Avid Wijaya,1 Endang Sri Dewi Hastuti Suryandari,1 Dea Allan Karunia Sakti,1 Tsalits Maulidah Hariez,1 Harinto Nur Seha2

1Medical Record and Health Information Department, Poltekkes Kemenkes Malang, Kota Malang, Jawa Timur, Indonesia; 2Medical Record and Health Information Department, Poltekkes Permata Indonesia Yogyakarta, Kabupaten Sleman, Yogyakarta, Indonesia

Correspondence: Avid Wijaya, Poltekkes Kemenkes Malang, Jl. Besar Ijen 77C, Kota Malang, Indonesia, Email [email protected]

View the original paper by Miss Massen and colleagues

A Response to Letter has been published for this article.

The article titled “Using Routinely Collected Electronic Healthcare Record Data to Investigate Fibrotic Multimorbidity in England” is a significant contribution to the field of epidemiology, particularly in the study of chronic diseases through electronic healthcare records (EHR).1 The research demonstrates the utility of EHR data in identifying and analyzing the prevalence and impact of fibrotic diseases that often occur together, termed fibrotic multimorbidity. This approach offers a valuable perspective on the burden of such conditions on public health systems by leveraging existing data, making it cost-effective and expansive in scope.2,3 However, the study’s methodology invites some criticism, particularly concerning data quality and the potential biases inherent in routinely collected EHR data. Medical records, while rich in information, can suffer from incompleteness, inconsistency, or biases introduced by healthcare providers’ coding practices, potentially leading to classification errors or inadequate reporting of specific conditions. Furthermore, the study’s reliance on pre-existing codes and the lack of standardization across different healthcare settings could have impacted the accuracy of the multimorbidity estimates.4 To address these issues, future research should prioritize the standardization of EHR data collection and coding practices across different healthcare systems to improve data consistency and reliability. Additionally, implementing advanced data cleaning and validation techniques could mitigate the effects of incomplete or biased records, ensuring more accurate and representative results.5 Integrating patient-reported outcomes and other qualitative data sources might also enrich the understanding of fibrotic multimorbidity, providing a more holistic view of these conditions. Overall, while the article successfully highlights the potential of EHR data in epidemiological research, these methodological enhancements are crucial for refining future studies in this area.

The authors report no conflicts of interest in this communication.

1. Massen G, Whittaker H, Cook S, et al. Using routinely collected electronic healthcare record data to investigate fibrotic multimorbidity in England. Clinical Epidemiology. 2024;16:433–443. doi:10.2147/CLEP.S463499

2. Lian B. Adoption of network and plan-do-check-action in the international classification of disease 10 coding. World J Clin Cases. 2024;12(19):3734–3743. doi:10.12998/wjcc.v12.i19.3734

3. Jefferies D, Johnson M, Griffiths R. A meta‐study of the essentials of quality nursing documentation. Int J Nurs Pract. 2010;16(2):112–124. doi:10.1111/j.1440-172X.2009.01815.x

4. Pati S, Mahapatra P, Dwivedi R, et al. Multimorbidity and its outcomes among patients attending psychiatric care settings: an observational study from Odisha, India. Front Public Health. 2021:8. doi:10.3389/fpubh.2020.616480

5. Khan A, Huda A, Ghose A, Dam HK. Towards knowledge-centric process mining; 2023. Available from: http://arxiv.org/abs/2301.10927. Accessed September02, 2024.

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