Objective: As the demand for community mental health services continues to grow, the need for well-equipped and organized services has become apparent. This study aimed to optimize the roles and infrastructure of mental health services, by establishing, among other initiatives, standardized operating models. Methods: The study was conducted in multiple phases from May 12, 2021, to December 29, 2021. Stakeholders within South Korea and metropolitan mental health welfare centers were targeted, but addiction management support centers, including officials, patients, and their families, were integrated as well. A literature review and survey, focus group interviews, a Delphi survey, and expert consultation contributed to comprehensive revisions and improvements of the mental health service model. Results: The proposed model for community mental health welfare centers emphasizes the expansion of personnel and infrastructure, with a focus on severe mental illnesses and suicide prevention. The model for metropolitan mental health welfare centers delineates essential tasks in areas such as project planning and establishment, community research, and education about severe mental illnesses. The establishment of a 24-hour emergency intervention center was a crucial feature. In the integrated addiction support center model, the need to promote addiction management is defined as an essential task and the establishment of national governance for addiction policies is recommended. Conclusion This study proposed standard operating models for three types of mental health service centers. To meet the increasing need for community care, robust mental health service delivery systems are of primary importance.

Objective: In South Korea, there is a significant gap in systematic, evidence-based research on intensive case management (ICM) for individuals with severe mental illness (SMI). This study aims to evaluate the effectiveness of ICM through a randomized controlled trial (RCT) comparing ICM with standard case management (non-ICM). Methods: An RCT was conducted to assess the effectiveness of Seoul-intensive case management (S-ICM) vs. non-ICM in individuals with SMI in Seoul. A total of 78 participants were randomly assigned to either the S-ICM group (n=41) or the control group (n=37). Various clinical assessments, including the Brief Psychiatric Rating Scale (BPRS), Montgomery–Åsberg Depression Rating Scale, Health of the Nation Outcome Scale, and Clinical Global Impression-Improvement (CGI-I), along with quality-of-life measures such as the WHO Disability Assessment Schedule, WHO Quality of Life scale, and Multidimensional Scale of Perceived Social Support (MSPSS) were evaluated over a 3-month period. Statistical analyses, including analysis of covariance and logistic regression, were used to determine the effectiveness of S-ICM. Results: The S-ICM group had significantly lower odds of self-harm or suicidal attempts compared to the control group (adjusted odds ratio [aOR]=0.30, 95% confidence interval [CI]: 0.21–1.38). Psychiatric symptoms measured by the BPRS and perceived social support measured by the MSPSS significantly improved in the S-ICM group. The S-ICM group also had significantly higher odds of CGI-I compared to the control group (aOR=8.20, 95% CI: 2.66–25.32). Conclusion This study provides inaugural evidence on the effectiveness of S-ICM services, supporting their standardization and potential nationwide expansion.

Background: The purpose of our study was to analyze the effects of hospital volume and region on in-hospital and long-term mortality, direct medical costs (DMCs), and length of hospital stay (LOS) in elderly patients following hip fracture, utilizing nationwide claims data. Methods: This retrospective nationwide study sourced its subjects from the Korean National Health Insurance Review and Assessment Service database spanning from January 2011 to December 2018. A generalized estimating equation model with a Poisson distribution and logarithmic link function was used to estimate adjusted odds ratios (aORs) and 95% CIs to assess the association of hospital volume with in-hospital and 1-year mortality, DMCs, and LOS . Results: A total of 172,144 patients were included. Comparing the risk of in-hospital death between high-volume and low-volume hospitals, the risk of in-hospital death was 1.2 times higher at low-volume hospitals (aOR, 1.20; 95% CI, 1.07–1.33; p = 0.002).Additionally, the risk of death at 1 year was 1.05 times higher at low-volume hospitals (aOR, 1.05; 95% CI, 1.01–1.09; p = 0.008) compared to high-volume hospitals. DMCs were 0.84 times lower at low-volume hospitals for in-hospital period (aOR, 0.84; 95% CI, 0.84–0.85; p < 0.001) and 0.87 times lower for 1 year (aOR, 0.87; 95% CI, 0.86–0.88; p < 0.001) compared to high-volume hospitals. In-hospital LOS was 1.21 times longer at low-volume hospitals (aOR, 1.21; 95% CI, 1.20–1.22; p < 0.001) than at high-volume hospitals. In addition, the risk of in-hospital death was 1.22 times higher (aOR, 1.22; 95% CI, 1.12–1.33; p < 0.001) and the risk of 1-year death was 1.07 times higher (aOR, 1.07; 95% CI, 1.04–1.10; p < 0.001) at rural hospitals compared to urban hospitals. Conclusions Clinicians should focus on improving clinical outcomes for hip fracture patients in low-volume and rural hospital settings, with a specific emphasis on reducing mortality rates.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

The Korean Enhanced Recovery After Surgery (ERAS) Committee within the Korean Society of Surgical Metabolism and Nutrition was established to develop ERAS guidelines tailored to the Korean context. This guideline focuses on creating the most current evidence-based practice guidelines for ERAS purposes, based on systematic reviews. All key questions targeted randomized controlled trials exclusively, and if fewer than 2 were available, studies employing propensity score matching were also included. Recommendations for each key question were marked with strength of recommendation and level of evidence following internal and external review processes by the committee.

The Korean Enhanced Recovery After Surgery (ERAS) Committee within the Korean Society of Surgical Metabolism and Nutrition was established to develop ERAS guidelines tailored to the Korean context. This guideline focuses on creating the most current evidence-based practice guidelines for ERAS purposes, based on systematic reviews. All key questions targeted randomized controlled trials exclusively, and if fewer than 2 were available, studies employing propensity score matching were also included. Recommendations for each key question were marked with strength of recommendation and level of evidence following internal and external review processes by the committee.

Background: The purpose of our study was to analyze the effects of hospital volume and region on in-hospital and long-term mortality, direct medical costs (DMCs), and length of hospital stay (LOS) in elderly patients following hip fracture, utilizing nationwide claims data. Methods: This retrospective nationwide study sourced its subjects from the Korean National Health Insurance Review and Assessment Service database spanning from January 2011 to December 2018. A generalized estimating equation model with a Poisson distribution and logarithmic link function was used to estimate adjusted odds ratios (aORs) and 95% CIs to assess the association of hospital volume with in-hospital and 1-year mortality, DMCs, and LOS . Results: A total of 172,144 patients were included. Comparing the risk of in-hospital death between high-volume and low-volume hospitals, the risk of in-hospital death was 1.2 times higher at low-volume hospitals (aOR, 1.20; 95% CI, 1.07–1.33; p = 0.002).Additionally, the risk of death at 1 year was 1.05 times higher at low-volume hospitals (aOR, 1.05; 95% CI, 1.01–1.09; p = 0.008) compared to high-volume hospitals. DMCs were 0.84 times lower at low-volume hospitals for in-hospital period (aOR, 0.84; 95% CI, 0.84–0.85; p < 0.001) and 0.87 times lower for 1 year (aOR, 0.87; 95% CI, 0.86–0.88; p < 0.001) compared to high-volume hospitals. In-hospital LOS was 1.21 times longer at low-volume hospitals (aOR, 1.21; 95% CI, 1.20–1.22; p < 0.001) than at high-volume hospitals. In addition, the risk of in-hospital death was 1.22 times higher (aOR, 1.22; 95% CI, 1.12–1.33; p < 0.001) and the risk of 1-year death was 1.07 times higher (aOR, 1.07; 95% CI, 1.04–1.10; p < 0.001) at rural hospitals compared to urban hospitals. Conclusions Clinicians should focus on improving clinical outcomes for hip fracture patients in low-volume and rural hospital settings, with a specific emphasis on reducing mortality rates.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Background/Aims: The study investigated the incidence, risk factors, and clinical outcomes of chronic antibiotic-refractory pouchitis (CARP) in Korean patients with ulcerative colitis (UC). Methods: This single-center retrospective study included patients with UC who underwent total proctocolectomy with ileal pouch-anal anastomosis at the Asan Medical Center in Korea between January 1987 and December 2022. The primary outcomes were endoscopic remission and pouch failure. The Cox’s proportional hazard model was used to identify the risk factors for CARP. Results: The clinical data of 232 patients were analyzed. The most common cause of surgery was steroid refractoriness (50.9%), followed by dysplasia/colorectal cancer (26.7%). Among 74 patients (31.9%) with chronic pouchitis (CP), 31 (13.4%) had CARP, and 43 (18.5%) had chronic antibiotic-dependent pouchitis (CADP). The most frequent endoscopic phenotype was focal inflammation of the pouch (CP, 47.3%; CARP, 35.5%; CADP, 55.8%). Patients with CARP were less likely to use concomitant probiotics than patients with CADP (29.0% vs 72.1%, p<0.01). The endoscopic remission rate of CP, CARP, and CADP was 14.9%, 9.7%, and 18.6%, respectively.The pouch failure rate associated with CP, CARP, and CADP was 13.5%, 16.1%, and 11.6%, respectively. Current smoking status (adjusted hazard ratio [aHR], 2.96; 95% confidence interval [CI], 1.27 to 6.90; p=0.01) and previous use of biologics/small molecules (aHR, 2.40; 95% CI, 1.05 to 5.53; p=0.04) were significantly associated with CARP development. Conclusions UC patients who were current smokers and previously used biologics/small molecules had a higher risk of developing CARP. Concomitant use of probiotics was less likely to be associated with CARP development.