University of California at Los Angeles David Geffen School of Medicine, Los Angeles, CA, USA.
Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
BMC Med Inform Decis Mak. 2024 Oct 23;24(1):309. doi: 10.1186/s12911-024-02724-8.
The mechanism for recording International Classification of Diseases (ICD) and diagnosis related groups (DRG) codes in a patient's chart is through a certified medical coder who manually reviews the medical record at the completion of an admission. High-acuity ICD codes justify DRG modifiers, indicating the need for escalated hospital resources. In this manuscript, we demonstrate that value of rules-based computer algorithms that audit for omission of administrative codes and quantifying the downstream effects with regard to financial impacts and demographic findings did not indicate significant disparities.
All study data were acquired via the UCLA Department of Anesthesiology and Perioperative Medicine's Perioperative Data Warehouse. The DataMart is a structured reporting schema that contains all the relevant clinical data entered into the EPIC (EPIC Systems, Verona, WI) electronic health record. Computer algorithms were created for eighteen disease states that met criteria for DRG modifiers. Each algorithm was run against all hospital admissions with completed billing from 2019. The algorithms scanned for the existence of disease, appropriate ICD coding, and DRG modifier appropriateness. Secondarily, the potential financial impact of ICD omissions was estimated by payor class and an analysis of ICD miscoding was done by ethnicity, sex, age, and financial class.
Data from 34,104 hospital admissions were analyzed from January 1, 2019, to December 31, 2019. 11,520 (32.9%) hospital admissions were algorithm positive for a disease state with no corresponding ICD code. 1,990 (5.8%) admissions were potentially eligible for DRG modification/upgrade with an estimated lost revenue of $22,680,584.50. ICD code omission rates compared against reference groups (private payors, Caucasians, middle-aged patients) demonstrated significant p-values < 0.05; similarly significant p-value where demonstrated when comparing patients of opposite sexes.
We successfully used rules-based algorithms and raw structured EHR data to identify omitted ICD codes from inpatient medical record claims. These missing ICD codes often had downstream effects such as inaccurate DRG modifiers and missed reimbursement. Embedding augmented intelligence into this problematic workflow has the potential for improvements in administrative data, but more importantly, improvements in administrative data accuracy and financial outcomes.
在患者病历中记录国际疾病分类（ICD）和诊断相关组（DRG）代码的机制是通过认证的医疗编码员在入院完成后手动审查病历。高敏 ICD 代码证明了 DRG 修饰符的合理性，表明需要增加医院资源。在本文中，我们展示了基于规则的计算机算法在审核行政代码遗漏和量化财务影响和人口统计发现方面的价值，这些算法没有显示出显著的差异。
所有研究数据均通过加州大学洛杉矶分校麻醉与围手术期医学系围手术期数据仓库获取。DataMart 是一个结构化报告方案，其中包含输入到 EPIC（威斯康星州 Verona 的 EPIC 系统）电子健康记录中的所有相关临床数据。为符合 DRG 修饰符标准的十八种疾病状态创建了计算机算法。每个算法都针对所有有完整计费的住院患者在 2019 年进行了运行。算法扫描疾病的存在、适当的 ICD 编码和 DRG 修饰符的适当性。其次，通过支付者类别估算 ICD 遗漏的潜在财务影响，并按种族、性别、年龄和财务类别对 ICD 错误编码进行分析。
分析了 2019 年 1 月 1 日至 12 月 31 日期间 34104 例住院患者的数据。11520 例（32.9%）住院患者的疾病状态无相应 ICD 编码，算法阳性。1990 例（5.8%）患者可能有资格进行 DRG 修改/升级，估计损失收入 22680584.50 美元。与参考组（私人支付者、白种人、中年患者）相比，ICD 代码遗漏率的 p 值均<0.05；在比较不同性别的患者时，也显示出了显著的 p 值。
我们成功地使用基于规则的算法和原始结构化电子健康记录数据从住院病历中识别出遗漏的 ICD 代码。这些缺失的 ICD 代码通常会产生下游影响，例如不准确的 DRG 修饰符和错过的报销。将增强型人工智能嵌入到这个有问题的工作流程中有可能改善行政数据，但更重要的是，改善行政数据的准确性和财务结果。
