Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study explored the experiences of recording electronic nursing notes among ward nurses in the hospital. Methods: A qualitative descriptive study was conducted. Data were collected through in-depth interviews with 15 nurses at two university hospitals in Daegu between November 2022 and April 2023. Hsieh and Shannon’s conventional content analysis method was applied, and MAXQDA was used for the analysis. Results: Three categories were identified from the data, with 12 subcategories and 46 codes. The three categories were as follows: utility, limitations to effective use, and seeking a way to expand functions efficiently. Conclusion Records involving electronic nursing notes played a very important role in patient care and legal protection, as well as serving as a guide to nursing diagnosis, a source of pride, and a resource with convenient functions. Areas for improvement relate to awareness, burden, discomfort, disparities, and obstacles to record-keeping. A more efficient record system needs to be established, with corresponding education. Furthermore, hospital administrators should provide policy support to improve the quality of nursing records.

Purpose: This study explored the experiences of recording electronic nursing notes among ward nurses in the hospital. Methods: A qualitative descriptive study was conducted. Data were collected through in-depth interviews with 15 nurses at two university hospitals in Daegu between November 2022 and April 2023. Hsieh and Shannon’s conventional content analysis method was applied, and MAXQDA was used for the analysis. Results: Three categories were identified from the data, with 12 subcategories and 46 codes. The three categories were as follows: utility, limitations to effective use, and seeking a way to expand functions efficiently. Conclusion Records involving electronic nursing notes played a very important role in patient care and legal protection, as well as serving as a guide to nursing diagnosis, a source of pride, and a resource with convenient functions. Areas for improvement relate to awareness, burden, discomfort, disparities, and obstacles to record-keeping. A more efficient record system needs to be established, with corresponding education. Furthermore, hospital administrators should provide policy support to improve the quality of nursing records.

Purpose: This study explored the experiences of recording electronic nursing notes among ward nurses in the hospital. Methods: A qualitative descriptive study was conducted. Data were collected through in-depth interviews with 15 nurses at two university hospitals in Daegu between November 2022 and April 2023. Hsieh and Shannon’s conventional content analysis method was applied, and MAXQDA was used for the analysis. Results: Three categories were identified from the data, with 12 subcategories and 46 codes. The three categories were as follows: utility, limitations to effective use, and seeking a way to expand functions efficiently. Conclusion Records involving electronic nursing notes played a very important role in patient care and legal protection, as well as serving as a guide to nursing diagnosis, a source of pride, and a resource with convenient functions. Areas for improvement relate to awareness, burden, discomfort, disparities, and obstacles to record-keeping. A more efficient record system needs to be established, with corresponding education. Furthermore, hospital administrators should provide policy support to improve the quality of nursing records.

Purpose: Despite decreased prevalence of tuberculosis, the incidence of the diseases associated with nontuberculous mycobacteria (NTM) has been increasing in South Korea and around the world. The present retrospective study was conducted to determine longitudinal changes in the epidemiology and distribution of NTM over 13 years at a tertiary care hospital in Korea. Materials and Methods: We retrospectively analyzed data on Mycobacterium species over 13 years (January 2007 to December 2019) by utilizing the laboratory information system. Mycobacterium species were identified using biochemical tests and PCR-restriction fragment length polymorphism and Mycobacteria GenoBlot assays. Results: After excluding duplicates from the initial pool of 17996 mycobacterial isolates, 7674 strains were analyzed and 2984 (38.9%) NTM were isolated. The proportion of NTM continuously increased over the 13-year period, from 17.0% in 2007 to 57.5% in 2019. Among the NTM isolates, the most common species were Mycobacterium intracellulare (50.6%), M. avium (18.3%), M. fortuitum complex (4.9%), M. abscessus (4.5%), M. gordonae (3.3%), M. kansasii (1.1%), M. chelonae (1.0%), and M. massiliense (0.9%).In patients over the age of 70 years, the proportion of NTM among the isolates increased from 26.6% in 2007 to 62.0% in 2019, and that of M. intracellulare isolates among the NTM increased from 13.9% (11/79) in 2007 to 37.4% (175/468) in 2019. Conclusion The number of NTM isolates continuously increased over the study period, and the increase in the proportion of M. intracellulare in patients aged over 70 years was notable.

Purpose: Despite decreased prevalence of tuberculosis, the incidence of the diseases associated with nontuberculous mycobacteria (NTM) has been increasing in South Korea and around the world. The present retrospective study was conducted to determine longitudinal changes in the epidemiology and distribution of NTM over 13 years at a tertiary care hospital in Korea. Materials and Methods: We retrospectively analyzed data on Mycobacterium species over 13 years (January 2007 to December 2019) by utilizing the laboratory information system. Mycobacterium species were identified using biochemical tests and PCR-restriction fragment length polymorphism and Mycobacteria GenoBlot assays. Results: After excluding duplicates from the initial pool of 17996 mycobacterial isolates, 7674 strains were analyzed and 2984 (38.9%) NTM were isolated. The proportion of NTM continuously increased over the 13-year period, from 17.0% in 2007 to 57.5% in 2019. Among the NTM isolates, the most common species were Mycobacterium intracellulare (50.6%), M. avium (18.3%), M. fortuitum complex (4.9%), M. abscessus (4.5%), M. gordonae (3.3%), M. kansasii (1.1%), M. chelonae (1.0%), and M. massiliense (0.9%).In patients over the age of 70 years, the proportion of NTM among the isolates increased from 26.6% in 2007 to 62.0% in 2019, and that of M. intracellulare isolates among the NTM increased from 13.9% (11/79) in 2007 to 37.4% (175/468) in 2019. Conclusion The number of NTM isolates continuously increased over the study period, and the increase in the proportion of M. intracellulare in patients aged over 70 years was notable.