Background: In February 2024, a nationwide resignation of resident physicians and fellows in South Korea caused a sudden disruption in the healthcare service delivery system. This study aimed to investigate how the crisis affected hospital admission patterns, treatment timelines, and early outcomes in patients with acute hemorrhagic stroke. Methods: We retrospectively analyzed data from prospective cohorts of patients diagnosed with intracerebral hemorrhage or subarachnoid hemorrhage admitted to a single cerebrovascular-specialty hospital between March 2023 and February 2025. Patients were categorized into two groups: those admitted before (Before crisis group, n = 130) and after (After crisis group, n = 214) the crisis. Clinical characteristics, regional distribution, time delays, and 3-month modified Rankin Scale (mRS) outcomes were compared. Results: Following the crisis, a significant increase was observed in admissions from outside the hospital’s primary coverage area (p < 0.001). Onset-to-arrival (138.0 vs. 92.0 min, p = 0.040) and onset-to-operation times (200.0 vs. 166.0 min, p = 0.046) were significantly delayed, particularly in patients who underwent surgical treatment. However, arrival-to-operation time remained stable (p = 0.694), and initial neurological severity was comparable. Functional outcomes at 3 months did not differ significantly (mRS 0–2: 53.8% vs. 50.5%, p = 0.157), indicating preserved in-hospital care quality, despite external disruption. Conclusions: The medical crisis disrupted the stroke care delivery system and delayed prehospital care in South Korea. Nevertheless, the cerebrovascular-specialty hospital maintained timely intervention and preserved outcomes. These findings support the strategic importance of decentralized specialty hospitals in ensuring the resilience of the healthcare service delivery system during a national healthcare crisis.

Background: While carotid pathologies are well-established risk factors for stroke, their specific effects on outcomes following stroke that cannot be classified as atherosclerotic remain unclear. In this study, we aimed to determine whether carotid pathologies are associated with functional dependence (FD) 3 months after small vessel occlusion (SVO) stroke. Methods: This retrospective study included patients with a first-ever SVO stroke admitted to a single cerebrovascular-specialty hospital between October 2021 and March 2024. Standardized ultrasound criteria were used to grade the carotid plaques. The modified Rankin scale (mRS) was used to assess functional outcomes at 3 months. Logistic regression analysis was performed to identify FD predictors (mRS of ≥2). Results: Of the 372 included patients, 276 achieved functional independence and 96 experienced FD at 3 months. Univariable analysis revealed an association between carotid plaque grade III and FD (odds ratio [OR], 2.46; 95% confidence interval [CI], 1.05–6.51; p = 0.049). However, this association was not significant in the multivariable model. Overall, age (adjusted OR, 1.07; 95% CI 1.03–1.10, p < 0.001), NIHSS at initial presentation (adjusted OR, 1.84; 95% CI, 1.55–2.18; p < 0.001), and diabetes (adjusted OR, 2.84; 95% CI, 1.37–5.92; p = 0.005) were independently associated with FD 3 months after SVO stroke. Conclusions: Carotid plaque severity was not independently associated with functional outcomes 3 months after SVO stroke. Age, NIHSS at initial presentation, and diabetes were identified as independent FD predictors. Future in-depth studies are warranted to confirm the complex interplay of factors influencing functional outcomes in patients with SVO stroke and carotid pathologies simultaneously.

Background/Objectives: Return to work (RTW) after an industrial accident is crucial for an individual’s well-being and socioeconomic recovery. This study investigated factors influencing RTW among workers who sustained physical or brain lesion-related disabilities following industrial accidents. Methods: Using five-year panel data (2018–2022) from the Panel Study of Workers’ Compensation Insurance of South Korea, we analyzed 340 individuals with physical or brain lesion-related disabilities sustained from industrial accidents. We used logistic regression models to identify factors associated with RTW and return to employed (RTE) status. Results: The RTW and non-RTW groups comprised 160 and 180 participants, respectively. Factors associated with non-RTW included female sex (adjusted odds ratio [aOR], 0.33; 95% confidence interval [CI], 0.13–0.86; p = 0.023), injury caused by disease (aOR, 0.18; 95% CI, 0.05–0.66; p = 0.010), long recovery periods (aOR, 0.27; 95% CI, 0.10–0.72; p = 0.009), low self-confidence (aOR, 0.16; 95% CI, 0.07–0.35; p < 0.001), and older age (aOR, 0.08; 95% CI, 0.02–0.34; p = 0.001). Workers with no blood pressure problems (aOR, 2.21; 95% CI, 1.11–4.38; p = 0.024) and longer employment durations (aOR, 3.84; 95% CI, 1.15–12.81; p = 0.029) had a higher chance of RTW. Similar factors were associated with RTE, with more emphasis on older age, long recovery periods, low self-confidence, and injury caused by disease. Conclusions: Our findings contribute to developing targeted support services and informing policy decisions to improve RTW for workers with physical or brain lesion-related disabilities caused by industrial accidents.

Stroke causes approximately 12.2 million new cases and 6.5 million deaths annually, with survivors requiring coordinated care across pre-hospital, acute, rehabilitative, and preventive phases. Mobile health (mHealth) technologies, including smartphone applications, wearable sensors, and tablet-based platforms, have shown clinical potential across these contexts, yet a structured mapping of their distribution across the full stroke care continuum is lacking. We searched PubMed, Scopus, and Web of Science for publications from January 2019 to March 2025. Studies evaluated mHealth interventions in which the mobile platform directly performed diagnostic, therapeutic, or rehabilitative functions in stroke populations. Of 4524 records identified, 17 met the inclusion criteria. Studies originated from eight countries and used heterogeneous designs: five randomized controlled trials, five non-randomized studies, four cohort studies, and three diagnostic accuracy studies. Median sample size was 37 participants (range 10–2249). Evidence concentrated at two poles: six studies addressed acute diagnosis and ten addressed rehabilitation, predominantly in the chronic phase. One study addressed secondary prevention; two targeted early rehabilitation, the period of maximum neuroplasticity after discharge. All seventeen studies covered a single care phase. Smartphone platforms dominated acute contexts; wearable and mixed-modality systems were confined to rehabilitation. The mHealth stroke landscape is fragmented and phase-specific, exhibiting a silo effect in which interventions operate as isolated tools rather than components of an integrated care system. An important gap is the near-absence of research in early rehabilitation. Future priorities include cross-continuum design, expansion into cognitive and secondary prevention domains, and progression toward adequately powered trials.

Background: Efficient and accurate clinical data management is crucial for stroke care and research; however, for complex stroke cohorts, manual data entry is often complicated by high human error rates and significant inefficiency. This study addressed this gap by developing and evaluating an automated system for constructing high-quality stroke cohorts from electronic health records (EHRs). Methods: This retrospective cohort study was conducted at a single specialty hospital, comparing manual data entry (January–June 2022) with an automated system (January–June 2024). The system uses C# and secure SQL protocols for direct EHR integration. We developed an automated system using the C# programming language to extract 133 items covering the full hospitalization period (from admission to discharge) from EHRs, aligning with the Korean Stroke Registry, the Registry of Stroke Care Quality, and national quality assessment programs. The system’s effectiveness was evaluated by assessing the data entry time, data error rate, and medical record completion rate and comparing the automated method’s performance against conventional data entry. Results: The automated system significantly reduced the data entry time from 35 min to 19 s per patient. Furthermore, the data error rate decreased from 2.32% to 0.15% (p < 0.001), and the rate of missing medical records decreased from 28.9% to 16.2% (p < 0.001). Conclusions: The proposed clinical data collection and cohort construction system effectively improved data quality and efficiency compared to the manual method. This system provides a reliable and scalable data infrastructure that could facilitate research on stroke and quality improvement initiatives.

The growing volume of clinical data necessitates efficient management of Electronic Health Records (EHR) data. Traditional labor-intensive EHR management methods have led to significant risks, including high error rates and data omissions. This study modified and developed EHR templates focusing on key cohort-specific variables and developed a C#-based automated system for extracting and managing data from patients with acute stroke. The system reduced data entry time per patient from 35 minutes to 19 seconds. The data error rate decreased from 2.32% to 0.15%. This system enhances data management efficiency and is expected to support the construction of various cohorts through further expansion.