Objectives: We aimed to derive observational research evidence on treatment patterns through a scoping review of common data model (CDM)-based publications. Methods: We searched the medical literature databases PubMed and EMBASE, as well as the Observational Health Data Sciences and Informatics (OHDSI) website, for papers published between January 1, 2010 and August 21, 2023 to identify research papers relevant to our topic. Results: Eighteen articles satisfied the inclusion criteria for this scoping review. We summarized study characteristics such as phenotypes, patient numbers, data periods, countries, Observational Medical Outcomes Partnership (OMOP) CDM databases, and definitions of index date and target cohort. Type 2 diabetes mellitus emerged as the most frequently studied disease, covered in five articles, followed by hypertension and depression, each addressed in four articles. Biguanides, with metformin as the primary drug, were the most commonly prescribed first-line treatments for type 2 diabetes mellitus. Most studies utilized sunburst plots to visualize treatment patterns, whereas two studies used Sankey plots. Various software tools were employed for treatment pattern analysis, including JavaScript, the open-source ATLAS by OHDSI, R code, and the R package “TreatmentPatterns.” Conclusions This study provides a comprehensive overview of research on treatment patterns using the CDM, highlighting the growing importance of OMOP CDM in enabling multinational observational network studies and advancing collaborative research in this field.

Objectives: We aimed to derive observational research evidence on treatment patterns through a scoping review of common data model (CDM)-based publications. Methods: We searched the medical literature databases PubMed and EMBASE, as well as the Observational Health Data Sciences and Informatics (OHDSI) website, for papers published between January 1, 2010 and August 21, 2023 to identify research papers relevant to our topic. Results: Eighteen articles satisfied the inclusion criteria for this scoping review. We summarized study characteristics such as phenotypes, patient numbers, data periods, countries, Observational Medical Outcomes Partnership (OMOP) CDM databases, and definitions of index date and target cohort. Type 2 diabetes mellitus emerged as the most frequently studied disease, covered in five articles, followed by hypertension and depression, each addressed in four articles. Biguanides, with metformin as the primary drug, were the most commonly prescribed first-line treatments for type 2 diabetes mellitus. Most studies utilized sunburst plots to visualize treatment patterns, whereas two studies used Sankey plots. Various software tools were employed for treatment pattern analysis, including JavaScript, the open-source ATLAS by OHDSI, R code, and the R package “TreatmentPatterns.” Conclusions This study provides a comprehensive overview of research on treatment patterns using the CDM, highlighting the growing importance of OMOP CDM in enabling multinational observational network studies and advancing collaborative research in this field.

Objectives: We aimed to derive observational research evidence on treatment patterns through a scoping review of common data model (CDM)-based publications. Methods: We searched the medical literature databases PubMed and EMBASE, as well as the Observational Health Data Sciences and Informatics (OHDSI) website, for papers published between January 1, 2010 and August 21, 2023 to identify research papers relevant to our topic. Results: Eighteen articles satisfied the inclusion criteria for this scoping review. We summarized study characteristics such as phenotypes, patient numbers, data periods, countries, Observational Medical Outcomes Partnership (OMOP) CDM databases, and definitions of index date and target cohort. Type 2 diabetes mellitus emerged as the most frequently studied disease, covered in five articles, followed by hypertension and depression, each addressed in four articles. Biguanides, with metformin as the primary drug, were the most commonly prescribed first-line treatments for type 2 diabetes mellitus. Most studies utilized sunburst plots to visualize treatment patterns, whereas two studies used Sankey plots. Various software tools were employed for treatment pattern analysis, including JavaScript, the open-source ATLAS by OHDSI, R code, and the R package “TreatmentPatterns.” Conclusions This study provides a comprehensive overview of research on treatment patterns using the CDM, highlighting the growing importance of OMOP CDM in enabling multinational observational network studies and advancing collaborative research in this field.

This retrospective cohort study aimed to compare coronavirus disease 2019 (COVID-19)-related clinical outcomes between patients with and without gout. Electronic health recordbased data from two centers (Seoul National University Hospital [SNUH] and Boramae Medical Center [BMC]), from January 2021 to April 2022, were mapped to a common data model. Patients with and without gout were matched using a large-scale propensityscore algorithm based on population-level estimation methods. At the SNUH, the risk for COVID-19 diagnosis was not significantly different between patients with and without gout (hazard ratio [HR], 1.07; 95% confidence interval [CI], 0.59–1.84). Within 30 days after COVID-19 diagnosis, no significant difference was observed in terms of hospitalization (HR, 0.57; 95% CI, 0.03–3.90), severe outcomes (HR, 2.90; 95% CI, 0.54–13.71), or mortality (HR, 1.35; 95% CI, 0.06–16.24). Similar results were obtained from the BMC database, suggesting that gout does not increase the risk for COVID-19 diagnosis or severe outcomes.

Objectives: In the era of the Fourth Industrial Revolution, where an ecosystem is being developed to enhance the quality of healthcare services by applying information and communication technologies, systematic and sustainable data management is essential for medical institutions. In this study, we assessed the data management status and emerging concerns of three medical institutions, while also examining future directions for seamless data management. Methods: To evaluate the data management status, we examined data types, capacities, infrastructure, backup methods, and related organizations. We also discussed challenges, such as resource and infrastructure issues, problems related to government regulations, and considerations for future data management. Results: Hospitals are grappling with the increasing data storage space and a shortage of management personnel due to costs and project termination, which necessitates countermeasures and support. Data management regulations on the destruction or maintenance of medical records are needed, and institutional consideration for secondary utilization such as long-term treatment or research is required. Government-level guidelines for facilitating hospital data sharing and mobile patient services should be developed. Additionally, hospital executives at the organizational level need to make efforts to facilitate the clinical validation of artificial intelligence software. Conclusions This analysis of the current status and emerging issues of data management reveals potential solutions and sets the stage for future organizational and policy directions. If medical big data is systematically managed, accumulated over time, and strategically monetized, it has the potential to create new value.

Objectives: To assess the prevalence of probable sarcopenia according to 3 different definitions (“strength, assistance with walking, rise from a chair, climb stairs, falls”- SARC-F score, low grip strength, and the guidelines indicated by the European Working Group on Sarcopenia in Older People 2 - EWGSOP2) and assess the association of probable sarcopenia with functional disability and falls among community-dwelling older adults. Methods: Cross-sectional study with 419 older adults. Probable sarcopenia was assessed by 3 definitions: a SARC-F 4, low grip strength (< 27 kg for men and < 16 kg for women), and the EWGSOP2 criteria. Associations were investigated using Pearson's chi-square test and prevalence ratios were estimated by Poisson regression (P < 0.05). Results: Of the total, probable sarcopenia was identified in 23.0% of participants (SARC-F 4 score), 33.7% (low grip strength), and 10.4% (EWGSOP2) according to each different definition. In adjusted regression models, having at least 1 instrumental activities of daily living (IADL) disability and having fallen in the last 12 months were significantly associated with a SARC-F 4 (prevalence ratio, PR ¼ 1.60; and PR ¼ 2.50, respectively) and EWGSOP2 (PR ¼ 1.78; and PR ¼ 2.19, respectively). Conclusions IADL disability and falls were associated with a SARC-F 4 and the EWGSOP2 criteria (SARC-F 4 and low grip strength). Probable sarcopenia may be used in clinical practice in order to facilitate the diagnosis of definite sarcopenia and to implement early interventions that could prevent functional decline and falls in older people.