Optimizing outcomes: A retrospective analysis of discharge safety for elderly mTBI patients on anticoagulation therapy.

BACKGROUND: Mild traumatic brain injuries (mTBIs) pose a significant risk, particularly in the elderly population on anticoagulation therapy. The safety of discharging these patients from the emergency department (ED) with a negative initial computed tomography (CT) scan has been debated due to the risk of delayed intracranial hemorrhage (d-ICH). OBJECTIVE: To compare outcomes, including d-ICH, between elderly patients on anticoagulation therapy presenting with mTBI who were admitted versus discharged from the ED after an initial negative head CT scan. METHODS: We conducted a retrospective observational study at the Chaim Sheba Medical Center, assessing outcomes of 1598 elderly patients on anticoagulation therapy who presented with mTBI and an initial negative head CT scan. Patients were either admitted for 24-h observation (Group A, n = 829) or discharged immediately from the ED (Group B, n = 769). The primary outcome was incidence of d-ICH within 14 days. RESULTS: Among the 1598 patients included in the study, 46 admitted patients and 1 discharged patient returned within 14 days for repeat CT, identifying one asymptomatic hemorrhage in the discharged patient. Mortality at 30 days was significantly higher in admitted patients compared to discharged patients (4.8% vs. 1.8%, p = 0.001), though cause of death was unrelated to head injury in both groups. CONCLUSION: In elderly patients on anticoagulation with mTBI and a negative initial CT, admission was associated with a higher risk of d-ICH compared to discharge. These findings have implications for clinical decision-making in this high-risk population.

Augmenting intensive care unit nursing practice with generative AI: A formative study of diagnostic synergies using simulation-based clinical cases.

BACKGROUND: As generative artificial intelligence (GenAI) tools continue advancing, rigorous evaluations are needed to understand their capabilities relative to experienced clinicians and nurses. The aim of this study was to objectively compare the diagnostic accuracy and response formats of ICU nurses versus various GenAI models, with a qualitative interpretation of the quantitative results. METHODS: This formative study utilized four written clinical scenarios representative of real ICU patient cases to simulate diagnostic challenges. The scenarios were developed by expert nurses and underwent validation against current literature. Seventy-four ICU nurses participated in a simulation-based assessment involving four written clinical scenarios. Simultaneously, we asked ChatGPT-4 and Claude-2.0 to provide initial assessments and treatment recommendations for the same scenarios. The responses from ChatGPT-4 and Claude-2.0 were then scored by certified ICU nurses for accuracy, completeness and response. RESULTS: Nurses consistently achieved higher diagnostic accuracy than AI across open-ended scenarios, though certain models matched or exceeded human performance on standardized cases. Reaction times also diverged substantially. Qualitative response format differences emerged such as concision versus verbosity. Variations in GenAI models system performance across cases highlighted generalizability challenges. CONCLUSIONS: While GenAI demonstrated valuable skills, experienced nurses outperformed in open-ended domains requiring holistic judgement. Continued development to strengthen generalized decision-making abilities is warranted before autonomous clinical integration. Response format interfaces should consider leveraging distinct strengths. Rigorous mixed methods research involving diverse stakeholders can help iteratively inform safe, beneficial human-GenAI partnerships centred on experience-guided care augmentation. RELEVANCE TO CLINICAL PRACTICE: This mixed-methods simulation study provides formative insights into optimizing collaborative models of GenAI and nursing knowledge to support patient assessment and decision-making in intensive care. The findings can help guide development of explainable GenAI decision support tailored for critical care environments. PATIENT OR PUBLIC CONTRIBUTION: Patients or public were not involved in the design and implementation of the study or the analysis and interpretation of the data.

Treating wartime injuries amidst attack: insights from a medical facility on the edge of combat.

BACKGROUND: Providing emergency care during conflict poses unique challenges for frontline hospitals. Barzilai Medical Center (BUMCA) in Ashkelon, Israel is a Level I trauma center located close to the Gaza border. During the November 2023 escalation of conflict, BUMCA experienced surging numbers of civilian and military trauma patients while also coming under rocket fire. METHODS: We conducted a retrospective review of BUMCA operational records and 827 de-identified patient records from October 7-14, 2023. Records provided data on daily patient volumes, injury patterns, resource constraints, and impacts of rocket attacks on hospital function. Basic demographic data was obtained including age, gender, injury severity scores, and disposition. RESULTS: Of the 827 patients brought to BUMCA, most (n = 812, 98.2%) presented through the emergency department. Tragically, 99 individuals were pronounced dead on arrival. Injury severity assessments found nearly half (47%) had minor injuries such as lacerations, contusions and sprains, while 25% exhibited moderate injuries like deep lacerations and fractures. 15% sustained severe or critical injuries including severe head injuries. The largest age group consisted of adults aged 19-60 years. No pediatric patients were admitted despite proximity to residential neighborhoods. The majority of cases (61%) involved complex polytrauma affecting multiple body regions. BUMCA served as both the primary treatment facility and a triage hub, coordinating secondary transports to other trauma centers as needed. Patient volumes fluctuated unpredictably from 30 to an overwhelming 125 daily, straining emergency services. Resources faced shortages of beds, medical staff, supplies and disruptions to power from nearby missile impacts further challenging care delivery. CONCLUSION: Despite facing surging demand, unpredictable conditions and external threats, BUMCA demonstrated resilience in maintaining emergency trauma services through an adaptive triage approach and rapid surges in capacity. Their experience provides insights for improving frontline hospital preparedness and continuity of care during conflict through advance contingency planning and surge protocols. Analysis of patient outcomes found a mortality rate of 15% given the complex, multi-region injuries sustained by many patients. This study highlights the challenges faced and strengths exhibited by medical professionals operating under hazardous conditions in minimizing loss of life. PATIENT AND PUBLIC INVOLVEMENT IN RESEARCH: Given that the study analyzed patient data from a hospital treating casualties of an ongoing armed conflict, directly engaging patients or the public during the sensitive research process could have posed risks. The volatile security situation and restrictions and protections in place amidst the crisis made it not feasible or appropriate to involve them in the study's design, methods, reporting of results, or dissemination plans. Our aim was to conduct this retrospective analysis in a way that did not endanger those affected or compromise the hospital's emergency response operations.

Evaluating the Accuracy and Impact of the ESR-iGuide Decision Support Tool in Optimizing CT Imaging Referral Appropriateness.

Radiology referral quality impacts patient care, yet factors influencing quality are poorly understood. This study assessed the quality of computed tomography (CT) referrals, identified associated characteristics, and evaluated the ESR-iGuide clinical decision support tool's ability to optimize referrals. A retrospective review analyzed 300 consecutive CT referrals from an acute care hospital. Referral quality was evaluated on a 5-point scale by three expert reviewers (inter-rater reliability κ = 0.763-0.97). The ESR-iGuide tool provided appropriateness scores and estimated radiation exposure levels for the actual referred exams and recommended exams. Scores were compared between actual and recommended exams. Associations between ESR-iGuide scores and referral characteristics, including the specialty of the ordering physician (surgical vs. non-surgical), were explored. Of the referrals, 67.1% were rated as appropriate. The most common exams were head and abdomen/pelvis CTs. The ESR-iGuide deemed 70% of the actual referrals "usually appropriate" and found that the recommended exams had lower estimated radiation exposure compared to the actual exams. Logistic regression analysis showed that non-surgical physicians were more likely to order inappropriate exams compared to surgical physicians. Over one-third of the referrals showed suboptimal quality in the unstructured system. The ESR-iGuide clinical decision support tool identified opportunities to optimize appropriateness and reduce radiation exposure. Implementation of such a tool warrants consideration to improve communication and maximize patient care quality.

The Patient's Perspective: Does It Align with Dialysis Adequacy?

BACKGROUND: The concept of patient-centered care puts the individual's health needs and desired health outcomes as the driving forces behind medical decision-making and quality assessment in the healthcare system. Patients with end-stage kidney disease (ESKD) treated by hemodialysis require frequent encounters with the dialysis facility in order to survive. Therefore, their satisfaction with care and perceived patient experience are important aspects that might impact their adherence to the care regimen. The aim of this study was to evaluate patient satisfaction and its association with perceived patient experience and objective clinical quality parameters, across three hemodialysis clinics. METHODS: A prospective cohort study analyzed the data of 126 patients with ESKD receiving chronic hemodialysis over 9 months in 3 different care facilities. Sociodemographic characteristics, medical history, treatment details, and dialysis adequacy (measures as STDKt/V) were collected. Perceived quality of care, patient satisfaction and clinical outcomes were assessed. RESULTS: Patients differed significantly between sites by age, diabetes status and biochemical parameters. Satisfaction scores varied significantly for 12/14 survey questions and at the site-level, with Site 2 scoring highest. Overall satisfaction did not correlate with Kt/V. At Site 1, a moderate negative correlation was found between satisfaction and Kt/V. Kt/V correlated positively with age but inversely with satisfaction. Hospitalization rates were similar regardless of satisfaction. Mortality trended lower in the highest Kt/V quartile. CONCLUSIONS: Achieving clinical quality while optimizing patient satisfaction requires multifactorial approaches tailored to the unique population of the hemodialysis facility. Further research is needed to fully understand factors influencing satisfaction and perceived quality.

Conflict-Related Breakdowns in Routine Medical Care - Longitudinal Outcomes for Civilians with Pre-Existing Cognitive Impairment.

Individuals with dementia face increased vulnerability during crises like armed conflicts. However, little is known about how conflicts affect dementia care delivery and patients' health. We conducted a longitudinal cohort study using medical record data. The study included 23,733 adults aged≥65 years with a diagnosis of dementia and 249,749 matched adults without dementia. Data were collected at baseline (March-October 2023), and two follow-up timepoints (December 2023 and February 2024), bracketing an armed conflict between Israel and Palestinian militant groups that began on October 7, 2023. We compared changes over time in clinical characteristics, medication use, healthcare utilization, costs between groups. Dementia prevalence was stable, but psychotropic medication use declined more sharply in those with dementia. Rates of depression diagnoses fell, and obesity rose in both groups. Healthcare utilization decreased substantially post-conflict, with fewer outpatient visits, hospitalizations, and emergency visits. Cost divergence between groups also increased over time. Machine learning identified shifting clusters of service users from high to mainly low users' post-conflict. The conflict severely disrupted routine dementia care and altered health behaviors. Flexible service delivery and access promotion strategies are needed to support vulnerable populations like people with dementia during crises.

Harnessing innovation to help meet the needs of elders: field testing an electronic tool to streamline geriatric assessments across healthcare settings.

BACKGROUND: As populations age globally, effectively managing geriatric health poses challenges for primary care. Comprehensive geriatric assessments (CGAs) aim to address these challenges through multidisciplinary screening and coordinated care planning. However, most CGA tools and workflows have not been optimised for routine primary care delivery. OBJECTIVE: This study aimed to evaluate the impact of a computerised CGA tool, called the Golden Age Visit, implemented in primary care in Israel. METHODS: This study employed a quasiexperimental mixed-methods design to evaluate outcomes associated with the Golden Age electronic health assessment tool. Quantitative analysis used electronic medical records data from Maccabi Healthcare Services, the second largest health management organisation (HMO) in Israel. Patients aged 75 and older were included in analyses from January 2017 to December 2019 and January 2021 to December 2022. For patients, data were also collected on controls who did not participate in the Golden Age Visit programme during the same time period, to allow for comparison of outcomes. For physicians, qualitative data were collected via surveys and interviews with primary care physicians who used the Golden Age Visit SMARTEST e-assessment tool. RESULTS: A total of 9022 community-dwelling adults aged 75 and older were included in the study: 1421 patients received a Golden Age Visit CGA (intervention group), and 7601 patients did not receive the assessment (control group). After CGAs, diagnosis rates increased significantly for neuropsychiatric conditions and falls. Referrals to physiotherapy, occupational therapy, dietetics and geriatric outpatient clinics also rose substantially. However, no differences were found in rates of hip fracture or relocation to long-term care between groups. Surveys among physicians (n=151) found high satisfaction with the programme. CONCLUSION: Implementation of a large-scale primary care CGA programme was associated with improved diagnosis and management of geriatric conditions. Physicians were also satisfied, suggesting good uptake and feasibility within usual care. Further high-quality studies are still needed but these results provide real-world support for proactively addressing geriatric health needs through structured screening models.

Modeling mortality prediction in older adults with dementia receiving COVID-19 vaccination.

OBJECTIVE: This study compared COVID-19 outcomes between vaccinated and unvaccinated older adults with and without cognitive impairment. METHOD: Electronic health records from Israel from March 2020-February 2022 were analyzed for a large cohort (N = 85,288) aged 65 + . Machine learning constructed models to predict mortality risk from patient factors. Outcomes examined were COVID-19 mortality and hospitalization post-vaccination. RESULTS: Our study highlights the significant reduction in mortality risk among older adults with cognitive disorders following COVID-19 vaccination, showcasing a survival rate improvement to 93%. Utilizing machine learning for mortality prediction, we found the XGBoost model, enhanced with inverse probability of treatment weighting, to be the most effective, achieving an AUC-PR value of 0.89. This underscores the importance of predictive analytics in identifying high-risk individuals, emphasizing the critical role of vaccination in mitigating mortality and supporting targeted healthcare interventions. CONCLUSIONS: COVID-19 vaccination strongly reduced poor outcomes in older adults with cognitive impairment. Predictive analytics can help identify highest-risk cases requiring targeted interventions.

An evaluation of the capabilities of language models and nurses in providing neonatal clinical decision support.

AIM: To assess the clinical reasoning capabilities of two large language models, ChatGPT-4 and Claude-2.0, compared to those of neonatal nurses during neonatal care scenarios. DESIGN: A cross-sectional study with a comparative evaluation using a survey instrument that included six neonatal intensive care unit clinical scenarios. PARTICIPANTS: 32 neonatal intensive care nurses with 5-10 years of experience working in the neonatal intensive care units of three medical centers. METHODS: Participants responded to 6 written clinical scenarios. Simultaneously, we asked ChatGPT-4 and Claude-2.0 to provide initial assessments and treatment recommendations for the same scenarios. The responses from ChatGPT-4 and Claude-2.0 were then scored by certified neonatal nurse practitioners for accuracy, completeness, and response time. RESULTS: Both models demonstrated capabilities in clinical reasoning for neonatal care, with Claude-2.0 significantly outperforming ChatGPT-4 in clinical accuracy and speed. However, limitations were identified across the cases in diagnostic precision, treatment specificity, and response lag. CONCLUSIONS: While showing promise, current limitations reinforce the need for deep refinement before ChatGPT-4 and Claude-2.0 can be considered for integration into clinical practice. Additional validation of these tools is important to safely leverage this Artificial Intelligence technology for enhancing clinical decision-making. IMPACT: The study provides an understanding of the reasoning accuracy of new Artificial Intelligence models in neonatal clinical care. The current accuracy gaps of ChatGPT-4 and Claude-2.0 need to be addressed prior to clinical usage.

Evaluating emergency response at a hospital near the Gaza border within 24 h of increased conflict.

BACKGROUND: Frontline hospitals near active hostilities face unique challenges in delivering emergency care amid threats to infrastructure and personnel safety. Existing literature focuses on individual aspects like mass casualty protocols or medical neutrality, with limited analysis of operating acute services directly under fire. OBJECTIVES: To describe the experience of a hospital situated meters from hostilities and analyze strategies implemented for triage, expanding surge capacity, and maintaining continuity of care during attacks with limited medical staff availability due to hazardous conditions. A focus will be placed on assessing how the hospital functioned and adapted care delivery models in the event of staffing limitations preventing all teams from arriving on site. METHODS: A retrospective case study was conducted of patient records from Barzilai University Medical Center at Ashkelon (BUMCA) Medical Center in Israel within the first 24 h after escalated conflict began on October 7, 2023. Data on 232 admissions were analyzed regarding demographics, treatment protocols, time to disposition, and mortality. Missile alert data correlated patient surges to attacks. Statistical and geospatial analyses were performed. RESULTS: Patients predominantly male soldiers exhibited blast/multisystem trauma. Patient surges at the hospital were found to be correlated with the detection of incoming missile attacks from Gaza within 60 min of launch. While 131 (56%) patients were discharged and 55 (24%) transferred within 24 h, probabilities of survival declined over time reflecting injury severity limitations. 31 deaths occurred from severe presentation. CONCLUSION: Insights gleaned provide a compelling case study on managing mass casualties at the true frontlines. By disseminating BUMCA's trauma response experience, strategies can strengthen frontline hospital protocols optimizing emergency care delivery during hazardous armed conflicts through dynamic surge capacity expansion, early intervention prioritization, and infrastructure/personnel protection measures informed by risks.

Exploring the Self-Reported Physical and Psychological Effects in a Population Exposed to a Regional Conflict.

BACKGROUND: Conflict profoundly impacts community health and well-being. While post-conflict research exists, little is known about initial effects during active hostilities. OBJECTIVE: To assess self-reported changes in health behaviors, distress, and care access within one month of regional warfare onset in a conflict-affected community. METHODS: An online survey was conducted in November 2023 among 501 residents (mean age 40.5 years) of a community where war began October 7th. Measures evaluated physical health, mental health, diet, substance use, sleep, weight changes, and healthcare access before and after the declaration of war. RESULTS: Relative to pre-war, respondents reported significantly increased rates of tobacco (56%) and alcohol (15%) consumption, worsening sleep quality (63%), elevated distress (18% sought help; 14% needed but didn't receive it), and postponed medical care (36%). Over a third reported weight changes. Distress was higher among females and those endorsing maladaptive coping. CONCLUSION: Within one month, substantial impacts on community psychosocial and behavioral health emerged. Unmet mental health needs and risk-taking behaviors were early indicators of conflict's health consequences. Continuous monitoring of conflict-affected communities is needed to inform tailored interventions promoting resilience and prevent entrenchment of harms over time.

A comparative vignette study: Evaluating the potential role of a generative AI model in enhancing clinical decision-making in nursing.

AIM: This study explores the potential of a generative artificial intelligence tool (ChatGPT) as clinical support for nurses. Specifically, we aim to assess whether ChatGPT can demonstrate clinical decision-making equivalent to that of expert nurses and novice nursing students. This will be evaluated by comparing ChatGPT responses to clinical scenarios to those of nurses on different levels of experience. DESIGN: This is a cross-sectional study. METHODS: Emergency room registered nurses (i.e. experts; n = 30) and nursing students (i.e. novices; n = 38) were recruited during March-April 2023. Clinical decision-making was measured using three validated clinical scenarios involving an initial assessment and reevaluation. Clinical decision-making aspects assessed were the accuracy of initial assessments, the appropriateness of recommended tests and resource use and the capacity to reevaluate decisions. Performance was also compared by timing response generations and word counts. Expert nurses and novice students completed online questionnaires (via Qualtrics), while ChatGPT responses were obtained from OpenAI. RESULTS: Concerning aspects of clinical decision-making and compared to novices and experts: (1) ChatGPT exhibited indecisiveness in initial assessments; (2) ChatGPT tended to suggest unnecessary diagnostic tests; (3) When new information required re-evaluation, ChatGPT responses demonstrated inaccurate understanding and inappropriate modifications. In terms of performance, the mean number of words utilized in ChatGPT answers was 27-41 times greater than that utilized by both experts and novices; and responses were provided approximately 4 times faster than those of novices and twice faster than expert nurses. ChatGPT responses maintained logical structure and clarity. CONCLUSIONS: A generative AI tool demonstrated indecisiveness and a tendency towards over-triage compared to human clinicians. IMPACT: The study shows that it is important to approach the implementation of ChatGPT as a nurse's digital assistant with caution. More study is needed to optimize the model's training and algorithms to provide accurate healthcare support that aids clinical decision-making. REPORTING METHOD: This study adhered to relevant EQUATOR guidelines for reporting observational studies. PATIENT OR PUBLIC CONTRIBUTION: Patients were not directly involved in the conduct of this study.

Optimising wound monitoring: Can digital tools improve healing outcomes and clinic efficiency.

BACKGROUND: Chronic wounds present significant challenges for patients and nursing care teams worldwide. Digital health tools offer potential for more standardised and efficient nursing care pathways but require further rigorous evaluation. OBJECTIVE: This retrospective matched cohort study aimed to compare the impacts of a digital tracking application for wound documentation versus traditional manual nursing assessments. METHODS: Data from 5236 patients with various wound types were analysed. Propensity score matching balanced groups, and bivariate tests, correlation analyses, linear regression, and Hayes' Process Macro Model 15 were utilised for a mediation-moderation model. RESULTS: Digital wound tracking was associated with significantly shorter healing durations (15 vs. 35 days) and fewer clinic nursing visits (3 vs. 5.8 visits) compared to standard nursing monitoring. Digital tracking demonstrated improved wound size reduction over time. Laboratory values tested did not consistently predict healing outcomes. Digital tracking exhibited moderate negative correlations with the total number of nursing visits. Regression analysis identified wound complexity, hospitalizations, and initial wound size as clinical predictors for more nursing visits in patients with diabetes mellitus (p < .01). Digital tracking significantly reduced the number of associated nursing visits for patients with peripheral vascular disease. CONCLUSION: These findings suggest that digital wound management may streamline nursing care and provide advantages, particularly for comorbid populations facing treatment burdens. REPORTING METHOD: This study adhered to STROBE guidelines in reporting this observational research. RELEVANCE TO CLINICAL PRACTICE: By streamlining documentation and potentially shortening healing times, digital wound tracking could help optimise nursing resources, enhance wound care standards, and improve patient experiences. This supports further exploration of digital health innovations to advance evidence-based nursing practice. PATIENT OR PUBLIC CONTRIBUTION: This study involved retrospective analysis of existing patient records and did not directly include patients or the public in the design, conduct, or reporting of the research.

Identifying Diabetes Related-Complications in a Real-World Free-Text Electronic Medical Records in Hebrew Using Natural Language Processing Techniques.

BACKGROUND: Studies have demonstrated that 50% to 80% of patients do not receive an International Classification of Diseases (ICD) code assigned to their medical encounter or condition. For these patients, their clinical information is mostly recorded as unstructured free-text narrative data in the medical record without standardized coding or extraction of structured data elements. Leumit Health Services (LHS) in collaboration with the Israeli Ministry of Health (MoH) conducted this study using electronic medical records (EMRs) to systematically extract meaningful clinical information about people with diabetes from the unstructured free-text notes. OBJECTIVES: To develop and validate natural language processing (NLP) algorithms to identify diabetes-related complications in the free-text medical records of patients who have LHS membership. METHODS: The study data included 2.3 million records of 41 469 patients with diabetes aged 35 or older between the years 2012 and 2017. The diabetes related complications included cardiovascular disease, diabetic neuropathy, nephropathy, retinopathy, diabetic foot, cognitive impairments, mood disorders and hypoglycemia. A vocabulary list of terms was determined and adjudicated by two physicians who are experienced in diabetes care board certified diabetes specialist in endocrinology or family medicine. Two independent registered nurses with PhDs reviewed the free-text medical records. Both rule-based and machine learning techniques were used for the NLP algorithm development. Precision, recall, and F-score were calculated to compare the performance of (1) the NLP algorithm with the reviewers' comments and (2) the ICD codes with the reviewers' comments for each complication. RESULTS: The NLP algorithm versus the reviewers (gold standard) achieved an overall good performance with a mean F-score of 86%. This was better than the ICD codes which achieved a mean F-score of only 51%. CONCLUSION: NLP algorithms and machine learning processes may enable more accurate identification of diabetes complications in EMR data.

Priority setting for health technology adoption at the national level: Lessons learned over 25 years' experience.

BACKGROUND: Limited health budgets and continual advancement of health technologies require mechanisms for prioritization. Israel, with a publicly funded health service basket, has implemented and optimized such a health technology assessment process since 1999.We describe the process of evaluating technologies according to the Israeli model, analyze its outputs and benefits over two decades of implementation, and compare its key features with international experience. METHODS: Retrospective data were collected between 1998 and 2023, including work processes, committee composition, number of applications submitted and approved by a clinical domain, and yearly cost of the basket. Features were evaluated within the evidence-informed deliberative process (EDP) framework. RESULTS: This national model involves relevant stake holders in a participatory and transparent process, in a timely manner, and is accepted by the public, health professionals, and policy makers, facilitating early adoption of the newest medical technologies. Between 11 and 19 percent of applications are approved for reimbursement annually, mostly pharmaceuticals. On average 26 percent of approved technologies are added to the list without additional budget. Major domains of approved technologies were oncology, cardiology, and neurology. CONCLUSIONS: Israel created a unique model for the expansion of the health service basket. Despite an increasing number of applications and rising costs, the mechanism enables a consensus to be reached on which technologies to fund, while remaining within budget constraints and facilitating immediate implementation. The process, which prioritizes transparency and stake holder involvement, allows just a resource allocation while maximizing the adoption of novel technologies, contributing to an outstanding national level of health despite relatively low health spending.

Association between COVID-19 vaccination and critical outcomes among older adults with dementia: a comparative cohort study.

Background: As COVID-19 vaccines became available, understanding their potential benefits in vulnerable populations has gained significance. This study explored the advantages of COVID-19 vaccination in individuals with cognitive disorders by analyzing health-related variables and outcomes. Methods: A prospective cohort study analyzed electronic medical records of 25,733 older adults with cognitive disorders and 65,544 older adults without cognitive disorders from March 2020 to February 2022. COVID-19 vaccination status was the primary exposure variable, categorized as fully vaccinated or unvaccinated. The primary outcomes measured were all-cause mortality and hospitalization rates within 14 and 400 days post-vaccination. Data on vaccination status, demographics, comorbidities, testing history, and clinical outcomes were collected from electronic health records. The study was ethically approved by the relevant medical facility's Institutional Review Board (0075-22-MHS). Results: Vaccinated individuals had significantly lower mortality rates in both groups. In the research group, the mortality rate was 52% (n = 1852) for unvaccinated individuals and 7% (n = 1,241) for vaccinated individuals (p < 0.001). Similarly, in the control group, the mortality rate was 13.58% (n = 1,508) for unvaccinated individuals and 1.85% (n = 936) for vaccinated individuals (p < 0.001), despite higher COVID-19 positivity rates. In the research group, 30.26% (n = 1,072) of unvaccinated individuals tested positive for COVID-19, compared to 37.16% (n = 6,492) of vaccinated individuals (p < 0.001). In the control group, 17.31% (n = 1922) of unvaccinated individuals were COVID-19 positive, while 37.25% (n = 18,873) of vaccinated individuals tested positive (p < 0.001). Vaccination also showed potential benefits in mental health support. The usage of antipsychotic drugs was lower in vaccinated individuals (28.43%, n = 4,967) compared to unvaccinated individuals (37.48%, n = 1,328; 95% CI [0.92-1.28], p < 0.001). Moreover, vaccinated individuals had lower antipsychotic drug prescription rates (23.88%, n = 4,171) compared to unvaccinated individuals (27.83%, n = 968; 95% CI [-1.02 to -0.63], p < 0.001). Vaccination appeared to have a positive impact on managing conditions like diabetes, with 38.63% (n = 6,748) of vaccinated individuals having diabetes compared to 41.55% (n = 1,472) of unvaccinated individuals (95% CI [0.24, 0.48], p < 0.001). Discussion: The findings highlight the importance of vaccination in safeguarding vulnerable populations during the pandemic and call for further research to optimize healthcare strategies for individuals with cognitive disorders.

Evaluating the reliability of ChatGPT as a tool for imaging test referral: a comparative study with a clinical decision support system.

OBJECTIVES: As the technology continues to evolve and advance, we can expect to see artificial intelligence (AI) being used in increasingly sophisticated ways to make a diagnosis and decisions such as suggesting the most appropriate imaging referrals. We aim to explore whether Chat Generative Pretrained Transformer (ChatGPT) can provide accurate imaging referrals for clinical use that are at least as good as the ESR iGuide. METHODS: A comparative study was conducted in a tertiary hospital. Data was collected from 97 consecutive cases that were admitted to the emergency department with abdominal complaints. We compared the imaging test referral recommendations suggested by the ESR iGuide and the ChatGPT and analyzed cases of disagreement. In addition, we selected cases where ChatGPT recommended a chest abdominal pelvis (CAP) CT (n = 66), and asked four specialists to grade the appropriateness of the referral. RESULTS: ChatGPT recommendations were consistent with the recommendations provided by the ESR iGuide. No statistical differences were found between the appropriateness of referrals by age or gender. Using a sub-analysis of CAP cases, a high agreement between ChatGPT and the specialists was found. Cases of disagreement (12.4%) were further analyzed and presented themes of vague recommendations such as "it would be advisable" and "this would help to rule out." CONCLUSIONS: ChatGPT's ability to guide the selection of appropriate tests may be comparable to some degree with the ESR iGuide. Features such as the clinical, ethical, and regulatory implications are still warranted and need to be addressed prior to clinical implementation. Further studies are needed to confirm these findings. CLINICAL RELEVANCE STATEMENT: The article explores the potential of using advanced language models, such as ChatGPT, in healthcare as a CDS for selecting appropriate imaging tests. Using ChatGPT can improve the efficiency of the decision-making process KEY POINTS: • ChatGPT recommendations were highly consistent with the recommendations provided by the ESR iGuide. • ChatGPT's ability in guiding the selection of appropriate tests may be comparable to some degree with ESR iGuide's.