Harinto Nur Seha,1 Savitri Citra Budi,2 Ahmad Yani Noor,3 I Gusti Agung Ngurah putra Pradnyantara4

1Medical Record and Health Information Department, Poltekkes Permata Indonesia, Yogyakarta, Indonesia; 2Department of Health Information and Services, Vocational School, Universitas Gadjah Mada, Yogyakarta, Indonesia; 3Hospital Administration, Poltekkes Permata Indonesia, Yogyakarta, Indonesia; 4Medical Record and Health Information Department, STIKES Wira Medika, Bali, Indonesia

Correspondence: Harinto Nur Seha, Poltekkes Permata Indonesia Yogyakarta, Jl. Ringroad utara No. 22C, Condongcatur, Depok, Sleman, Daerah Istimewa, Yogyakarta, Indonesia, Email [email protected]

View the original paper by Dr Hsieh and colleagues

The recent study by Hsieh et al, published in Clinical Epidemiology, makes a significant contribution to improving disease reporting quality by validating ICD-10 codes for Guillain-Barré Syndrome (GBS) in Taiwan’s National Health Insurance claims database. The study’s key finding, that the positive predictive value (PPV) for code G61.0 varied depending on the operational definition, is particularly impactful. While the PPV reached 98.3% by incorporating nerve conduction studies (NCS) and treatment claims, this came at the cost of reduced sensitivity in identifying true GBS cases.1 This highlights the importance of balancing specificity with sensitivity to enhance the accuracy of reporting for research and public health interventions.

Furthermore, the study demonstrates the value of integrating electronic medical records (EMRs) with claims data for code validation.2 As highlighted, the alignment between diagnostic certainty (based on Brighton criteria) and code placement in discharge diagnoses provides actionable insights for both clinicians and coders. Such findings could inform targeted training programs to enhance coding accuracy and the utility of claims databases for epidemiological research.

Nevertheless, this study also highlights the inherent limitations of relying exclusively on ICD-10 codes for clinical research. The exclusion of a significant number of true GBS cases under a stricter definition suggests that complementary methodologies, such as machine learning algorithms or hybrid data validation approaches, may be necessary to optimize case identification.3 Given the global reliance on administrative databases for disease surveillance, I encourage further research to replicate and refine these findings across various healthcare settings. Additionally, exploring the enhanced coding capabilities of ICD-11 could offer promising opportunities to improve diagnostic accuracy and research validity.4

In conclusion, Hsieh et al’s meticulous study is a commendable contribution to the field, emphasizing the importance of robust validation protocols for ICD-10 codes. As a community, we must continue to innovate and refine our approaches to ensure that health data serve as a reliable foundation for advancing clinical epidemiology and public health.

The authors report no conflicts of interest in this communication.

1. Hsieh CY, Chen PT, Shao SC, Lin SJ, Liao SC, Lai E. Validating ICD-10 diagnosis codes for Guillain-Barré syndrome in Taiwan’s National Health insurance claims database. Clin Epidemiol. 2024;16:733–742. doi:10.2147/CLEP.S485953

2. Massen GM, Whittaker HR, Cook S, et al. Using routinely collected electronic healthcare record data to investigate fibrotic multimorbidity in England. Clin Epidemiol. 2024;16:433–443. doi:10.2147/CLEP.S463499

3. Flores A, Tito-Chura H. Multi-step forecasting of Guillain Barré cases using deep learning. Informatica. 2024;48(20):17–26. doi:10.31449/inf.v48i20.6358

4. Ooi ECW, Isa ZM, Manaf MRA, et al. Factors influencing the intention to use the ICD-11 among medical record officers (MROs) and assistant medical record officers (AMROs) in Ministry of Health, Malaysia. Sci Rep. 2024;14(1). doi:10.1038/s41598-024-60439-2

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