Essential medical care is a concept referring to indispensable medical care. World Health Organization has defined essential healthcare services as the evidence-based technologies needed to cost-effectively solve health problems. However, most countries have no such term that describes essential healthcare services. Rather, it has been used to efficiently allocate medical resources in countries with limited resources. Therefore, a clear definition of essential healthcare services must be accompanied by a specific purpose and a clear direction for the need. The target to whom medical services are to be provided, the region and institution to be provided, the content of medical services, and the purpose of provision should be distinguished. Recently, as public interest in essential healthcare services has increased, the government announced “the Essential Healthcare Policy Package” and “the 1st Healthcare Reform Implementation Plan” to specifically promote it. But concerns of feasibility and financial estimates are being raised. So, here, I analyze the government policy package and its 1st implantation plan, and suggest policy proposals for them.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Background/Aims: Elevated troponin levels predict in-hospital mortality and influence decisions regarding thrombolytic therapy in patients with acute pulmonary embolism (PE). However, the usefulness of high-sensitivity troponin T (hsTnT) regarding PE remains uncertain. We aimed to establish the optimal cut-off level and compare its performance for precise risk stratification. Methods: 374 patients diagnosed with acute PE were reviewed. PE-related adverse outcomes, a composite of PE-related deaths, cardiopulmonary resuscitation incidents, systolic blood pressure < 90 mmHg, and all-cause mortality within 30 days were evaluated. The optimal hsTnT cut-off for all-cause mortality, and the net reclassification index (NRI) was used to assess the incremental value in risk stratification. Results: Among 343 normotensive patients, 17 (5.0%) experienced all-cause mortality, while 40 (10.7%) had PE-related adverse outcomes. An optimal hsTnT cut-off value of 60 ng/L for all-cause mortality (AUC 0.74, 95% CI 0.61–0.85, p < 0.001) was identified, which was significantly associated with PE-related adverse outcomes (OR 4.07, 95% CI 2.06–8.06, p < 0.001). Patients with hsTnT ≥ 60 ng/L were older, hypotensive, had higher creatinine levels, and right ventricular dysfunction signs. Combining hsTnT ≥ 60 ng/L with simplified pulmonary embolism severity index ≥1 provided additional prognostic information. Reclassification analysis showed a significant shift in risk categories, with an NRI of 1.016 ± 0.201 (p < 0.001). Conclusions We refined troponin’s predictive value in patients with acute PE, proposing a new cut-off value of hsTnT ≥ 60 ng/L. Validation through large-scale studies is essential to offer clinically useful guidance for managing patient population.

Essential medical care is a concept referring to indispensable medical care. World Health Organization has defined essential healthcare services as the evidence-based technologies needed to cost-effectively solve health problems. However, most countries have no such term that describes essential healthcare services. Rather, it has been used to efficiently allocate medical resources in countries with limited resources. Therefore, a clear definition of essential healthcare services must be accompanied by a specific purpose and a clear direction for the need. The target to whom medical services are to be provided, the region and institution to be provided, the content of medical services, and the purpose of provision should be distinguished. Recently, as public interest in essential healthcare services has increased, the government announced “the Essential Healthcare Policy Package” and “the 1st Healthcare Reform Implementation Plan” to specifically promote it. But concerns of feasibility and financial estimates are being raised. So, here, I analyze the government policy package and its 1st implantation plan, and suggest policy proposals for them.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Background/Aims: Elevated troponin levels predict in-hospital mortality and influence decisions regarding thrombolytic therapy in patients with acute pulmonary embolism (PE). However, the usefulness of high-sensitivity troponin T (hsTnT) regarding PE remains uncertain. We aimed to establish the optimal cut-off level and compare its performance for precise risk stratification. Methods: 374 patients diagnosed with acute PE were reviewed. PE-related adverse outcomes, a composite of PE-related deaths, cardiopulmonary resuscitation incidents, systolic blood pressure < 90 mmHg, and all-cause mortality within 30 days were evaluated. The optimal hsTnT cut-off for all-cause mortality, and the net reclassification index (NRI) was used to assess the incremental value in risk stratification. Results: Among 343 normotensive patients, 17 (5.0%) experienced all-cause mortality, while 40 (10.7%) had PE-related adverse outcomes. An optimal hsTnT cut-off value of 60 ng/L for all-cause mortality (AUC 0.74, 95% CI 0.61–0.85, p < 0.001) was identified, which was significantly associated with PE-related adverse outcomes (OR 4.07, 95% CI 2.06–8.06, p < 0.001). Patients with hsTnT ≥ 60 ng/L were older, hypotensive, had higher creatinine levels, and right ventricular dysfunction signs. Combining hsTnT ≥ 60 ng/L with simplified pulmonary embolism severity index ≥1 provided additional prognostic information. Reclassification analysis showed a significant shift in risk categories, with an NRI of 1.016 ± 0.201 (p < 0.001). Conclusions We refined troponin’s predictive value in patients with acute PE, proposing a new cut-off value of hsTnT ≥ 60 ng/L. Validation through large-scale studies is essential to offer clinically useful guidance for managing patient population.

Purpose: This study aimed to identify the risk factors for peripheral intravenous catheter-related phlebitis in hospitalized neurosurgery patients. Methods: This study involved 443 neurosurgery patients who were admitted to a general hospital in Seoul. The analysis included 982 intravenous lines. Data were retrospectively extracted from electronic medical records for the period between November 1, 2022, and May 31, 2023. Data were analyzed using descriptive statistics, independent t-test, x 2 test, and logistic regression. Results: The incidence rate of phlebitis was 13.6%, with the majority of cases classified as grade 2. Gender, present diseases, length of hospital stay, needle gauge size, and cardiovascular drugs were identified as risk factors for phlebitis in neurosurgery patients. Conclusion This study is significant as it provides basic data for the prevention and management of peripheral phlebitis in hospitalized neurosurgery patients. The risk factors identified in this study should be incorporated into nursing education to implement a systematic peripheral intravenous management program, and appropriate peripheral intravenous catheter-related nursing interventions are necessary based on the characteristics of each patient.

South Korea’s current vaccination policies leave a surveillance gap for non-National Immunization Program (NIP) vaccines. In this study, we proposed a sentinel surveillance approach for monitoring the safety of non-NIP vaccines. Vaccination data were collected retrospectively among patients hospitalized with pre-defined adverse events of special interest (AESI) by reviewing electronic medical records in five university hospitals. This approach incorporates expert assessment to determine the causal relationship. We confirmed that 16 patients had received non-NIP vaccines among 860 patients diagnosed with AESI.We concluded one case of preeclampsia was possibly related to tetanus-diphtheria-pertussis vaccination. We propose a multi-hospital-based, retrospective assessment system for predefined AESIs as an alternative to active vaccine safety monitoring method. These efforts are expected to enhance both the accuracy and timeliness of safety monitoring in South Korea.