Literature DB >> 31564812

Identification of cancer patients using claims data from health insurance systems: A real-world comparative study.

Hongrui Tian1, Ruiping Xu2, Fenglei Li3, Chuanhai Guo1, Lixin Zhang2, Zhen Liu1, Mengfei Liu1, Yaqi Pan1, Zhonghu He1, Yang Ke1.   

Abstract

OBJECTIVE: To evaluate the accuracy of identifying cancer patients by use of medical claims data in a health insurance system in China, and provide the basis for establishing the claims-based cancer surveillance system in China.
METHODS: We chose Hua County, Henan Province as the study site, and randomly selected 300 and 1,200 qualified inpatient electronic medical records (EMRs) as well as the New Rural Cooperative Medical Scheme (NCMS) claims records for cancer patients in Hua County People's Hospital (HCPH) and Anyang Cancer Hospital (ACH) in 2017. Diagnostic information for NCMS claims was evaluated on an individual level, and sensitivity and positive predictive value (PPV) were calculated taking the EMRs as the gold standard.
RESULTS: The sensitivity of NCMS was 95.2% (93.8%-96.3%) and 92.0% (88.3%-94.8%) in ACH and HCPH, respectively. The PPV of the NCMS was 97.8% (96.7%-98.5%) in ACH and 89.0% (84.9%-92.3%) in HCPH. Overall, the weighted and combined sensitivity and PPV of NCMS in Hua County was 93.1% and 92.1%, respectively. Significantly higher sensitivity and PPV in identifying patients with common cancers than non-common cancers were detected in HCPH and ACH separately (P<0.01).
CONCLUSIONS: Identification of cancer patients by use of the NCMS is accurate on individual level, and it is therefore feasible to conduct claims-based cancer surveillance in areas not covered by cancer registries in China.
Copyright © 2019 Chinese Journal of Cancer Research. All rights reserved.

Entities:  

Keywords:  NCMS; cancer surveillance; claims data; positive predictive value; sensitivity

Year:  2019        PMID: 31564812      PMCID: PMC6736657          DOI: 10.21147/j.issn.1000-9604.2019.04.13

Source DB:  PubMed          Journal:  Chin J Cancer Res        ISSN: 1000-9604            Impact factor:   5.087


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