Effective factors on accuracy of principal diagnosis coding based on International Classification of Diseases, the 10th revision (ICD-10)

Farzandipour, Mehrdad; Sheikhtaheri, Abbas; Sadoughi, Farahnaz

doi:10.1016/j.ijinfomgt.2009.07.002

Cited by 50 publications

(43 citation statements)

References 30 publications

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“…EHR data lack an explicit indication for why each laboratory test was ordered, and using other dimensions of EHR data for derivation of such information (such as ICD-9 codes and clinical notes) is equally problematic. ICD-9 codes are notoriously non-specific to patient disease state and are often not recorded for all patient ailments[11, 12]. Clinical notes rarely explicitly state the exact reason a test has been ordered.…”

Section: Introductionmentioning

confidence: 99%

Identifying and mitigating biases in EHR laboratory tests

Pivovarov

Albers

Sepulveda

et al. 2014

Journal of Biomedical Informatics

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Electronic health record (EHR) data show promise for deriving new ways of modeling human disease states. Although EHR researchers often use numerical values of laboratory tests as features in disease models, a great deal of information is contained in the context within which a laboratory test is taken. For example, the same numerical value of a creatinine test has different interpretation for a chronic kidney disease patient and a patient with acute kidney injury. We study whether EHR research studies are subject to biased results and interpretations if laboratory measurements taken in different contexts are not explicitly separated. We show that the context of a laboratory test measurement can often be captured by the way the test is measured through time. We perform three tasks to study the properties of these temporal measurement patterns. In the first task, we confirm that laboratory test measurement patterns provide additional information to the stand-alone numerical value. The second task identifies three measurement pattern motifs across a set of 70 laboratory tests performed for over 14,000 patients. Of these, one motif exhibits properties that can lead to biased research results. In the third task, we demonstrate the potential for biased results on a specific example. We conduct an association study of lipase test values to acute pancreatitis. We observe a diluted signal when using only a lipase value threshold, whereas the full association is recovered when properly accounting for lipase measurements in different contexts (leveraging the lipase measurement patterns to separate the contexts). Aggregating EHR data without separating distinct laboratory test measurement patterns can intermix patients with different diseases, leading to the confounding of signals in large-scale EHR analyses. This paper presents a methodology for leveraging measurement frequency to identify and reduce laboratory test biases.

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Section: Introductionmentioning

confidence: 99%

Identifying and mitigating biases in EHR laboratory tests

Pivovarov

Albers

Sepulveda

et al. 2014

Journal of Biomedical Informatics

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show abstract

“…4,12 Factors such as clarity of documentation, incomplete information in medical records and lack of attention to detail can lead to unreliable and inaccurate coding. 13,14 Previous studies have highlighted that inadequacies in morbidity and mortality reports are a direct reflection of shortcomings in the way some conditions are recorded.…”

mentioning

confidence: 99%

A pilot study of the impact of an educational intervention aimed at improving medical record documentation

Farzandipour¹,

Meidani²,

F³

et al. 2013

J R Coll Physicians Edinb

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“…In the first stage, base classifiers are trained to predict labels of the set of the infectious diseases where the labels are identified via using ICD-9 codes as surrogates. In particular, the labels are “noisy” since the diagnostic coding system was created mainly for administrative and billing purposes and hence not always accurate [14,15]. Fig.…”

Section: Methodsmentioning

confidence: 99%

EHR-based phenotyping: Bulk learning and evaluation

Chiu

Hripcsak

2017

Journal of Biomedical Informatics

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In data-driven phenotyping, a core computational task is to identify medical concepts and their variations from sources of electronic health records (EHR) to stratify phenotypic cohorts. A conventional analytic framework for phenotyping largely uses a manual knowledge engineering approach or a supervised learning approach where clinical cases are represented by variables encompassing diagnoses, medicinal treatments and laboratory tests, among others. In such a framework, tasks associated with feature engineering and data annotation remain a tedious and expensive exercise, resulting in poor scalability. In addition, certain clinical conditions, such as those that are rare and acute in nature, may never accumulate sufficient data over time, which poses a challenge to establishing accurate and informative statistical models. In this paper, we use infectious diseases as the domain of study to demonstrate a hierarchical learning method based on ensemble learning that attempts to address these issues through feature abstraction. We use a sparse annotation set to train and evaluate many phenotypes at once, which we call bulk learning. In this batch-phenotyping framework, disease cohort definitions can be learned from within the abstract feature space established by using multiple diseases as a substrate and diagnostic codes as surrogates. In particular, using surrogate labels for model training renders possible its subsequent evaluation using only a sparse annotated sample. Moreover, statistical models can be trained and evaluated, using the same sparse annotation, from within the abstract feature space of low dimensionality that encapsulates the shared clinical traits of these target diseases, collectively referred to as the bulk learning set.

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Effective factors on accuracy of principal diagnosis coding based on International Classification of Diseases, the 10th revision (ICD-10)

Cited by 50 publications

References 30 publications

Identifying and mitigating biases in EHR laboratory tests

Identifying and mitigating biases in EHR laboratory tests

A pilot study of the impact of an educational intervention aimed at improving medical record documentation

EHR-based phenotyping: Bulk learning and evaluation

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