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.

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.

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.

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.

Inappropriate CT examinations: how much, who and where? Insights from a clinical decision support system (CDSS) analysis.

OBJECTIVE: To assess the appropriateness of Computed Tomography (CT) examinations, using the ESR-iGuide. MATERIAL AND METHODS: A retrospective study was conducted in 2022 in a medium-sized acute care teaching hospital. A total of 278 consecutive cases of CT referral were included. For each imaging referral, the ESR-iGuide provided an appropriateness score using a scale of 1-9 and the Relative Radiation Level using a scale of 0-5. These were then compared with the appropriateness score and the radiation level of the recommended ESR-iGuide exam. DATA ANALYSIS: Pearson's chi-square test or Fisher exact test was used to explore the correlation between ESR-iGuide appropriateness level and physician, patients, and shift characteristics. A stepwise logistic regression model was used to capture the contribution of each of these factors. RESULTS: Most of exams performed were CT head (63.67%) or CT abdominal pelvis (23.74%). Seventy percent of the actual imaging referrals resulted in an ESR-iGuide score corresponding to "usually appropriate." The mean radiation level for actual exam was 3.2 ± 0.45 compared with 2.16 ± 1.56 for the recommended exam. When using a stepwise logistic regression for modeling the probability of non-appropriate score, both physician specialty and status were significant (p = 0.0011, p = 0.0192 respectively). Non-surgical and specialist physicians were more likely to order inappropriate exams than surgical physicians. CONCLUSIONS: ESR-iGuide software indicates a substantial rate of inappropriate exams of CT head and CT abdominal-pelvis and unnecessary radiation exposure mainly in the ED department. Inappropriate exams were found to be related to physicians' specialty and seniority. CLINICAL RELEVANCE STATEMENT: These findings underscore the urgent need for improved imaging referral practices to ensure appropriate healthcare delivery and effective resource management. Additionally, they highlight the potential benefits and necessity of integrating CDSS as a standard medical practice. By implementing CDSS, healthcare providers can make more informed decisions, leading to enhanced patient care, optimized resource allocation, and improved overall healthcare outcomes. KEY POINTS: • The overall mean of appropriateness for the actual exam according to the ESR-iGuide was 6.62 ± 2.69 on a scale of 0-9. • Seventy percent of the actual imaging referrals resulted in an ESR-iGuide score corresponding to "usually appropriate." • Inappropriate examination is related to both the specialty of the physician who requested the exam and the seniority status of the physician.

Dynamic Trends in Sociodemographic Disparities and COVID-19 Morbidity and Mortality-A Nationwide Study during Two Years of a Pandemic.

Social epidemiological research has documented that health outcomes, such as the risk of becoming diseased or dying, are closely tied to socioeconomic status. The aim of the current study was to investigate the impact of socioeconomic status on morbidity, hospitalization, and mortality outcomes throughout five waves of the pandemic amongst the Israeli population. A retrospective archive study was conducted in Israel from March 2020 to February 2022 in which data were obtained from the Israeli Ministry of Health's (MOH) open COVID-19 database. Our findings, though requiring careful and cautious interpretation, indicate that the socioeconomic gradient patterns established in previous COVID-19 literature are not applicable to Israel throughout the five waves of the pandemic. The conclusions of this study indicate a much more dynamic and complex picture, where there is no single group that dominates the realm of improved outcomes or bears the burden of disease with respect to morbidity, hospitalization, and mortality. We show that health trends cannot necessarily be generalized to all countries and are very much dynamic and contingent on the socio-geographical context and must be thoroughly examined throughout distinct communities with consideration of the specific characteristics of the disease. Furthermore, the implications of this study include the importance of identifying the dynamic interplay and interactions of sociodemographic characteristics and health behavior in order to enhance efforts toward achieving improved health outcomes by policymakers and researchers.

Inequalities in end-stage renal disease: underprivileged and ethnic minority members are at higher risk.

BACKGROUND: Incidence of end-stage renal disease (ESRD) is higher in Israel than the European average. Socio-economic differences in ESRD have been reported globally, but many countries lack a national register. Using national data, we assessed which socio-demographic factors are associated with 5-year incidence of ESRD in Israel, where there is universal access to renal replacement therapy (RRT). METHODS: Data on all incident ESRD cases aged ≥20 years receiving chronic RRT between 1 January 2014 and 31 December 2018 (N = 7883) were collected from Israel's National Dialysis & Renal Transplant Register. Individual-level data on ESRD cases requiring RRT included residential area, age, gender, ethnicity (Jewish or Arab) and ESRD cause (diabetes, other, unknown/missing). Area-level data included age and sex distribution, socio-economic status (SES) and proportion of Arab population. The associations between individual-level socio-demographic characteristics and ESRD cause were tested in bivariate comparisons. The risk of developing ESRD during the study period (from all and specific causes) was estimated using multiple Poisson regression models with negative binomial distribution, using four parameters, namely sex, ethnicity, SES category and age strata, based on area-level distribution of these parameters, and with the whole population (aged ≥20 years) as the denominator. RESULTS: A socio-economic gradient was seen for ESRD from all causes, more marked for diabetic aetiology [rate ratio (RR)=0.45, 95% CI: 0.39-0.52 highest vs lowest SES categories] than from other (RR = 0.64, 95% CI: 0.55-0.75) or unknown cause (RR = 0.79, 95% CI: 0. 62-0.99). Based on population area-level data, predominantly Arab neighbourhoods showed higher risk for ESRD requiring RRT for all causes, with the strongest association for diabetes (RR = 1.69, 95% CI: 1.53-1.86) adjusted for SES, age and sex. CONCLUSIONS: A strong socio-economic gradient was demonstrated for ESRD requiring RRT. Arab ethnicity was associated with higher risk for ESRD, especially due to diabetes. Our findings suggest the need for allocation of health resources according to needs and culturally appropriate interventions for improving control of modifiable risk factors for chronic renal failure.

Does clinical decision support system promote expert consensus for appropriate imaging referrals? Chest-abdominal-pelvis CT as a case study.

OBJECTIVES: We assessed the appropriateness of chest-abdominal-pelvis (CAP) CT scan use in the Emergency Department (ED), based on expert physicians and the ESR iGuide, a clinical decision support system (CDSS). METHODS: A retrospective cross-study was conducted. We included 100 cases of CAP-CT scans ordered at the ED. Four experts rated the appropriateness of the cases on a 7-point scale, before and after using the decision support tool. RESULTS: Before using the ESR iGuide the overall mean rating of the experts was 5.2 ± 1.066, and it increased slightly after using the system (5.85 ± 0.911 (p < 0.01)). Using a threshold of 5 (on a 7-level scale), the experts considered only 63% of the tests appropriate before using the ESR iGuide. The number increased to 89% after consultation with the system. The degree of overall agreement among the experts was 0.388 before ESR iGuide consultation and 0.572 after consultation. According to the ESR iGuide, for 85% of the cases, CAP CT was not a recommended option (score 0). Abdominal-Pelvis CT was "usually appropriate" for 65 out of the 85 (76%) cases (score 7-9). 9% of the cases did not require CT as first exam modality. CONCLUSIONS: According to both the experts and the ESR iGuide, inappropriate testing was prevalent, in terms of both frequency of the scans and also inappropriately chosen body regions. These findings raise the need for unified workflows that might be achieved using a CDSS. Further studies are needed to investigate the CDSS contribution to the informed decision-making and increased uniformity among different expert physicians when ordering the appropriate test.

Socioeconomic and ethnic disparities along five waves of the COVID-19 pandemic: Lessons we have not yet learnt.

PURPOSE: The COVID-19 pandemic, now in its third year, has served as a magnifying glass, exposing the inequitable impact of the outbreak. The study aims to analyze the relationships between the socioeconomic and ethnic characteristics of the population and COVID-19 testing, infection, and vaccination throughout the first five pandemic waves. DESIGN: A secondary analysis of an existing national database was conducted in Israel from March 2020 to May 2022. During the study period, Israel underwent 5 pandemic peaks or waves (March-April 2020, September-October 2020, January-February 2021, September 2021, and January-February 2022). METHODS: Data on tests performed, confirmed COVID-19 cases, and uptake of vaccine doses one through four during the study period, were analyzed by the socioeconomic (SE) cluster (scale of 1 to 10) and ethnicity (Jewish, Arab, mixed Jewish- Arab ethnicity) of the residents' local authority. RESULTS: COVID-19 testing rate gradually increased from the lowest to the highest SE clusters, with rates 3.2 times higher in the second highest, compared with the lowest cluster. People living in Jewish localities were tested twice more than those in Arab or mixed localities. The rate of confirmed cases was 1.9, 3.0, 6.3, and 4.3 times higher, respectively, among cluster 1 (the lowest) compared with cluster 9 (second highest) in the first, second, third, and fourth pandemic waves, respectively. Rates among people living in Arab or mixed localities were higher compared with those living in Jewish localities in 3 of the 5 waves. Vaccine uptake revealed a clear social gradient, with the percentage of the population being vaccinated gradually increasing from cluster 1 (the lowest) to the higher clusters. The relative difference between the lowest and highest SE clusters increased from 2.4 in the first vaccine dose to 5.5 in the third and fourth doses. Ethnic disparities also grew with vaccine dose, with a Jewish to an Arab rate ratio of 1.1, 1.2, 1.6, and 4.5 for vaccine doses 1,2,3, and 4, respectively. CONCLUSIONS: Covering 26 consecutive months of the COVID-19 pandemic at the national level, the current study demonstrates that despite high accessibility of tests and vaccines to the entirety of the population and tailored outreach efforts, socioeconomic, and ethnic disparities not only failed to diminish, but they even widened along the five pandemic waves. CLINICAL RELEVANCE: The pandemic exposed the vulnerability of the weakest segments of the population. Therefore, the combined action of the Ministry of Health, health providers, and local authorities is required to further adapt health messages to the cultural characteristics of diverse populations, to equip the health professionals with practical tools to promote healthy choices among the vulnerable populations, and to build communities that promote healthy lifestyles. The pandemic has highlighted the importance of reducing health disparities and building trust between vulnerable populations and the healthcare system during "normal" or routine times, to better prepare for times of emergencies, such as the current pandemic.

Timely childhood vaccination in Israel: a national retrospective study of ethnic and socioeconomic disparities.

A large proportion of children do not receive vaccines within the recommended timeframe. This study examined ethnic and socioeconomic differences in age-appropriate immunization of children in Israel, where immunization is freely available. Percent of children receiving MMR/V at 12-13 months, and four doses of DTP/IPV/Hib by 18 months were obtained from the National Programme for Quality Measures between 2015 and 2018. Ethnic group (Jewish vs Arab) (defined by proxy by the neighbourhood in which the clinic was located), neighbourhood socioeconomic status and peripherality were obtained. Rates of MMR vaccination were 61% in the Jewish and 82% in the Arab population; for DPT/IPV/Hib 75% in the Jewish, compared to 92% in the Arab population. These patterns were stable over time. Lowest rates occurred in the most peripheral areas for Arab children, and in urban areas for Jewish children. Differences between ethnic groups were significant at higher SES levels. Greater adherence to the vaccination schedule occurred in the Arab minority in contrast to studies showing lower vaccination in ethnic minorities elsewhere. Lower immunization rates among rural Arab children suggest a need for improved access to clinics. Efforts should be directed towards lower SES groups, while emphasizing the importance of timely vaccination in wealthier groups in order to achieve herd immunity.