OBJECTIVES: To reveal differences in drug-drug interaction (DDI) alerts and the reasons for alert overrides between admitting departments. METHODS: A retrospective observational study was performed using longitudinal Electronic Health Record (EHR) data and information from an alert and logging system. Adult patients hospitalized in the emergency department (ED) and general ward (GW) during a 46-month period were included. For qualitative analyses, we manually reviewed all reasons for alert overrides, which were recorded as free text in the EHRs. RESULTS: Among 14,780,519 prescriptions, 51,864 had alerts for DDIs (0.35%; 1.32% in the ED and 0.23% in the GW). The alert override rate was higher in the ED (94.0%) than in the GW (57.0%) (p < 0.001). In an analysis of the study population, including ED and GW patients, 'clinically irrelevant alert' (52.0%) was the most common reason for override, followed by 'benefit assessed to be greater than the risk' (31.1%) and 'others' (17.3%). The frequency of alert overrides was highest for anti-inflammatory and anti-rheumatic drugs (89%). In a sub-analysis of the population, 'clinically irrelevant alert' was the most common reason for alert overrides in the ED (69.3%), and 'benefit assessed to be greater than the risk' was the most common reason in the GW (61.4%). CONCLUSIONS: We confirmed that the DDI alerts and the reasons for alert overrides differed by admitting department. Different strategies may be efficient for each admitting department.
Admitting Department, Hospital* ; Adult ; Antirheumatic Agents ; Decision Support Systems, Clinical ; Drug Interactions ; Electronic Health Records ; Emergency Service, Hospital ; Humans ; Observational Study ; Patients' Rooms ; Prescriptions ; Retrospective Studies

The national licensing examination is used to evaluate the medical competency at the time of graduation, however no study has been performed on the validity of traditional Korean medicine license examination yet. The purpose of this study was to develop learning analytics using item response theory (IRT) to examine the validity and academic competency of the mock test of the national licensing exam. Classical test theory and IRT were used to evaluate the validity of test items, and IRT was used for test validity and competency analysis. The correlation between competency score of 12 subjects was analyzed using Pearson’s correlation. The distribution of students’ latent competencies was examined by gender and administrative group using a Kernel density map, Latent Profile Analysis, and χ2. The guessing parameter of 340 items was relatively high, and the information level of 12 subjects were relatively low. Significant correlations (r = 0.49–0.83, p < 0.05) were observed between the competency scores of total and 12 subjects. Two (high and low) latent academic competency groups were identified based on the competency score of 12 subjects. The low academic competency group requiring intensive management has a significantly higher frequency of male students with the experience of academic fail in the seven-year course. This study presented the quantitative learning analytics for the national licensing exam of traditional Korean medicine. The multifaceted item and test validities of the mock license test were provided, and an evidence-based approach to competency-based student management and national licensing exam of traditional Korean medicine was suggested.

Background: Obesity and weight loss are associated with increased mortality. Understanding the association between weight change and mortality is critical and can help inform effective prevention and intervention strategies. Therefore, this study aimed to investigate the association between weight change and mortality based on body mass index (BMI) intervals using data from a 12-year follow-up survey in Korea. Methods: We used data from the Korean Longitudinal Study of Aging from 2006 to 2018. Individuals aged 45–69 years without a history of malignancy and chronic obstructive pulmonary disease at baseline were selected. Cox regression analysis was used to compare mortality based on body mass index and weight change. Results: Compared with individuals with a body mass index of 20.0–25.0 kg/m2 and an increase in body weight of <5 kg, mortality was 3.8 times higher in the group with a body mass index of <20.0 kg/m2 and a weight loss of <5 kg, two times higher in the group with a body mass index of 20.0–25.0 kg/m2 and weight loss of >10 kg, and 4.3 times higher in the group with a body mass index of ≥25.0 kg/m2 and weight gain of ≥10 kg. Conclusions Weight loss in underweight or normal-weight individuals and weight gain in individuals with obesity increased the mortality rate compared with individuals with normal weight and less weight change. This suggests that body weight and the changes in the weight of individuals are crucial, and weight loss in patients with underweight and weight gain in patients with obesity are closely related to increased mortality.

Background: Obesity and weight loss are associated with increased mortality. Understanding the association between weight change and mortality is critical and can help inform effective prevention and intervention strategies. Therefore, this study aimed to investigate the association between weight change and mortality based on body mass index (BMI) intervals using data from a 12-year follow-up survey in Korea. Methods: We used data from the Korean Longitudinal Study of Aging from 2006 to 2018. Individuals aged 45–69 years without a history of malignancy and chronic obstructive pulmonary disease at baseline were selected. Cox regression analysis was used to compare mortality based on body mass index and weight change. Results: Compared with individuals with a body mass index of 20.0–25.0 kg/m2 and an increase in body weight of <5 kg, mortality was 3.8 times higher in the group with a body mass index of <20.0 kg/m2 and a weight loss of <5 kg, two times higher in the group with a body mass index of 20.0–25.0 kg/m2 and weight loss of >10 kg, and 4.3 times higher in the group with a body mass index of ≥25.0 kg/m2 and weight gain of ≥10 kg. Conclusions Weight loss in underweight or normal-weight individuals and weight gain in individuals with obesity increased the mortality rate compared with individuals with normal weight and less weight change. This suggests that body weight and the changes in the weight of individuals are crucial, and weight loss in patients with underweight and weight gain in patients with obesity are closely related to increased mortality.

Background: Obesity and weight loss are associated with increased mortality. Understanding the association between weight change and mortality is critical and can help inform effective prevention and intervention strategies. Therefore, this study aimed to investigate the association between weight change and mortality based on body mass index (BMI) intervals using data from a 12-year follow-up survey in Korea. Methods: We used data from the Korean Longitudinal Study of Aging from 2006 to 2018. Individuals aged 45–69 years without a history of malignancy and chronic obstructive pulmonary disease at baseline were selected. Cox regression analysis was used to compare mortality based on body mass index and weight change. Results: Compared with individuals with a body mass index of 20.0–25.0 kg/m2 and an increase in body weight of <5 kg, mortality was 3.8 times higher in the group with a body mass index of <20.0 kg/m2 and a weight loss of <5 kg, two times higher in the group with a body mass index of 20.0–25.0 kg/m2 and weight loss of >10 kg, and 4.3 times higher in the group with a body mass index of ≥25.0 kg/m2 and weight gain of ≥10 kg. Conclusions Weight loss in underweight or normal-weight individuals and weight gain in individuals with obesity increased the mortality rate compared with individuals with normal weight and less weight change. This suggests that body weight and the changes in the weight of individuals are crucial, and weight loss in patients with underweight and weight gain in patients with obesity are closely related to increased mortality.

Background: Obesity and weight loss are associated with increased mortality. Understanding the association between weight change and mortality is critical and can help inform effective prevention and intervention strategies. Therefore, this study aimed to investigate the association between weight change and mortality based on body mass index (BMI) intervals using data from a 12-year follow-up survey in Korea. Methods: We used data from the Korean Longitudinal Study of Aging from 2006 to 2018. Individuals aged 45–69 years without a history of malignancy and chronic obstructive pulmonary disease at baseline were selected. Cox regression analysis was used to compare mortality based on body mass index and weight change. Results: Compared with individuals with a body mass index of 20.0–25.0 kg/m2 and an increase in body weight of <5 kg, mortality was 3.8 times higher in the group with a body mass index of <20.0 kg/m2 and a weight loss of <5 kg, two times higher in the group with a body mass index of 20.0–25.0 kg/m2 and weight loss of >10 kg, and 4.3 times higher in the group with a body mass index of ≥25.0 kg/m2 and weight gain of ≥10 kg. Conclusions Weight loss in underweight or normal-weight individuals and weight gain in individuals with obesity increased the mortality rate compared with individuals with normal weight and less weight change. This suggests that body weight and the changes in the weight of individuals are crucial, and weight loss in patients with underweight and weight gain in patients with obesity are closely related to increased mortality.

OBJECTIVES: A distributed research network (DRN) has the advantages of improved statistical power, and it can reveal more significant relationships by increasing sample size. However, differences in data structure constitute a major barrier to integrating data among DRN partners. We describe our experience converting Electronic Health Records (EHR) to the Observational Health Data Sciences and Informatics (OHDSI) Common Data Model (CDM). METHODS: We transformed the EHR of a hospital into Observational Medical Outcomes Partnership (OMOP) CDM ver. 4.0 used in OHDSI. All EHR codes were mapped and converted into the standard vocabulary of the CDM. All data required by the CDM were extracted, transformed, and loaded (ETL) into the CDM structure. To validate and improve the quality of the transformed dataset, the open-source data characterization program ACHILLES was run on the converted data. RESULTS: Patient, drug, condition, procedure, and visit data from 2.07 million patients who visited the subject hospital from July 1994 to November 2014 were transformed into the CDM. The transformed dataset was named the AUSOM. ACHILLES revealed 36 errors and 13 warnings in the AUSOM. We reviewed and corrected 28 errors. The summarized results of the AUSOM processed with ACHILLES are available at http://ami.ajou.ac.kr:8080/. CONCLUSIONS: We successfully converted our EHRs to a CDM and were able to participate as a data partner in an international DRN. Converting local records in this manner will provide various opportunities for researchers and data holders.
Clinical Coding ; Data Accuracy* ; Dataset ; Electronic Health Records* ; Epidemiologic Methods ; Hospitals, Teaching* ; Humans ; Informatics ; Sample Size ; Vocabulary

Objective: We aimed to investigate the oncologic outcomes of patients with endometrial cancer who underwent sentinel lymph node (SLN) biopsy without ultrastaging compared with that of those who underwent lymphadenectomy (LND). Methods: Patients with endometrial cancer who underwent staging with SLN biopsy or LND during 2006 – 2021 were analyzed using propensity score matching (PSM). SLN metastasis was examined using hematoxylin and eosin staining, without ultrastaging. Progression-free survival (PFS) was compared between the two groups before and after PSM using age, histology, and stage as covariates. Clinical variables such as recurrence patterns and lymphatic complications, were assessed. Results: After excluding 213 patients who underwent validation LND with SLN biopsy, 902 were identified. The demographics of the remaining patients differed according to histology, myometrial invasion depth, and stage. Lymph node metastasis was less frequent in the SLN group than in the LND group (9.4% vs. 3.8%, p=0.004). The recurrence rates within 2 years were lower in the SLN group. The SLN group exhibited significantly superior 2-year and overall PFS than the LND group. Among patients with uterus-confined disease, overall PFS was favorable for SLN biopsy. After matching, differences in PFS were no longer observed, although the lymphocele and lymphedema rates were significantly lower in the SLN group. Conclusion In patients with endometrial cancer, SLN biopsy without ultrastaging did not compromise survival outcomes and was associated with significantly reduced lymphatic complication rates compared with LND. Therefore, SLN biopsy can be recommended for patients with endometrial cancer without definitive preoperative evidence of distant metastasis.

Purpose: Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of sentinel lymph nodes by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin–stained frozen tissue sections of SLNs in breast cancer patients. Materials and Methods: A total of 297 digital slides were obtained from frozen SLN sections, which include post–neoadjuvant cases (n = 144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for six weeks with two P40 GPUs. The algorithms were assessed in terms of the AUC (area under receiver operating characteristic curve). Results: The top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy. Conclusion In a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative sentinel lymph node biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting