MSD SHARP & DOHME GmbH, Levelingstrasse 4A, 81673, Munich, Germany.
Department of Hematology and Oncology, Internal Medicine-Oncology, Pius Hospital, Medical Campus University of Oldenburg, Cancer Center Oldenburg, Georgstrasse 12, 26121, Oldenburg, Germany.
BMC Health Serv Res. 2022 Jun 28;22(1):834. doi: 10.1186/s12913-022-07982-8.
The analysis of statutory health insurance (SHI) data is a little-used approach for understanding treatment and care as well as resource use of lung cancer (LC) patients in Germany. The aims of this observational, retrospective, longitudinal analysis of structured data were to analyze the healthcare situation of LC patients in Germany based on routine data from SHI funds, to develop an algorithm that sheds light on LC types (non-small cell / NSCLC vs. small cell / SCLC), and to gain new knowledge to improve needs-based care.
Anonymized billing data of approximately four million people with SHI were analyzed regarding ICD-10 (German modification), documented medical interventions based on the outpatient SHI Uniform Assessment Standard Tariff (EBM) or the inpatient Operations and Procedure Code (OPS), and the dispensing of prescription drugs to outpatients (ATC classification). The study included patients who were members of 64 SHI funds between Jan-1st, 2015 and Dec-31st, 2016 and who received the initial diagnosis of LC in 2015 and 2016.
The analysis shows that neither the cancer type nor the cancer stage can be unambiguously described by the ICD-10 coding. Furthermore, an assignment based on the prescribed medication provides only limited information: many of the drugs are either approved for both LC types or are used off-label, making it difficult to assign them to a specific LC type. Overall, 25% of the LC patients were unambiguously identifiable as NSCLC vs SCLC based on the ICD-10 code, the drug therapy, and the billing data.
The current coding system appears to be of limited suitability for drawing conclusions about LC and therefore the SHI patient population. This makes it difficult to analyze the healthcare data with the aim of gathering new knowledge to improve needs-based care. The approach chosen for this study did not allow for development of a LC differentiation algorithm based on the available healthcare data. However, a better overview of patient specific needs could make it possible to modify the range of services provided by the SHI funds. From this perspective, it makes sense, in a first step, to refine the ICD-10 system to facilitate NSCLC vs. SCLC classification.
分析法定健康保险 (SHI) 数据是了解德国肺癌 (LC) 患者治疗和护理以及资源利用情况的一种未被充分利用的方法。本项基于 SHI 基金结构化数据的观察性、回顾性、纵向分析旨在根据 SHI 基金的常规数据来分析德国 LC 患者的医疗保健情况，开发一种能够阐明 LC 类型（非小细胞/ NSCLC 与小细胞/SCLC）的算法，并获得新知识以改善基于需求的护理。
对约 400 万具有 SHI 的人的匿名计费数据进行分析，分析内容包括基于门诊 SHI 统一评估标准费率 (EBM) 或住院手术和程序代码 (OPS) 的记录医疗干预措施，以及向门诊患者发放的处方药物 (ATC 分类)。本研究包括 2015 年 1 月 1 日至 2016 年 12 月 31 日期间加入 64 个 SHI 基金的患者，以及 2015 年和 2016 年初次诊断为 LC 的患者。
分析表明，ICD-10 编码既不能明确描述癌症类型，也不能明确描述癌症分期。此外，基于规定药物的分配只能提供有限的信息：许多药物既批准用于两种 LC 类型，也用于非适应证用药，因此难以将其分配给特定的 LC 类型。总体而言，根据 ICD-10 代码、药物治疗和计费数据，25%的 LC 患者可明确地识别为 NSCLC 与 SCLC。
目前的编码系统似乎不太适合得出关于 LC 以及因此 SHI 患者人群的结论。这使得难以分析医疗保健数据以获取新知识来改善基于需求的护理。本研究选择的方法无法根据可用的医疗保健数据开发 LC 区分算法。然而，对患者特定需求的更好了解可以使 SHI 基金能够修改其提供的服务范围。从这个角度来看，首先完善 ICD-10 系统以促进 NSCLC 与 SCLC 的分类是有意义的。
