concerned 3446 self-reported overweight patients who visited their GP in 2012, and 1516 patients who visited their GP every year between 2012 and 2015. Logistic multilevel regression analyses were performed to identify associations between patient characteristics and weight recording.ResultsIn 2012, weight was recorded in the EHRs of a quarter of patients who self-reported as being overweight. Greater age, lower education level, higher self-reported body mass index, and the presence of diabetes mellitus, chronic obstructive pulmonary disease, and/or cardiovascular disorders were associated with higher rates of weight recording. The strongest association was found for diabetes mellitus (adjusted OR = 10.3; 95% CI [7.3, 14.5]). Between 2012 and 2015, 90% of patients with diabetes mellitus had at least one weight measurement recorded in their EHR. In the group of patients without a chronic condition related to being overweight, this percentage was 33%.ConclusionsWeight was frequently recorded for overweight patients with a chronic condition, for whom regular weight measurement is recommended in clinical guidelines, and for which weight recording is a performance indicator as part of the payment system. For younger patients and those without a chronic condition related to being overweight, weight was less frequently recorded. For these patients, routine recording of weight in EHRs deserves more attention, with the aim to support early recognition and treatment of overweight.

Project description:BackgroundAlcohol misuse is an important cause of premature disability and death. While clinicians are recommended to ask patients about alcohol use and provide brief interventions and specialist referral, this is poorly implemented in routine practice. We undertook a national consultation to ascertain the appropriateness of proposed standards for recording information about alcohol use in electronic health records (EHRs) in the UK and to identify potential barriers and facilitators to their implementation in practice.MethodsA wide range of stakeholders in the UK were consulted about the appropriateness of proposed information standards for recording alcohol use in EHRs via a multi-disciplinary stakeholder workshop and online survey. Responses to the survey were thematically analysed using the Consolidated Framework for Implementation Research.ResultsThirty-one stakeholders participated in the workshop and 100 in the online survey. This included patients and carers, healthcare professionals, researchers, public health specialists, informaticians, and clinical information system suppliers. There was broad consensus that the Alcohol Use Disorders Identification Test (AUDIT) and AUDIT-Consumption (AUDIT-C) questionnaires were appropriate standards for recording alcohol use in EHRs but that the standards should also address interventions for alcohol misuse. Stakeholders reported a number of factors that might influence implementation of the standards, including having clear care pathways and an implementation guide, sharing information about alcohol use between health service providers, adequately resourcing the implementation process, integrating alcohol screening with existing clinical pathways, having good clinical information systems and IT infrastructure, providing financial incentives, having sufficient training for healthcare workers, and clinical leadership and engagement. Implementation of the standards would need to ensure patients are not stigmatised and that patient confidentiality is robustly maintained.ConclusionsA wide range of stakeholders agreed that use of AUDIT-C and AUDIT are appropriate standards for recording alcohol use in EHRs in addition to recording interventions for alcohol misuse. The findings of this consultation will be used to develop an appropriate information model and implementation guide. Further research is needed to pilot the standards in primary and secondary care.

Dataset Information

Data-driven discovery of changes in clinical code usage over time: a case-study on changes in cardiovascular disease recording in two English electronic health records databases (2001-2015).

Publications

Data-driven discovery of changes in clinical code usage over time: a case-study on changes in cardiovascular disease recording in two English electronic health records databases (2001-2015).

Similar Datasets

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