Exploring practical approaches to maximising data quality in electronic healthcare records in the primary care setting and associated benefits. Report of panel-led discussion held at SAPC in July 2014

Dungey, Sheena; Glew, Simon; Heyes, Barbara; Macleod, J. J. R.; Tate, A. Rosemary

doi:10.1017/s1463423615000596

Cited by 3 publications

(3 citation statements)

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“…32 One solution suggested to increase data quality in medical records has been to make data more accessible to patients which might act as an impetus to increase data quality. 33 The dynamic nature of carerecording 33 together with the adoption of new approaches will hopefully lead to the patient interval being recorded in a more standardised way.…”

Section: Comparison With Existing Literaturementioning

confidence: 99%

Validation of self-reported help-seeking, and measurement of the patient interval, for cancer symptoms: an observational study to inform methodological challenges in symptomatic presentation research

et al. 2019

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Background To improve earlier presentation with potential symptoms of cancer, accurate data are needed on how people respond to these symptoms. It is currently unclear how self-reported medical help-seeking for symptoms associated with cancer by people from the community correspond to what is recorded in their general practice records, or how well the patient interval (time from symptom onset to first presentation to a health-professional) can be estimated from patient records. Method Data from two studies that reviewed general practice electronic records of residents in Scotland, (i) the ‘Useful Study’: respondents to a general population survey who reported experiencing symptoms potentially associated with one of four common cancers (breast, colorectal, lung and upper gastro-intestinal) and (ii) the ‘Detect Cancer Early’ programme: cancer patients with one of the same four cancers. Survey respondents’ self-reported help-seeking (yes/no) was corroborated; Cohen’s Kappa assessed level of agreement. Combined data on the patient interval were evaluated using descriptive analysis. Results ‘Useful Study’ respondents’ self-report of help-seeking showed exact correspondence with general practice electronic records in 72% of cases (n = 136, kappa 0.453, moderate agreement). Between both studies, 1269 patient records from 35 general practices were reviewed. The patient interval could not be determined in 44% (n = 809) of symptoms presented by these individuals. Conclusions Patient self-report of help-seeking for symptoms potentially associated with cancer offer a reasonably accurate method to research responses to these symptoms. Incomplete patient interval data suggest routine general practice records are unreliable for measuring this important part of the patient’s symptom journey.

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Section: Comparison With Existing Literaturementioning

confidence: 99%

Validation of self-reported help-seeking, and measurement of the patient interval, for cancer symptoms: an observational study to inform methodological challenges in symptomatic presentation research

et al. 2019

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“…This has shifted the traditional use of medical records as an aide-memoire to that of a data collection system [6]. Yet the nature of the data that a primary health care practitioner requires for the care of patients can differ from what is needed for other purposes, for example, research [7]. Therefore, the overall assessment of the quality of these data can vary depending on their intended use.…”

Section: Introductionmentioning

confidence: 99%

A basic model for assessing primary health care electronic medical record data quality

Terry

Stewart

Cejic

et al. 2019

BMC Med Inform Decis Mak

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BackgroundThe increased use of electronic medical records (EMRs) in Canadian primary health care practice has resulted in an expansion of the availability of EMR data. Potential users of these data need to understand their quality in relation to the uses to which they are applied. Herein, we propose a basic model for assessing primary health care EMR data quality, comprising a set of data quality measures within four domains. We describe the process of developing and testing this set of measures, share the results of applying these measures in three EMR-derived datasets, and discuss what this reveals about the measures and EMR data quality. The model is offered as a starting point from which data users can refine their own approach, based on their own needs.MethodsUsing an iterative process, measures of EMR data quality were created within four domains: comparability; completeness; correctness; and currency. We used a series of process steps to develop the measures. The measures were then operationalized, and tested within three datasets created from different EMR software products.ResultsA set of eleven final measures were created. We were not able to calculate results for several measures in one dataset because of the way the data were collected in that specific EMR. Overall, we found variability in the results of testing the measures (e.g. sensitivity values were highest for diabetes, and lowest for obesity), among datasets (e.g. recording of height), and by patient age and sex (e.g. recording of blood pressure, height and weight).ConclusionsThis paper proposes a basic model for assessing primary health care EMR data quality. We developed and tested multiple measures of data quality, within four domains, in three different EMR-derived primary health care datasets. The results of testing these measures indicated that not all measures could be utilized in all datasets, and illustrated variability in data quality. This is one step forward in creating a standard set of measures of data quality. Nonetheless, each project has unique challenges, and therefore requires its own data quality assessment before proceeding.Electronic supplementary materialThe online version of this article (10.1186/s12911-019-0740-0) contains supplementary material, which is available to authorized users.

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“… 2–4 Characteristics of the healthcare system also impact the availability of data: data are not shared across many healthcare settings, leading to data that are more representative of the type of providers or healthcare capabilities than of the health of the patient. 5 , 6 Alternatively, care fragmentation leads to duplicative efforts with resulting poor data quality. 7 Finally, there are community- and patient-level factors that impact access to care (and, consequentially, inclusion in the EHR population), including financial security, transportation capabilities, distance to care, and a number of other social determinants of health.…”

Section: Introductionmentioning

confidence: 99%

Healthcare utilization is a collider: an introduction to collider bias in EHR data reuse

Weiskopf

Dorr

Jackson

et al. 2023

Journal of the American Medical Informatics Association

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Objectives Collider bias is a common threat to internal validity in clinical research but is rarely mentioned in informatics education or literature. Conditioning on a collider, which is a variable that is the shared causal descendant of an exposure and outcome, may result in spurious associations between the exposure and outcome. Our objective is to introduce readers to collider bias and its corollaries in the retrospective analysis of electronic health record (EHR) data. Target audience Collider bias is likely to arise in the reuse of EHR data, due to data-generating mechanisms and the nature of healthcare access and utilization in the United States. Therefore, this tutorial is aimed at informaticians and other EHR data consumers without a background in epidemiological methods or causal inference. Scope We focus specifically on problems that may arise from conditioning on forms of healthcare utilization, a common collider that is an implicit selection criterion when one reuses EHR data. Directed acyclic graphs (DAGs) are introduced as a tool for identifying potential sources of bias during study design and planning. References for additional resources on causal inference and DAG construction are provided.

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Exploring practical approaches to maximising data quality in electronic healthcare records in the primary care setting and associated benefits. Report of panel-led discussion held at SAPC in July 2014

Abstract: The importance of good high quality electronic healthcare records has been set forth along with the need for a practical user-considered and collaborative approach to its improvement.

Cited by 3 publications

References 1 publication

Validation of self-reported help-seeking, and measurement of the patient interval, for cancer symptoms: an observational study to inform methodological challenges in symptomatic presentation research

Validation of self-reported help-seeking, and measurement of the patient interval, for cancer symptoms: an observational study to inform methodological challenges in symptomatic presentation research

A basic model for assessing primary health care electronic medical record data quality

Healthcare utilization is a collider: an introduction to collider bias in EHR data reuse

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