Accuracy of Billing Codes Used in the Therapeutic Care of Diabetic Retinopathy

Lau, Marisa; Prenner, Jonathan L.; Brucker, Alexander J.; VanderBeek, Brian L.

doi:10.1001/jamaophthalmol.2017.1595

Cited by 29 publications

(27 citation statements)

References 12 publications

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“…Diabetic retinopathy and cataracts have been found to be coded with good accuracy, which may be because clinicians are familiar with the full scope of these codes due to their frequent use. 4 , 5 Additionally, these codes are very granular, allowing maximal accuracy and specificity when selecting the correct diagnosis. Finally, codes for cataracts and diabetic retinopathy are often linked to surgical or office procedures with subsequent conditional reimbursement, further incentivizing the need for accurate coding.…”

Section: Discussionmentioning

confidence: 99%

Coding patterns used by ophthalmologists for hydroxychloroquine retinal toxicity

Chiu

Shaw

Luong³

et al. 2018

OPTH

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PurposeThe aim of this study was to characterize International Classification of Diseases (ICD-9) coding patterns used by ophthalmologists in clinical practice for hydroxychloroquine (HCQ) retinal toxicity.DesignThis is a retrospective cohort study.SubjectsPatients enrolled in the Kaiser Permanente Southern California health plan who were dispensed HCQ between 2001 and 2014 were included in this study.MethodsPatients’ medical records were electronically searched for the following ICD-9 codes that can be used to characterize retinopathy or maculopathy: toxic maculopathy, non-exudative age-related macular degeneration (AMD), drusen (degenerative), and/or (other) background retinopathy. The charts of patients with these codes were then manually reviewed to determine which of these patients had been diagnosed by their ophthalmologists with HCQ retinopathy.Main outcome measuresICD-9 codes used to indicate HCQ toxicity.ResultsA total of 23,362 patients were dispensed HCQ between 2001 and 2014. Of whom, 678 (2.9%) patients received at least one of the aforementioned ICD-9 codes with 53 having confirmed HCQ toxicity on chart review. Forty-three patients who were taking HCQ received a diagnosis code for toxic maculopathy, and of whom, 35 (81.4%) had HCQ toxicity. Of the patients with confirmed HCQ toxicity, 32.1% received a code other than toxic maculopathy to connote the presence of disease.ConclusionAlthough toxic maculopathy is the most commonly used ICD-9 code to identify patients with HCQ toxicity, a significant number of patients with toxicity received other codes in their medical records. Additionally, almost one-fifth of the patients who were coded as having toxic maculopathy were ultimately not diagnosed with HCQ toxicity. This study underscores the often imprecise nature of ICD coding, especially in conditions without a specific associated code. The limitations of using coding information should also be considered when conducting research that utilizes electronic databases. Future investigations should determine how to improve database searches and methods to create more uniform coding standards among ophthalmologists, especially for rare conditions such as HCQ toxicity.

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Section: Discussionmentioning

confidence: 99%

Coding patterns used by ophthalmologists for hydroxychloroquine retinal toxicity

Chiu

Shaw

Luong³

et al. 2018

OPTH

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“…These codes are used to quantify vital statistics, for surveillance, to seek cohorts of patients with similar diagnoses in downstream studies and also as a standardised information exchange method between hospitals. The thorough and accurate coding of EHRs affects critical clinical information extraction and also other industries such as insurance billing [10,11,12].…”

Section: Introductionmentioning

confidence: 99%

Extreme Multi-Label ICD Classification: Sensitivity to Hospital Service and Time

2020

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This work deals with clinical text mining for automatic classification of Electronic Health Records (EHRs) with respect to the International Classification of Diseases (ICD). ICD is the international standard for the identification of diseases and health conditions in EHRs and the foundation for reporting health statistics. Machine learning-based techniques have proven robust to infer classification models from EHRs. Since each EHR tends to involve multiple diseases, multi-label classification is required. The concern in this work is the versatility of the models inferred and their ability to generalise in two ways: as time goes ahead and across hospital services or health specialties. Indeed, in this work, we show the capabilities of a Bidirectional Recurrent Neural Network (RNN) with GRU units and ELMo embeddings on two corpora (a corpus comprising a set of EHRs within the Basque Health System, namely Osakidetza, and the well-known MIMIC-III corpus). To delve into and assess the versatility of the models, we focus on their resilience across hospital admissions taken over two different years and also across six distinct hospital services. In addition, we paid attention to the classification performance to estimate ICD codes of different granularity (e.g. with or without essential modifiers). Our best results are 39.55% and 47.28% F-Score for the Osakidetza and MIMIC-III datasets respectively, with the original main label-sets. Regarding the models evaluated per specialty, the most remarkable results are 57.00% and 72.74% F-Score, in the Cardiology and Nephrology medical services respectively. INDEX TERMS Extreme multi-label classification, Electronic Health Records, International Classification of Diseases, Classification across-time, Classification across hospital-services.

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“…In general, International Classification of Diseases (ICD) codes for most common diagnoses have been shown to be highly accurate. [1][2][3][4][5][6] In one study evaluating the accuracy of ICD-9 codes for cataract, primary open angle glaucoma, neovascular age-related macular degeneration, and proliferative diabetic retinopathy, the medical record supported the diagnosis code in 97% of cases. 1 Another recent study of Current Procedural Terminology (CPT) and drug codes (J codes) used in the treatment of diabetic retinopathy also found high positive and negative predictive values.…”

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confidence: 99%

“…1 Another recent study of Current Procedural Terminology (CPT) and drug codes (J codes) used in the treatment of diabetic retinopathy also found high positive and negative predictive values. 3 Thus, with the proper safeguards big data has become a powerful tool to study these ocular conditions and identify both practice patterns and clinical outcomes on a much larger scale than previously was possible.…”

mentioning

confidence: 99%

“…The procedural codes for diabetic retinopathy have been shown to have both high positive and negative predictive values. 3 Outside of ophthalmology, the diagnostic accuracy of both rheumatoid arthritis and diabetes have been shown to be greatly improved when ICD coding is combined with medication use. 13 Although yet to be proven in ophthalmology, it is reasonable to believe applying similar concepts to patients with eye disease should apply.…”

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confidence: 99%

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Techniques for improving ophthalmic studies performed on administrative databases

Borkar

Sobrin

Hubbard

et al. 2018

Ophthalmic Epidemiology

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Accuracy of Billing Codes Used in the Therapeutic Care of Diabetic Retinopathy

Cited by 29 publications

References 12 publications

Coding patterns used by ophthalmologists for hydroxychloroquine retinal toxicity

Coding patterns used by ophthalmologists for hydroxychloroquine retinal toxicity

Extreme Multi-Label ICD Classification: Sensitivity to Hospital Service and Time

Techniques for improving ophthalmic studies performed on administrative databases

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