Measuring data credibility and medical coding: a case study using a nationwide Portuguese inpatient database
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Measuring data credibility and medical coding: a case study using a nationwide Portuguese inpatient database Julio Souza 1,2
& Diana Pimenta
1
& Ismael Caballero
3
& Alberto Freitas
1,2
# Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Some countries have adopted the diagnosis-related groups (DRG) system to pay hospitals according to the number and complexity of patients they treat. Translating diseases and procedures into medical codes based on international standards such as ICD-9-CM or ICD-10-CM/PCS is at the core of the DRG systems. However, certain types of coding errors undermine this system, namely, upcoding, in which data is manipulated by deliberately using medical codes that increase patient’s complexity, resulting in higher reimbursements. In this sense, ensuring data credibility in the context of upcoding is critical for an effectively functioning DRG system. We developed a method to measure data credibility in the context of upcoding through a case study using data on pneumonia-related hospitalizations from six public hospitals in Portugal. Frequencies of codes representing pneumonia-related diagnosis and comorbidities were compared between hospitals and support vector machine models to predict DRGs were employed to verify whether codes with discrepant frequencies were related to upcoding. Data were considered not credible if codes with discrepant frequencies were responsible for increasing DRG complexity. Six pneumonia-related diagnoses and fifteen comorbidities presented a higher-than-expected frequency in at least one hospital and a link between increased DRG complexity, and these targeted codes was found. However, overall credibility was very high for nearly all conditions, except for renal disease, which presented the highest percentage of potential upcoding. The main contribution of this paper is a generic and reproducible method that can be employed to monitor data credibility in the context of upcoding in DRG databases. Keywords Data quality . Diagnosis-related groups . Clinical coding . Hospital administration . Data credibility . Support vector machine
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11219-02009504-3) contains supplementary material, which is available to authorized users.
* Julio Souza [email protected] Extended author information available on the last page of the article
Software Quality Journal
1 Background In the Portuguese hospital management sector, all information concerning the patient’s diseases, health status, disease progression, procedures, and treatments are routinely reported in health records, discharge notes, pathology, and surgical reports (Alonso et al. 2019). All this largely unstructured information is abstracted and translated into standard clinical codes representing the patient’s diagnoses and procedures according to the International Classification of Diseases, Clinical Modification, ninth and tenth revisions (ICD-9-CM and ICD-10CM) (Centers for Medicare and Med
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