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.

中文翻译：

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衡量数据可信度和医学编码：使用葡萄牙全国住院患者数据库的案例研究

一些国家采用了诊断相关组（DRG）制度，根据医院治疗的患者数量和复杂程度向医院支付费用。将疾病和程序转换为基于 ICD-9-CM 或 ICD-10-CM/PCS 等国际标准的医学代码是 DRG 系统的核心。但是，某些类型的编码错误会破坏该系统，即上编码，其中故意使用医疗代码来操纵数据，从而增加了患者的复杂性，从而导致更高的报销。从这个意义上说，确保上编码环境中的数据可信度对于有效运行的 DRG 系统至关重要。我们开发了一种方法，通过使用葡萄牙六家公立医院肺炎相关住院数据的案例研究，在升级的背景下衡量数据可信度。比较医院之间代表肺炎相关诊断和合并症的代码频率，并采用支持向量机模型来预测 DRG，以验证具有差异频率的代码是否与上行编码相关。如果频率不一致的代码导致 DRG 复杂性增加，则数据被认为是不可信的。至少在一家医院，6 项肺炎相关诊断和 15 项合并症的频率高于预期，并且与 DRG 复杂性增加之间存在联系，并且发现了这些目标代码。然而，几乎所有疾病的总体可信度都非常高，但肾脏疾病除外，肾脏疾病的潜在升级比例最高。