Search Results (195)

Background: Older adults (≥65 years) account for a substantial share of Emergency Medical Service (EMS) activations, yet differences between nursing homes (NHs) and the community are insufficiently defined. This study aimed to compare EMS responses for older adults inside and outside NHs. Methods: We conducted a retrospective observational study of all EMS activations involving patients ≥65 years in a rural German region from July 2020 to March 2025, based on fully anonymized prehospital patient care reports electronically documented on tablets. Prehospital patient care was assessed using the ABCDE approach, with additional analysis of feedback codes transmitted to the control center (response and on-scene times, physician dispatch, lights and sirens use, feedback codes, hospital destination, and admission urgency). Continuous variables were summarized as mean (SD) or median [IQR], categorical variables as n (%), and group comparisons performed using the Chi-Square test or Fisher’s exact test (categorical) and the t test or Mann–Whitney U test (continuous), as appropriate. Results: Among 46,598 EMS activations in adults aged ≥65 years, 28,749 EMS responses were included in the analysis after excluding non-emergency transports and duplicate physician records. Of these, 20% occurred in NHs (5707/28,749) and 80% in the community (23,042/28,749). Median age was 85 years (IQR 80–89) in NH patients and 80 years (IQR 73–86) in community patients (p < 0.001). Females accounted for 60% (3450/5705) of NH patients and 53% (12,223/23,036) of community patients (p < 0.001). Emergency physicians were dispatched in 7% of NH incidents (392/5707) and 10% of community incidents (2327/23,042; p < 0.001). In NHs, bronchitis/pneumonia was a leading feedback code (6%, 354/5707), whereas in community patients, acute coronary syndrome (ACS) was prominent (5%, 1249/23,042). Admission urgency categories also differed significantly, with NH residents classified as category 3 (outpatient evaluation) in 11% (621/5706) and category 0 (no admission) in 5% (280/5706), whereas community patients were more often classified as category 1 (immediate intervention) in 13% (2886/23,037) (all p < 0.001). Conclusions: NH residents were older, more often female, and presented with low-to-moderate acuity. Frequent diagnoses were closed extremity injuries and bronchitis/pneumonia. In contrast, ACS and other cardiovascular emergencies were more common in the community, particularly among men, consistent with international evidence on sex-specific incidence. NH residents were more often classified as non-urgent or outpatient and transported to nearby hospitals, whereas community patients more frequently required immediate intervention and referral to tertiary centers. In summary, EMS responses for older adults differed in clinical presentations, operational patterns, and hospital pathways. Full article

Background: Physiotherapy plays a crucial role in managing women’s health conditions, such as pelvic pain and postpartum recovery. However, its integration into standard care relies heavily on the awareness and referral practices of gynecologists and obstetricians, which may be suboptimal. Objective: To investigate the awareness, attitudes, and referral practices of gynecologists and obstetricians in Libya regarding the role of physiotherapy in women’s health. Methods: A cross-sectional study was conducted in public and private hospitals in Tripoli, Libya. A total of 100 practitioners were invited to participate. A self-administered questionnaire assessed demographics, awareness, attitudes, and practices. Results: The response rate was 67% (n = 67/100). The results revealed a key disparity: while the vast majority of respondents (94.1%) acknowledged the importance of physiotherapy in women’s health and 76.1% reported a willingness to refer patients, only 67.1% perceived physiotherapists as competent to manage these conditions. This indicates a significant gap in understanding specific physiotherapy techniques. Awareness percentage was significantly influenced by factors such as the type of hospital and prior exposure to physiotherapy education. Conclusions: A striking disparity was found between high general awareness (94.1%) and significantly lower perceived competence (67.1%) and knowledge of its role in specific conditions. This gap between general approval and specific understanding directly creates the critical barrier to referral. To improve interdisciplinary collaboration and patient access to care, enhanced educational initiatives for physicians, the development of clear clinical guidelines, and the establishment of dedicated women’s health physiotherapy clinics are strongly recommended. Full article

Background/Objectives: Demand for dermatologic services exceeds specialist capacity, with average wait times of 26–50 days in the United States. Current triage methods rely on subjective judgment or disease-specific indices that do not generalize across diagnoses or translate to operational decisions. We developed and validated the Dermatologic Complexity Score (DCS), a standardized instrument to guide case prioritization across dermatology care settings and evaluate DCS as a workload-reduction filter, enabling safe delegation of approximately half of routine teledermatology cases (DCS ≤ 40) away from specialist review. Methods: We conducted a prospective validation study of the DCS using 100 consecutive teledermatology cases spanning 30 common conditions. The DCS decomposes complexity into five domains (Diagnostic, Treatment, Risk, Patient Complexity, Monitoring) summed to a 0–100 total with prespecified bands: ≤40 (low) (41–70), (moderate) (71–89), (high), ≥90 (extreme). Five board-certified dermatologists and an automated module independently scored all cases. Two primary care physicians completed all ≤40 cases to assess feasibility. Primary outcomes were interrater reliability using ICC (2,1) and agreement with automation. Secondary outcomes included time-to-decision, referral rates, and primary care feasibility. Results: Mean patient age was 46.2 years; 47% of cases scored ≤40, 33% scored 41–70, 18% scored 71–89, and 2% scored ≥90. Interrater reliability was excellent (ICC (1,2)) = 0.979; 95% CI 0.974–0.983), with near-perfect agreement between automated and mean dermatologist scores (r = 0.998). Time-to-decision increased monotonically across DCS bands from 2.11 min (≤40) to 5 (90) min (≥90) (p = 1.36 × 10⁻¹⁴). Referral rates were 0% for ≤40, 3% for 41–70, 27.8% for 71–89, and 100% for ≥90 cases. DCS strongly predicted referral decisions (AUC = 0.919). Primary care physicians successfully managed all ≤40 cases but required 6–8 additional minutes per case compared to dermatologists. Conclusions: The DCS demonstrates excellent reliability and strong construct validity, mapping systematically to clinically relevant outcomes, including decision time and referral patterns. The instrument enables standardized, reproducible triage decisions that can optimize resource allocation across teledermatology, clinic, procedural, and inpatient settings. Implementation could improve access to dermatologic care by supporting appropriate delegation of low-complexity cases to primary care while ensuring timely specialist evaluation for high-complexity conditions. Full article

Background and Objectives: Large language models (LLMs) offer promising applications in healthcare, including drafting referral letters. However, access to LLMs specifically designed for medical practice remains limited. While ChatGPT is widely available, its ability to generate comprehensive and clinically appropriate neurology referral letters remains uncertain. This study aimed to systematically evaluate the completeness and quality of neurology referral letters generated by ChatGPT for standardized clinical scenarios. Materials and Methods: Five standardized clinical scenarios representing common neurological complaints encountered in family medicine settings (headache, memory problems, stroke/TIA, tremor, radiculopathy) were used. Using a consistent prompt, ChatGPT (GPT-4o, 2025 release) generated 10 referral letters per scenario (50 letters in total). A dual board-certified neurologist and family physician scored the letters using a 30-point rubric across multiple domains: completeness (demographics, chief complaint, history of present illness, physical exam findings, management, and consultation questions) and quality (language level, structure, and letter length). Descriptive statistics and inferential analyses (ANOVA and Kruskal–Wallis tests) were applied to assess performance across scenarios. Results: The mean total score was 25.76/30 (95% CI: 24.85–26.67). Completeness averaged 87%, while language and structure consistently scored above 90%. Content gaps appeared in 36 out of 50 letters (72%), mainly in the history of present illness and physical examination sections. Variability was observed across letters, though not statistically significant between scenarios (ANOVA: F = 1.14, p = 0.352; Kruskal–Wallis: H = 3.52, p = 0.475). Conclusions: ChatGPT produced neurology referral letters of high linguistic quality but variable completeness, especially for clinically complex content. The variability pattern among letters reflected model inconsistency rather than case type. The reliance on a single rater and use of a non-validated rubric represent limitations. Future studies should include multiple raters, inter-rater reliability testing, and validated scoring frameworks. Ultimately, access to tailored LLMs exclusively trained for medical documentation could improve outcomes while safeguarding patient privacy. Full article

Background/Objectives: Family physicians are key stakeholders in the implementation of cancer prevention strategies, including risk factor assessment, lifestyle counseling, and early detection. Despite this, integration of personalized prevention into routine practice remains limited. This study aimed to explore family physicians’ perspectives on barriers, training needs, and collaboration opportunities in cancer prevention. Methods: A mixed-methods study was conducted using an exploratory sequential design. The qualitative phase involved semi-structured interviews with 12 family physicians from the North-West Region of Romania. Thematic analysis was employed to identify main challenges and opportunities. Findings informed the development of a structured online survey completed by 50 family physicians. Descriptive and comparative statistical analyses were applied to assess trends and subgroup differences. Results: Interviews and survey data revealed multiple barriers to cancer prevention in primary care: insufficient consultation time, limited access to diagnostic tools, administrative workload, and low patient health literacy. Physicians reported moderate familiarity with personalized prevention but expressed strong interest in further training, particularly through flexible and interactive learning formats. Collaboration with cancer centers was considered suboptimal; participants emphasized the need for streamlined referral pathways and improved communication. Conclusions: The study highlights systemic and educational gaps affecting cancer prevention efforts in family medicine. Tailored training programs, digital integration with cancer centers, and targeted policy adjustments are needed to enhance prevention capacity within primary care. Full article

Background/Objectives: Currently, clinicians and physiotherapists pay only limited attention to spinal deformities in children with cerebral palsy (CP). To enhance awareness, a tool based on a traffic light system (TLS-Spine) was developed for use by pediatricians and physiotherapists caring for children with CP. The objective of the study was to evaluate the applicability of this assessment tool in routine clinical practice. Methods: A review group consisting of 48 pediatricians and physiotherapists was recruited. Each participant was asked to apply the TLS-Spine to a minimum of 10 CP patients and to complete a questionnaire concerning its value and applicability in daily practice. Responses were rated on a scale from 1 (complete agreement) to 6 (complete disagreement). Results: The TLS-Spine was correctly applied in 96.3% cases. The questionnaires of 48 reviewers based on 537 completed survey sheets were analyzed. Overall, reviewers reported no difficulties with the introduction and use of the TLS-Spine in daily clinical routine practice (median/range: 1/1–4). The tool was considered straightforward to use (1/1–3), not time consuming (1/1–4), helpful in performing the clinical examination (2/1–6), understanding the findings (2/1–6) and deciding on further management of the patients (2/1–5). Physicians rated the TLS-Spine significantly higher than physiotherapists in four of nine dimensions. Conclusions: The TLS-Spine is a practical and user-friendly assessment tool. It may increase the awareness for spinal deformities and support early referral to orthopedic specialists for early detection and treatment. Full article

Universal tumor screening (UTS) of all newly diagnosed colorectal cancers (CRCs) for the identification of Lynch syndrome (LS) is recommended. We explored the impact of the COVID-19 pandemic on the UTS process in a safety-net university hospital to identify areas of vulnerability and opportunities for improvement. Patients undergoing resection of a primary CRC were categorized into three cohorts based on surgery date relative to the pandemic (pre-[2018,2019], early-[2020,2021] and late-[2022]). Data regarding (1) UTS performance of immunohistochemistry (IHC) for LS genes and microsatellite instability (MSI) testing; (2) referrals to cancer genetic counseling (CGC) based on mismatch repair deficient (dMMR) status and/or age < 50 years at diagnosis; (3) attendance at CGC; and (4) reasons for not attending CGC were extracted. Between 2018 and 2022, 342 patients underwent resection of a CRC. During the three time periods (pre-, early- and late-pandemic), 93%, 94% and 96% of cases were screened with at least MMR IHC, respectively. Of the patients eligible for referral to CGC in each time period, 60%, 71% and 63% had a referral submitted. Of these, 23%, 36% and 20% in each time period did not attend CGC, with the most common reason for not attending being the inability of schedulers to reach the patient. Although the COVID-19 pandemic did not cause significant variation in the different steps of the UTS process, CGC utilization remained suboptimal throughout the three time periods. Further research on barriers preventing physicians from referring patients to CGC as well as schedulers inability to reach eligible patients should be pursued. Full article

Background/Objectives: Outpatient parenteral antimicrobial therapy (OPAT) has been implemented throughout the world for the treatment of most infections. Published studies have focused on OPAT delivery, with limited data on coordination and monitoring practices. Methods: A mixed methods study, using an exploratory sequential design, was conducted at a tertiary hospital in Paris, France. Ten semi-structured interviews were conducted with prescribing physicians and professionals involved in OPAT coordination and monitoring. A general inductive approach was used to analyze verbatim data and build a framework for OPAT model characterization. Cost estimates, using a standardized scenario, were applied to each model. Results: Five OPAT coordination and monitoring models were identified. All OPATs were administered by visiting nurses in the patient’s home. Referral to an infectious disease physician was not systematic, and three models, with 3 to 50 OPAT episodes/year each, outsourced hospital-to-home coordination and monitoring to external medical service and device providers. Only one OPAT model, with 450 OPATs annually, included a nurse specialist within the unit to coordinate and monitor treatment. Clinically and/or socially vulnerable patients received OPAT through hospital at home services, which reported 30 OPATs/year. Under the standardized clinical scenario applied to each OPAT model, weekly costs ranged from EUR 1445 to EUR 2308. Conclusions: The diversity of OPAT coordination and monitoring practices identified within a single hospital suggests that similar trends may be observed in other settings, in France and elsewhere. Identifying the most cost-effective OPAT service model could guide stakeholders and facilitate the implementation of best practice recommendations in line with antimicrobial stewardship principles. Full article

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder in which early diagnosis improves quality of life and reduces disability. However, diagnostic delays remain common, particularly in low- and middle-income countries. This study investigated clinical and system-level factors contributing to diagnostic delay in Thailand. Methods: A retrospective chart review was conducted on patients newly diagnosed with PD at Thammasat University Hospital between June 2020 and June 2024. Demographic, clinical, and healthcare access data were analyzed. Diagnostic intervals were defined as onset-to-visit (OTV), visit-to-diagnosis (VTD), and onset-to-diagnosis (OTD). Age-at-onset groups included early-onset Parkinson’s disease (EOPD, <50 years), regular-onset PD, and very-late-onset PD (≥80 years). Results: Of 1093 patients screened, 109 newly diagnosed PD cases met the inclusion criteria. The median OTV was 360 days, and the median VTD was 10 days. Tremor was the most frequent initial symptom (75%). Patients with higher education and extended family support sought care earlier, whereas those under the Universal Coverage Scheme (UCS) experienced longer OTD durations (median, 541 vs. 181 days in privately insured patients). More than half of patients were initially misdiagnosed, especially when first evaluated by non-neurologists. Conclusions: Diagnostic delay in Thai PD patients stems mainly from late help-seeking and inequities in healthcare access. Addressing these gaps requires public awareness, physician training, streamlined UCS referral pathways, and adoption of biomarker-supported digital tools to ensure earlier and more equitable diagnosis. Full article

Acquired tracheoesophageal fistulas (TEF) are a rare but severe complication in post-coma neurorehabilitation patients, particularly those requiring long-term tracheostomy and enteral nutrition. Early recognition and proper surgical management are critical to prevent life-threatening outcomes and functional deterioration. However, variability in clinical presentation and the lack of standardized multidisciplinary pathways often delay referral to thoracic surgeons. We present the case of a young patient with severe traumatic brain injury, prolonged tracheostomy, and percutaneous endoscopic gastrostomy (PEG), who developed a TEF due to tracheal ischemic injury. Clinical suspicion arose from indirect signs—such as recurrent aspiration and air in the PEG system—the diagnosis was confirmed by bronchoscopy and sagittal CT imaging. Surgical planning was carried out in close collaboration between rehabilitation physicians and thoracic surgeons, based on shared criteria involving ventilator weaning, nutritional status, and clinical stability. This case highlights the importance of a multidisciplinary, protocol-driven approach in managing TEF. Current literature supports timely but carefully selected surgical intervention, particularly in patients who are no longer ventilator-dependent, significantly reducing perioperative mortality (reported up to 60% in ventilated patients). Recent reviews advocate for standardized surgical techniques—such as single-stage repair with muscle flap interposition—and emphasize the value of early diagnosis using a combination of bronchoscopy, videofluoroscopy, and sagittal CT. We propose a structured clinical pathway integrating neurorehabilitation and thoracic surgery, aimed at optimizing timing and surgical outcomes in patients with acquired TEF. This model may serve as a foundation for future guidelines, improving both safety and efficiency in the multidisciplinary management of this complex complication. Full article

Background: Ambatoboeny District in northern Madagascar faces significant health challenges due to widespread poverty, poor access to healthcare, and limited diagnostic capabilities. Despite high disease burden, data on morbidity patterns in the region are scarce. This study aims to identify the most prevalent diseases and most affected demographic groups, thus providing valuable insight into the region’s health profile. Methods: A retrospective analysis was conducted on medical records from 3678 patients who were admitted at Clinique Médicale BEYZYM, a secondary-level referral facility in Manerinerina, Boeny Region between January and December 2024. Diagnoses were retrieved from physician registration ledgers, hospitalization records, monthly laboratory reports, monthly general hospital activity reports and monthly reports from Centre de Traitement et de Diagnostic de la Tuberculose, which were cross-referenced and verified by trained clinical staff. Records were included if they contained identifiable demographic data and at least one clinical diagnosis. Diagnoses were coded using ICD-11 and were classified into 15 major categories. Results: The median patient age was 19.5 years (IQR: 7–42), with females accounting for 54% of the cohort. Most patients (87.2%) resided in Ambatoboeny. The most common reasons for admission were infectious and parasitic diseases (35.75%, 95% CI: 34.20–37.30), respiratory diseases (22.73%, 95% CI: 21.38–24.08), and diseases of the genitourinary system (13.95%, 95% CI: 12.83–15.07), collectively accounting for 72.43% of all recorded cases. Statistically significant differences in morbidity patterns were observed across age and sex groups. Conclusions: The findings underscore the multifaceted burden of disease in the Ambatoboeny District, where both infectious and chronic conditions coexist in a resource-limited setting. Delayed healthcare-seeking behavior, cultural beliefs, and diagnostic limitations further complicate care delivery. This study provides foundational data to inform targeted health policies, humanitarian medical missions, and diagnostic capacity-building tailored to local needs. Full article

Uterine fibroids are one of the leading health concerns for women worldwide, affecting up to 80% of women by the age of 50. While recent advancements have improved the diagnosis and treatment of fibroids, the current standard of care still faces important limitations due to the need for a personalized approach to treatment. Uterine fibroid embolization (UFE) has emerged as a promising minimally invasive alternative to traditional surgery, offering advantages such as shorter recovery times, fewer complications, and the preservation of the uterus. However, despite their highly reported effectiveness, only about 1% of eligible patients are offered UFE. This drastic underutilization is partially due to limited physician confidence in predicting patient-specific outcomes. To address this challenge, in this study, we aim to present an objective analysis of the factors influencing UFE success and introduce a scalable and interpretable machine learning (ML) system designed to support clinical decision-making. We have curated a dataset that includes 74 patients, with a total of 311 fibroids for our analysis. We have also developed two sets of ML models for predicting UFE procedure success based on a pre-operative MRI scan as the input. The first model predicts overall procedure success and the likelihood of relieving specific symptoms, achieving an accuracy of 75% (AUC = 0.74) for procedure outcome and 81–88% (AUC = 0.81–0.87) for different symptoms, respectively. The second set of models predicts the success of each individual fibroid responding to the treatment, achieving a 76% accuracy and 75% F-1 score. The AI models in this study can potentially provide patient-specific prediction of procedure effectiveness on both patient-level and fibroid-level, enhancing procedure referral accuracy. Full article

Background/Objective: Dry eye syndrome affects 16 million Americans with USD 52 billion in annual healthcare costs. With large language models (LLMs) increasingly used for healthcare information, understanding their performance in delivering equitable dry eye guidance across diverse populations is critical. This study aims to evaluate and compare five major LLMs (Grok, ChatGPT, Gemini, Claude.ai, and Meta AI) regarding dry eye syndrome information delivery across different demographic groups. Methods: LLMs were queried using standardized prompts simulating a 62-year-old patient with dry eye symptoms across four demographic categories (White, Black, East Asian, and Hispanic males and females). Responses were analyzed for word count, readability, cultural sensitivity scores (0–3 scale), keyword coverage, and response times. Results: Significant variations existed across LLMs. Word counts ranged from 32 to 346 words, with Gemini being the most comprehensive (653.8 ± 96.2 words) and Claude.ai being the most concise (207.6 ± 10.8 words). Cultural sensitivity scores revealed Grok demonstrated highest awareness for minority populations (scoring 3 for Black and Hispanic demographics), while Meta AI showed minimal cultural tailoring (0.5 ± 0.5). All models recommended specialist consultation, but medical term coverage varied significantly. Response times ranged from 7.41 s (Meta AI) to 25.32 s (Gemini). Conclusions: While all LLMs provided appropriate referral recommendations, substantial disparities exist in cultural sensitivity, content depth, and information delivery across demographic groups. No LLM consistently addressed the full spectrum of dry eye causes across all demographics. These findings underscore the importance for physician oversight and standardization in AI-generated healthcare information to ensure equitable access and prevent care delays. Full article

Background/Objectives: The demand for accessible and reliable digital health services has increased significantly in recent years, particularly in regions facing physician shortages. HomeDOCtor, a conversational AI platform developed in Slovenia, addresses this need with a nationally adapted architecture that combines retrieval-augmented generation (RAG) and a Redis-based vector database of curated medical guidelines. The objective of this study was to assess the performance and impact of HomeDOCtor in providing AI-powered healthcare assistance. Methods: HomeDOCtor is designed for human-centered communication and clinical relevance, supporting multilingual and multimedia citizen inputs while being available 24/7. It was tested using a set of 100 international clinical vignettes and 150 internal medicine exam questions from the University of Ljubljana to validate its clinical performance. Results: During its six-month nationwide deployment, HomeDOCtor received overwhelmingly positive user feedback with minimal criticism, and exceeded initial expectations, especially in light of widespread media narratives warning about the risks of AI. HomeDOCtor autonomously delivered localized, evidence-based guidance, including self-care instructions and referral suggestions, with average response times under three seconds. On international benchmarks, the system achieved ≥95% Top-1 diagnostic accuracy, comparable to leading medical AI platforms, and significantly outperformed stand-alone ChatGPT-4o in the national context (90.7% vs. 80.7%, p = 0.0135). Conclusions: Practically, HomeDOCtor eases the burden on healthcare professionals by providing citizens with 24/7 autonomous, personalized triage and self-care guidance for less complex medical issues, ensuring that these cases are self-managed efficiently. The system also identifies more serious cases that might otherwise be neglected, directing them to professionals for appropriate care. Theoretically, HomeDOCtor demonstrates that domain-specific, nationally adapted large language models can outperform general-purpose models. Methodologically, it offers a framework for integrating GDPR-compliant AI solutions in healthcare. These findings emphasize the value of localization in conversational AI and telemedicine solutions across diverse national contexts. Full article

Background/objectives: Racial and ethnic minority patients are underrepresented in cancer clinical trials (CCTs) and multilevel strategies are required to increase participation. This study describes barriers and facilitators to minority CCT participation alongside feedback on a multilevel intervention (MLI) designed to reduce participation barriers, as posited by the socioecological model (SEM). Methods: Interviews with Moffitt Cancer Center (MCC) physicians, community physicians, patients with cancer, community residents, and clinical research coordinators (CRCs) were conducted from June 2023–February 2024. Verbal responses were analyzed using thematic analysis and categorized into SEM levels. Mean helpfulness scores rating interventions (from 1 (not helpful) to 5 (very helpful)) were summarized. Results: Approximately 50 interviews were completed. Thematic findings confirmed CCT referral and enrollment barriers across all SEM levels. At the community level, MCC patients and community residents felt that community health educators can improve patient experiences and suggested they connect patients to social/financial resources, assist with patient registration, and provide CCT education. While physicians and CRCs reacted positively to all institutional-level tools, the highest scored tool simultaneously addressed CCT referral and enrollment at the institution (e.g., trial identification/referrals) and interpersonal level (communication platform for community and MCC physicians) (mean = 4.27). At the intrapersonal level, patients were enthusiastic about a digital CCT decision aid (mean = 4.53) and suggested its integration into MCC’s patient portal. Conclusions: Results underscore the value of conducting formative research to tailor interventions to target population needs. Our approach can be leveraged by future researchers seeking to evaluate MLIs addressing additional CCT challenges or broader health topics. Full article