Analysis of Medical Record Documentation and Diagnosis Coding in Orthopedic Inpatients

Grahyta Dhamayanti; Imelda Yanti; Ika Saptarini

doi:10.22146/jkesvo.110941

Analysis of Medical Record Documentation and Diagnosis Coding in Orthopedic Inpatients

https://doi.org/10.22146/jkesvo.110941

Grahyta Dhamayanti^(1*), Imelda Yanti⁽²⁾, Ika Saptarini⁽³⁾

(1) Inpatient Installation, Fatmawati Hospital, Jakarta
(2) Inpatient Installation, Fatmawati Hospital, Jakarta
(3) Research Center for Pre-Clinical and Clinical Medicine, National Research and Innovation Agency (BRIN), Jakarta
(*) Corresponding Author

Abstract

Background: Orthopedic surgery is a flagship service at Fatmawati Hospital that demands high complexity and requires accurate clinical documentation and diagnosis coding. Incomplete medical records and coding errors can lead to administrative and financial problems.

Objective: This study aims to analyze the association between the completeness of medical record documentation and the accuracy of diagnosis coding in orthopedic inpatients.

Methods: A quantitative cross-sectional study was conducted on 95 orthopedic inpatient medical records selected purposively. Data were collected using a documentation completeness checklist and a coding review form based on ICD-10 and ICD-9-CM. Analyses included chi-square tests, Pearson correlation, and logistic regression.

Results: Of all medical records, 78.9% were complete and 70.5% had accurate diagnosis coding. Documentation completeness was significantly associated with coding accuracy (χ² = 44.647; p = 0.001). Logistic regression confirmed documentation completeness as a predictor of coding accuracy (p = 0.001; OR = 0.013), accounting for 56.7% of the variance. A strong correlation was also found between coders’ length of work experience and coding accuracy (r = 0.805; p < 0.001).

Conclusion: Complete documentation improves diagnosis coding accuracy. Recommendations include the use of standardized checklists, routine coder training, SIMRS optimization, stronger coder–physician collaboration, and regular audits to enhance evidence-based service quality and claims validity.

Keywords

medical record; coding accuracy; orthopedic; service quality; evidence-based

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References

Al-Hawary. (2020). The impact of electronic medical records on healthcare quality: Evidence from hospitals. International Journal of Advanced Science and Technology, 29(3), 3240–3255. Available at: http://sersc.org/journals/index.php/IJAST/article/view/27356.

Bayhaqi, A. (2025). Digital transformation in hospital information systems: challenges and opportunities. Indonesian Journal of Health Information Management, 13(1), 45–55. Available at: https://doi.org/10.22146/jkesvo.106279.

Bernstam, E. (2022). Quantitating and assessing interoperability between electronic health record systems. Journal of the American Medical Informatics Association, 29(5), pp. 753–761. Available at: https://academic.oup.com/jamia/article/29/5/753.

Bittner, M. (2021). Accuracy of ICD coding and its implications for clinical care and research. Journal of General Internal Medicine, 36(8), 2486–2492. Available at: https://doi.org/10.1007/s11606-021-06778-0.

Cattani, L. (2023). Completeness and consistency of medical records data in hospitals: a multi-center analysis. BMC Health Services Research, 23(1), 10127.

Choi. (2022). Improving the validity of diagnosis coding through medical record documentation training. BMC Health Services Research, 22(1), 113–124. Available at: https://bmchealthservres.biomedcentral.com.

Dewi. (2021). Analisis faktor yang mempengaruhi ketepatan koding diagnosis penyakit dalam di rumah sakit. Jurnal Administrasi Kesehatan Indonesia, 9(1), 12–22. Available at: https://e-journal.unair.ac.id/jaki.

Febriadi, A. (2024). Analisis kesalahan koding klaim BPJS di RSK Dharmais. Jurnal Administrasi Rumah Sakit Indonesia, 10(2), 77–85. Available at: https://scholarhub.ui.ac.id/arsi/vol10/iss1/2.

Hadian, S. (2024). Key performance indicators for hospital performance assessment: a systematic review. BMC Health Services Research, 24, 10940. Available at: doi:10.1186/s12913-024-10940-1.

Hasan, M. (2023). Strengthening health data governance through medical records management. International Journal of Health Policy and Management, 12(6), 567–575.

Helmkamp, L. (2022). Enhancing coding accuracy through structured reporting. Journal of Health Information Management, 56(3), 211–219. Available at: https://pmc.ncbi.nlm.nih.gov.

Ibrahim, M. (2024). Analisis Faktor Penyebab Ketidakakuratan Kode Diagnosis Klaim BPJS Pasien Rawat Inap (Literature Review). Jurnal Rekam Medis (Medical Record Journal), 3(1), 124–132. Available at: DOI: 10.25311/jrm.Vol3.Iss1.1297.

Joseph, W. (2024). Improving clinical coding accuracy through targeted education: a retrospective cohort study. BMC Health Services Research, 24(1), 11267. Available at: https://doi.org/10.1186/s12913-024-11267-7.

Kementerian Kesehatan Republik Indonesia. (2020). Pedoman Pengelolaan Rekam Medis dan Informasi Kesehatan. Jakarta: Kemenkes RI.

Kementerian Kesehatan Republik Indonesia. (2023). Laporan Mutu Layanan Rumah Sakit Vertikal. Jakarta: Kemenkes RI.

Kueper, J. (2024). A framework for assessing data quality in electronic medical records: a stakeholder-informed approach. BMC Medical Informatics and Decision Making, 24(1). Available at: https://bmcmedinformdecismak.biomedcentral.com/.

Kulju, S., Morrish, W., King, L., Bender, J., Gunnar, W. (2022). Patient misidentification events in the Veterans Health Administration: A comprehensive review in the context of high-reliability health care. Journal of Patient Safety, 18(1), e290–e296. Available at: doi:10.1097/PTS.0000000000000767.

Mahendra, A. (2021). Evaluasi kelengkapan rekam medis pasien rawat inap di rumah sakit tipe A. Jurnal Manajemen Informasi Kesehatan Indonesia, 9(1), 33–42. Available at: https://jmiki.aptirmik.or.id/jmiki.

Mafrachi, B. (2021). Documentation errors in orthopedic surgery: a study from Jordan University Hospital. Patient Safety in Surgery, 15(1), 17. Available at: https://doi.org/10.1186/s13037-021-00298-0.

Maxelly, R. (2021). Hubungan kelengkapan anamnesa dengan ketepatan kode ICD-10 pada kasus kecelakaan di RS Panti Nirmala Malang. Jurnal Manajemen Informasi Kesehatan Indonesia, 9(2), 144. Available at: doi:10.33560/JMIKI.V9I2.330.

O’Malley, J. (2021). Measuring diagnoses: ICD-10 coding accuracy. Health Services Research, 56(2), 123–134. Available at: doi:10.1111/j.1475-6773.2005.00444.x.

Perkins, R. (2024). Improving clinical documentation with artificial intelligence: A systematic review. AHIMA Scientific Policy Journal. Available at: https://ahisp.ahima.org/Page/improving-clinical-documentation-with-artificial-intelligence-a-systematic-review.

Puspitasari, I. (2024). Kelengkapan informasi medis dan keakuratan kode diagnosis kasus penyakit kronis. Jurnal Manajemen Informasi Kesehatan Indonesia, 11(1), 55–63. Available at: DOI: 10.62951/jurmiki.v3i1.46.

Rahayu, S. (2022). Tanggung jawab hukum rumah sakit terhadap pengelolaan rekam medis digital (studi literatur). JMMR, 11(2), 103–112. Available at: https://jmmr.umy.ac.id/.

Siswati, S. (2024). Analisis tantangan kesiapan implementasi rekam medis elektronik di puskesmas kota Padang. Jurnal Kesehatan Vokasional, 8(1), 66–75. Available at: https://doi.org/10.22146/jkesvo.92719.

Suryani, N. (2022). Hubungan ketepatan terminologi medis diagnosis utama dengan keakuratan koding diagnosis pasien BPJS Kesehatan di Rumah Sakit Umum X Denpasar. Jurnal Manajemen Informasi Kesehatan Indonesia, 10(2), 122. Available at: doi:10.33560/jmiki.v10i2.369.

Vainiomäki, S. (2020). Quality of medical record documentation: A systematic review. International Journal for Quality in Health Care, 32(8), 531–540. Available at: https://academic.oup.com/intqhc.

Wang, L. (2023). Effectiveness of clinical coding audits in reducing coding error and claim denials: A 3-year experience. International Journal of Health Planning and Management, 38(1), 129–139.

Weiskopf, N. (2022). A unified conceptual framework for EHR data quality assessment. Journal of the American Medical Informatics Association, 29(9–10). Available at: DOI: 10.1093/jamia/ocad12.

Wissmann, S., Henderson, J., Robinson, K. (2024). The health information management workforce: Looking to the future. Health Information Management Journal, 54(2), 103–108. Available at: doi:10.1177/18333583241303635.

World Health Organization. (2019). International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10). Geneva: WHO.

Zhang, Y. (2024). Artificial intelligence applications to improve medical coding accuracy: current trends and future perspectives. Journal of Biomedical Informatics, 146, 104553. Available at: https://www.sciencedirect.com/journal/journal-of-biomedical-informatics.

DOI: https://doi.org/10.22146/jkesvo.110941