Dataset Information

Factors Affecting Accuracy of Data Abstracted from Medical Records.

ABSTRACT: Medical record abstraction (MRA) is often cited as a significant source of error in research data, yet MRA methodology has rarely been the subject of investigation. Lack of a common framework has hindered application of the extant literature in practice, and, until now, there were no evidence-based guidelines for ensuring data quality in MRA. We aimed to identify the factors affecting the accuracy of data abstracted from medical records and to generate a framework for data quality assurance and control in MRA.Candidate factors were identified from published reports of MRA. Content validity of the top candidate factors was assessed via a four-round two-group Delphi process with expert abstractors with experience in clinical research, registries, and quality improvement. The resulting coded factors were categorized into a control theory-based framework of MRA. Coverage of the framework was evaluated using the recent published literature.Analysis of the identified articles yielded 292 unique factors that affect the accuracy of abstracted data. Delphi processes overall refuted three of the top factors identified from the literature based on importance and five based on reliability (six total factors refuted). Four new factors were identified by the Delphi. The generated framework demonstrated comprehensive coverage. Significant underreporting of MRA methodology in recent studies was discovered.The framework generated from this research provides a guide for planning data quality assurance and control for studies using MRA. The large number and variability of factors indicate that while prospective quality assurance likely increases the accuracy of abstracted data, monitoring the accuracy during the abstraction process is also required. Recent studies reporting research results based on MRA rarely reported data quality assurance or control measures, and even less frequently reported data quality metrics with research results. Given the demonstrated variability, these methods and measures should be reported with research results.

SUBMITTER: Zozus MN

PROVIDER: S-EPMC4615628 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Factors Affecting Accuracy of Data Abstracted from Medical Records.

Zozus Meredith N MN Pieper Carl C Johnson Constance M CM Johnson Todd R TR Franklin Amy A Smith Jack J Zhang Jiajie J

PloS one 20151020 10

<h4>Objective</h4>Medical record abstraction (MRA) is often cited as a significant source of error in research data, yet MRA methodology has rarely been the subject of investigation. Lack of a common framework has hindered application of the extant literature in practice, and, until now, there were no evidence-based guidelines for ensuring data quality in MRA. We aimed to identify the factors affecting the accuracy of data abstracted from medical records and to generate a framework for data qual ...[more]

PMID: 26484762

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Project description:BackgroundDespite a substantial increase in the adoption of electronic medical records (EMRs) in primary health care settings, the use of advanced EMR features is limited. Several studies have identified both barriers and facilitating factors that influence primary care physicians' (PCPs) use of advanced EMR features and the maturation of their EMR use. The purpose of this study is to explore and identify the factors that impact PCPs' mature use of EMRs.MethodsA systematic review was conducted in accordance with the Cochrane Handbook. The MEDLINE, Embase, and PsycINFO electronic databases were searched from 1946 to June 13, 2019. Two independent reviewers screened the studies for eligibility; to be included, studies had to address factors influencing PCPs' mature use of EMRs. A narrative synthesis was conducted to collate study findings and to report on patterns identified across studies. The quality of the studies was also appraised.ResultsOf the 1893 studies identified, 14 were included in this study. Reported factors that influenced PCPs' mature use of EMRs fell into one of the following 5 categories: technology, people, organization, resources, and policy. Concerns about the EMR system's functionality, lack of physician awareness of EMR functionality, limited physician availability to learn more about EMRs, the habitual use of successfully completing clinical tasks using only basic EMR features, business-oriented organizational objectives, lack of vendor training, limited resource availability, and lack of physician readiness were reported as barriers to PCPs' mature use of EMRs. The motivation of physicians, user satisfaction, coaching and peer mentoring, EMR experience, gender, physician perception, transition planning for changes in roles and work processes, team-based care, adequate technical support and training, sharing resources, practices affiliated with an integrated delivery system, financial incentives, and policies to increase EMR use all had a favorable impact on PCPs' use of advanced EMR features.ConclusionsBy using a narrative synthesis to synthesize the evidence, we identified interrelated factors influencing the mature use of EMRs by PCPs. The findings underline the need to provide adequate training and policies that facilitate the mature use of EMRs by PCPs.Trial registrationPROSPERO CRD42019137526.

Project description:BACKGROUND:Class prediction models have been shown to have varying performances in clinical gene expression datasets. Previous evaluation studies, mostly done in the field of cancer, showed that the accuracy of class prediction models differs from dataset to dataset and depends on the type of classification function. While a substantial amount of information is known about the characteristics of classification functions, little has been done to determine which characteristics of gene expression data have impact on the performance of a classifier. This study aims to empirically identify data characteristics that affect the predictive accuracy of classification models, outside of the field of cancer. RESULTS:Datasets from twenty five studies meeting predefined inclusion and exclusion criteria were downloaded. Nine classification functions were chosen, falling within the categories: discriminant analyses or Bayes classifiers, tree based, regularization and shrinkage and nearest neighbors methods. Consequently, nine class prediction models were built for each dataset using the same procedure and their performances were evaluated by calculating their accuracies. The characteristics of each experiment were recorded, (i.e., observed disease, medical question, tissue/cell types and sample size) together with characteristics of the gene expression data, namely the number of differentially expressed genes, the fold changes and the within-class correlations. Their effects on the accuracy of a class prediction model were statistically assessed by random effects logistic regression. The number of differentially expressed genes and the average fold change had significant impact on the accuracy of a classification model and gave individual explained-variation in prediction accuracy of up to 72% and 57%, respectively. Multivariable random effects logistic regression with forward selection yielded the two aforementioned study factors and the within class correlation as factors affecting the accuracy of classification functions, explaining 91.5% of the between study variation. CONCLUSIONS:We evaluated study- and data-related factors that might explain the varying performances of classification functions in non-cancerous datasets. Our results showed that the number of differentially expressed genes, the fold change, and the correlation in gene expression data significantly affect the accuracy of class prediction models.

Project description:BackgroundTransmissible spongiform encephalopathy diseases are untreatable, uniformly fatal degenerative syndromes of the central nervous system that can be transmitted both within as well as between species. The bovine spongiform encephalopathy (BSE) epidemic and the emergence of a new human variant of Creutzfeldt-Jakob disease (vCJD), have profoundly influenced beef production processes as well as blood donation and surgical procedures. Simple, robust and cost effective diagnostic screening and surveillance tools are needed for both the preclinical and clinical stages of TSE disease in order to minimize both the economic costs and zoonotic risk of BSE and to further reduce the risk of secondary vCJD.ObjectiveUrine is well suited as the matrix for an ante-mortem test for TSE diseases because it would permit non-invasive and repeated sampling. In this study urine samples collected from BSE infected and age matched control cattle were screened for the presence of individual proteins that exhibited disease specific changes in abundance in response to BSE infection that might form the basis of such an ante-mortem test.ResultsTwo-dimensional differential gel electrophoresis (2D-DIGE) was used to identify proteins exhibiting differential abundance in two sets of cattle. The known set consisted of BSE infected steers and age matched controls throughout the course of the disease. The blinded unknown set was composed of BSE infected and control samples of both genders, a wide range of ages and two different breeds. Multivariate analyses of individual protein abundance data generated classifiers comprised of the proteins best able to discriminate between the samples based on disease state, breed, age and gender.ConclusionDespite the presence of confounding factors, the disease specific changes in abundance exhibited by a panel of urine proteins permitted the creation of classifiers able to discriminate between control and infected cattle with a high degree of accuracy.

Dataset Information

Factors Affecting Accuracy of Data Abstracted from Medical Records.

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Factors Affecting Accuracy of Data Abstracted from Medical Records.

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