Background:The Integrated Nursing Care Service (INCS) provides 24-hour nurseled inpatient care, aiming to establish patient-centered care and improve healthcare quality.As the program expands nationwide, evaluating whether INCS truly enhances patients' perceived quality of care and behavioral intentions-such as satisfaction, trust, and willingness to recommend-is essential to justify policy continuity and further investment.Methods: This study analyzed data from 375 adult inpatients extracted from the 2023 Korean Medical Service Experience Survey, jointly conducted by the Ministry of Health and Welfare and the Korea Institute for Health and Social Affairs.The dependent variables were overall satisfaction, intention to recommend the hospital, nursing service, and hospital environment, all measured on a 5-point Likert scale.The independent variable was utilization of INCS.Descriptive statistics, one-way ANOVA, and ordinal logistic regression analyses were conducted to examine differences and associations.Results: Patients receiving care under INCS reported significantly higher odds of hospital recommendation (OR=1.80,95% CI=1.13-2.88).Within the nursing domain, responsiveness (OR=2.28,95% CI=1.42-3.71)and discharge explanation (OR=2.07,95% CI=1.29-3.37)were key contributors to satisfaction and behavioral intentions, defined as the intention to recommend the hospital.Among environmental factors, comfort (OR=2.42,95% CI=1.52-3.90),reduced nighttime noise (OR=1.87,95% CI=1.22-2.86),and physical privacy (OR=2.05,95% CI=1.28-3.32)significantly improved patient experience scores.Conclusion: These findings empirically demonstrate that nurse-led integrated care (in which registered nurses provide both nursing and caregiving services without family or private caregivers) improves both service experience and behavioral outcomes.INCS functions as an effective policy tool to enhance hospital quality and patient-centeredness. Expanding the program nationwide and establishing standardized ward operations are recommended to further strengthen patient trust, satisfaction, and overall quality of care.

The rapid spread of tools such as web scraping and automated macros has made it technically easy-but legally complex-to collect large volumes of healthrelated data from websites and online services.This review compares the principal frameworks in South Korea, the United States, and the European Union to identify conditions for lawful and ethical research use.Baseline privacy statutes (Korea's PIPA, U.S. HIPAA, EU GDPR), sectoral instruments, and enforcement trends reveal convergent requirements: (1) robust de-identification or pseudonymization; (2) a valid legal basis (explicit consent or statutory alternatives for scientific research in the public interest); (3) strict respect for access controls and anti-circumvention rules (no bypassing logins, CAPTCHAs, paywalls, or technical protection measures); (4) transparency and independent oversight (e.g., notices, data-subject rights handling, IRB/ethics review); and (5) safeguards for cross-border transfers, including emerging national-security limits on bulk health datasets.In South Korea, PIPA treats health information as sensitive; pseudonymized data may be used without consent for statistics, scientific research, or archiving under defined safeguards, while cross-controller combinations are confined to designated institutions and API-based sharing is preferred.In the U.S., HIPAA governs research uses by covered entities (authorization or IRB waiver), while non-HIPAA actors face FTC oversight; scraping of publicly accessible pages may avoid CFAA liability but still implicates DMCA and contract/tort claims.In the EU, GDPR requires both an Article 6 basis and an Article 9 condition, with Article 14 transparency even for indirectly collected data; database rights and text-and-data-mining (TDM) rules shape permissible extraction, and the EHDS will expand controlled research access via secure environments.Together, these regimes point to a risk-managed pathway for research that centers lawful sourcing, technical safeguards, and accountable governance.

Drug shortages are a major global public health threat that disrupts patient care; increases costs, medication errors, and mortality; and forces treatment interruptions.Several countries have adopted mandatory reporting systems and national monitoring platforms, thus highlighting the need for predictive analytics.Prediction research generally includes two approaches: forecasting demand and assessing supply disruption risks.Demand forecasting applies models such as the autoregressive integrated moving average to sales, search, and inventory data, whereas supply risk assessment uses machine learning and artificial intelligence to detect vulnerable drugs or suppliers, as shown in studies conducted in the U.S., Canada, China, and France.Combining these approaches could enable proactive shortage management.However, certain limitations remain, and limited data quality and accessibility hinder predictive accuracy.Centralizing shortage data from all stakeholders across the pharmaceutical supply chain, including manufacturers, distributors, healthcare providers, and patients, can improve data availability.Despite these advances, the predictive accuracy of current analytical tools is limited, thereby highlighting the need to explore more advanced tools.Overall, embedding predictive analytics tools into clinical practice is essential; however, their effectiveness remains a policy challenge.

Pregnant women comprise a high-risk population, and therefore timely evaluation of vaccine safety and effectiveness is essential for them.National health insurance claims data enable large-scale observational studies but often lack essential obstetric details.This review compares methods for assessing safety of pregnancy vaccines using claims data, with a focus on gestational age estimation.We propose a standardized approach using the Korea Disease Control and Prevention Agency-COVID-19-National Health Insurance Service cohort (K-COV-N cohort) motherinfant dataset, and discuss strategies to enhance the reliability and applicability of real-world vaccine safety assessments.We conducted a narrative review of the methods used to assess vaccine safety during pregnancy using Korean NHIS claims data.PubMed was searched with "vaccine", "safety", "pregnan*", "national, " and "insurance" to identify studies on maternal vaccine exposure and its impact on obstetric or neonatal outcomes.We focused on pregnancy outcome definitions, event dating, pregnancy onset estimations, and gestational age calculations.The methods used in the K-COV-N cohort were also reviewed, and preterm birth and congenital anomaly rates were compared with national statistics.Among the 24 studies found, four met our inclusion criteria (two from Korea, one from Taiwan, and one from France).Methods for estimating pregnancy start date varied.The Korean studies derived date of last menstrual period from first-trimester ultrasound data, whereas the Taiwanese and French studies used gestational age and physician records.In the K-COV-N cohort, the calculated preterm birth rate was 6.6%, and the congenital anomaly rate ranged from 6.8% to 7.2%, consistent with national statistics.Gestational age estimation using first-trimester ultrasonography aligns with national statistics and enables rapid vaccine safety assessment; however, further validation is recommended.

Background: Outpatient appointments scheduled through the emergency department (ED) differ from routine appointments made via follow-ups or selfscheduling systems, as they are arranged at discharge following emergency care.Despite their importance in ensuring continuity of care, ED-scheduled appointments often face higher no-show rates.This study aimed to analyze the noshow rate and identify the key factors influencing appointment adherence among patients discharged from the ED.Methods: A retrospective analysis was conducted on outpatient appointments scheduled through the ED of a tertiary hospital in Seoul, Korea, between January 1 and December 31, 2019.Of the 21,634 ED-scheduled appointments, 10,751 were included from six alternating months.Sociodemographic, appointment-related, and clinical characteristics were analyzed.Multivariate logistic regression was used to determine significant predictors of no-show behavior. Results:The overall adherence rate for ED-scheduled outpatient appointments was 43.6%, with a no-show rate of 56.4%, which was substantially higher than that for general outpatient appointments.Significant factors associated with no-shows included alcohol use, lower patient acuity based on the Korean triage and acuity scale (KTAS), and appointments arranged by emergency medicine physicians.Conversely, patients who underwent complex procedures such as suturing or minor surgeries demonstrated better adherence.Waiting time was also a critical factor, with no-show rates exceeding 80% when the waiting period was longer than two weeks. Conclusion:This study highlights the need for targeted interventions to reduce the no-show rates in ED-scheduled outpatient appointments.Tailored strategies based on patient characteristics and appointment conditions are essential for improving scheduling efficiency and continuity of care.Further research across diverse healthcare settings is recommended to validate these findings and inform broader policy developments.

Risk adjustment, which reflects the baseline characteristics and risk factors of a patient, plays a crucial role in ensuring fair hospital performance assessment.This study aimed to examine risk adjustment methods in the National Quality Assessment Program (NQAP) of the Health Insurance Review & Assessment Service (HIRA) and provide recommendations.The goal of the NQAP is to enhance the quality of healthcare services and improve patient safety.The outcome indicators measured in the NQAP were extracted from the Quality Assessment Information Bank System operated by the HIRA.A literature review was conducted to identify the data sources, statistical methods, and risk factors of the risk-adjustment models for these outcome indicators.As of 2024, among the 36 assessment items in the NQAP, 11 have applied risk adjustment models to calculate outcome indicators.A total of 23 outcome indicators were adjusted for patient severity, most of which were indicators of mortality or readmission.Logistic and linear regression models were applied to develop risk adjustment models, and the final models were established through model validation.The risk adjustment models included demographic factors, clinical factors, and medical history or comorbidities.Comprehensive data collection incorporating clinical evidence and data quality management is essential for risk adjustment.Moreover, regularly updating models based on the latest data is crucial to ensure the accuracy and reliability of the assessment results.

Background: Next generation sequencing (NGS) technology allows precise genetic analysis and has advanced personalized medicine by reducing diagnostic time and cost.Although NGS identifies genetic mutations for tailored treatments of non-small-cell lung cancer (NSCLC), its cost-effectiveness remains underexplored in South Korea.Methods: This retrospective study analyzed patients with NSCLC between 2018 and 2024.The patients were divided into two groups: NGS-based diagnosis and traditional diagnostic methods.Propensity score matching ensured comparable baseline characteristics, including survival duration.Total treatment costs, including out-of-pocket expenses and insurance contributions, were evaluated with a focus on comparable survival durations.Results: The NGS group incurred higher initial costs due to diagnostic and targeted therapy expenses.However, from the third year onwards, the total treatment costs for the NGS group were consistently lower than those for the traditional diagnostic method group, when compared over equivalent survival durations.Over five years, the NGS group demonstrated superior cost efficiency, emphasizing its long-term economic benefits.Conclusion: Although upfront costs were higher, NGS is economically advantageous by reducing the cumulative costs for equivalent survival durations after the third year.These findings highlight the importance of expanding the application of NGS in South Korea and its cost-effectiveness in personalized oncology.

Background: Pilot projects in Korea's National Health Insurance (KNHI) system have been instrumental in testing innovative healthcare policies.However, frequent personnel changes and inconsistent evaluation mechanisms challenge the continuity and effectiveness of these projects.This study aims to perform a metaevaluation of pilot projects to assess their implementation, evaluation, and policy implications.Methods: A meta-evaluation framework was developed to analyze 49 pilot projects conducted between 2014 and 2023 focusing on three key elements: inputs, processes, and outcomes.Data were collected from publicly available reports and government records.Key evaluation components included input resources, process integrity, and outcome feedback.Mixed methods, combining quantitative and qualitative data, were utilized.Results: The analysis revealed inconsistencies in the allocation of evaluation personnel and a lack of independence of the evaluating organizations.As for the evaluation processes, randomized and quasi-experimental designs were generally employed, ensuring objective evaluation methods, and appropriate indicators and analytical approaches were largely utilized.However, for evaluation outcomes and feedback, only 11 of 49 projects demonstrated effective feedback implementation, and transparency of the evaluation reports was identified as a significant issue.Conclusion: Despite their strategic importance, KNHI pilot projects face systemic issues in evaluation and continuity.Enhancing evaluation transparency, establishing consistent guidelines, and addressing staffing challenges are crucial for maximizing their policy impact.This study underscores the need for a more structured evaluation system to ensure the sustainability and scalability of pilot projects.

The significance and quality of evidence-based healthcare systems are crucial to the effectiveness of healthcare delivery.In 2005, the Health Insurance Review & Assessment Service (HIRA) developed the Evidence Based Review Manual (EBRM) to provide scientifically grounded information that supports efficient benefit reviews, rational decision-making, and the evaluation of reimbursement systems.This article presents the latest insights from the 2024 7th edition of the EBRM.For this revision, we consulted systematic review methodologies employed internationally as well as policy data related to decision-making processes and pertinent academic journals.In the 7th edition, we updated the tools for assessing the risk of bias in clinical research and revisited the criteria for the decisionmaking process.The EBRM emphasizes the inclusion of textbooks, clinical practice guidelines, and clinical research literature as sources of scientific evidence.To facilitate this, the manual introduces a standardized, highly practical method for presenting evidence.Currently, various HIRA committees base reimbursement policy decisions on the EBRM, including rapid systematic reviews.We also reviewed the RAND/UCLA Appropriateness Method (RAM), a modified Delphi survey method used in the United States.Implementing the structured RAM would enhance the scientific rigor and transparency of HIRA's decision-making processes.As evidence-based decision-making systems expand within healthcare, it is essential that knowledge remains current and is continuously updated and disseminated.Accordingly, the 7th edition of the EBRM will be revised to improve both its internal and external application and to support the ongoing dissemination of scientific knowledge.

Background:The inpatient nursing management fee differentiation system adjusts inpatient fees based on nurse staffing levels.In January 2024, the policy was revised, and the grading criteria for tertiary hospitals and medical institutions in Seoul were changed from "beds-to-nurses" to "patients-to-nurses. " This study therefore aimed to analyze changes in grade distribution and nurse staffing levels following the policy revision, focusing on these institutions.Methods: "Healthcare institution status reports, " "Inpatient nursing management fee differentiation assessment records, " and "National Health Insurance claim data" from the fourth quarter of 2023 to the second quarter of 2024 were analyzed.The study assessed the distribution of healthcare institutions according to nursing grade and changes in nurse staffing levels using descriptive statistics and ANOVA.Nurse staffing levels were calculated as the "beds-to-nurses" ratio.Results: After the revision, nursing grades improved across all medical institutions.All tertiary hospitals were classified as either grade S or A, with a "patients-to-nurses" ratio below 2.0:1.In general hospitals, 39.5% and 18.6% were classified as grade A and S, respectively.For hospital-level institutions, the proportion in the top two grades increased significantly from 14.7% to 46.0%.Despite the shift in grading criteria, the average number of nurses in tertiary hospitals and Seoul institutions decreased in 2024.As such, no significant changes in nurse staffing levels were observed. Conclusion:The 2024 revision of the inpatient nursing grade system improved the compensation for nursing workloads but did not immediately increase nurse staffing levels.Long-term data analysis is needed to assess whether fee adjustments raised staffing levels and enhanced nursing services and inpatient care quality.