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Published on: January 8, 2020
[Comparison of inpatient disease statistics based on two coding rules]
1Medical Records Management and Statistical Information Center, Xiangya Hospital, Central South University, Changsha 410008, China.
Abstract:
Objective: To compare the impact of principal diagnosis coding rules and etiologic diagnosis coding rules based on the underlying cause of death coding logic on disease statistics for nonfatal medical records. Methods: This study analyzed 20 744 medical records of inpatients from a grade Ⅲ general hospital in Changsha, China, between November 1, 2022 and October 31, 2023. On the basis of clinical diagnostic records, professional medical record coders determined and coded diagnoses according to the principal diagnosis and etiologic diagnosis coding rules, respectively, in which χ2 test was used to compare discordance between the two coding results across patient groups. Disease spectrum differences under the two coding rules were further described by sex and age group. Results: In the 20 744 medical records used in the study, the discordance rate between the two coding results was 30.0% (95%CI: 29.3%-30.6%). The χ2 tests indicated that the discordance rate varied significantly by sex, age group, and clinical department (all P<0.05). Comparison of the disease spectrum under the two coding rules showed that the largest increase in the number of reported cases was observed for tumors (8 219 vs. 12 563). Meanwhile, Factors influencing health status and contact with health services showed the most notable change in ranking (2nd vs. 18th). Additionally, disease rankings and frequencies also varied by sex and age group. Conclusions: The two coding rules substantially affected the disease spectrum of inpatients, and their impact varied by sex, age group, and clinical department. Given that principal diagnosis coding focuses on clinical treatment and management, whereas etiologic diagnosis coding emphasizes the initiating cause of disease or injury, policymakers and researchers should consider the influence of differences in coding rules on disease composition and rankings when interpreting and using disease statistics generated under these two coding rules.
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