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Purpose: To analyze patient satisfaction in Israeli Defense Forces primary care clinics (PCCs), and to compare different satisfaction indices that best correlate with general satisfaction index.Methods: Large-scale patient satisfaction survey throughout all PCCs, classified as active front clinics, training schools clinics, and home front clinics.Results: Participants (5,103) filled out standardized questionnaires. Patients in active front clinics were more satisfied with their PCCs than in the other two settings. Patients showed the highest satisfaction level in the environment questions and the lowest satisfaction level in the accessibility questions. In training school clinics and home front clinics, accessibility questions were most important, whereas in active front clinics, health outcome questions prevailed.Conclusions: Overall patient satisfaction is the highest in active front clinics, lower in training school clinics, and lowest in home front clinics. Accessibility is the most important factor in patient satisfaction in training school and home front units, and is less important in active front units. [ABSTRACT FROM AUTHOR]

Patient satisfaction is a fundamental parameter of quality in health care. Identification of aspects of care that influence patient satisfaction may be used to design changes in health delivery systems, thus improving quality of care. The objective of this study was to examine the relationship between the patient's assessment of quality of health care and the size of primary care clinics (PCCs) (measured as number of monthly patient visits) as well as the physician workload (measured as number of visits per physician per month). This study was a cross-sectional study using PCCs' characteristics and patient satisfaction surveys. One hundred one PCCs were evaluated. There was a negative correlation between all satisfaction indices and the number of primary care physicians in the clinic and the number of monthly visits to the clinic. In contrast, there was no significant correlation between the actual workload per physician. In general linear models, clinic size correlated significantly and negatively with patient satisfaction even after correction for other factors. The study concluded that patient satisfaction in the medical settings of the Israel Defense Forces is adversely affected by large clinic size but is not affected by physician workload. [ABSTRACT FROM AUTHOR]

Abstract Ilan and Donchin have compared Israel and Canada's experiences in setting a national patient safety agenda. We broaden this comparison to include the U.S. experience, and suggest that there are three additional key steps which will be important in any national patient safety agenda, and which Israel in particular should consider. These are 1) using health information technology (HIT) to directly improve patient safety, 2) dissemination and broad use of checklists, and 3) measuring patient safety over time at the national level. Especially because of its already substantial commitment to HIT and well-developed HIT sector, Israel has a major opportunity to move forward rapidly in this area and to achieve broad impact on the safety front. This is a commentary on http://www.ijhpr.org/content/1/1/19/

Background: Current atherosclerotic cardiovascular disease (ASCVD) risk assessment tools like the Pooled Cohort Equations (PCEs) and PREVENT™ scores offer long-term predictions but may not effectively drive behavior change. Short-term risk predictions using mobile health (mHealth) data and electronic health records (EHRs) could enhance clinical decision-making and patient engagement. The aim of this study was to develop a short-term ASCVD risk prediction model for hypertensive individuals using mHealth and EHR data and compare its performance to existing risk assessment tools., Methods: This is a retrospective cohort study including 51,127 hypertensive participants aged ≥18 years old who enrolled in the Hello Heart CV risk self-management program between January 2015 and January 2024. A machine learning (ML) model was derived from EHR data and mHealth measurements of blood pressure (BP) and heart rate (HR) collected via at-home BP monitors. Its performance was compared to that of PCE and PREVENT., Results: The XgBoost model incorporating 291 features outperformed the PCE and PREVENT scores in discriminating ASCVD risk for both prediction periods. For 90-day prediction, mean C-statistics were 0.81 (XgBoost) vs. 0.74 (PCE) and 0.65 (PREVENT). Similar findings were observed for 365-day prediction. mHealth measurements incrementally enhanced 365-day risk prediction (ROC-AUC 0.82 vs. 0.80 without mHealth)., Conclusion: An EHR and mHealth-based ML model offers superior short-term ASCVD prediction compared to traditional tools. This approach supports personalized preventive strategies, particularly for populations with incomplete features for PCE or PREVENT. Further research should explore this novel risk prediction framework, and particularly additional mHealth data integration for broader applicability and increased predictive power., Competing Interests: TG, HL, EP, HM, SG, PA, GP, LV, and AD are employed by Hello Heart and receive equity from Hello Heart. EP is also employed by White Plains Hospital. EZ is employed by Sheba Medical Center, Tel Hashomer and is an advisor for Hello Heart and receives consulting fees. JP is employed by Scripps Research Translational Institute and the Scripps Research team was funded by the National Center for Advancing Translational Sciences at the National Institutes of Health (UM1TR004407)., (© 2024 Gazit, Mann, Gaber, Adamenko, Pariente, Volsky, Dolev, Lyson, Zimlichman, Pandit and Paz.)

Introduction: The Israeli health system is experiencing an ongoing manpower crisis that will deepen soon with the increase in the number of patients and the overcrowding in clinics and hospitals. The core of the crisis is the need to staff the hospitalization departments and clinics with quality manpower. Alongside the initial staffing, there is an obligation to ensure the survivability of the professional personnel in the system over the years, and this must be done while constantly preventing the process of professional burnout. In the present article, we propose to change the face of medicine by moving from relying on a single professional (physician) to the operation of a multidisciplinary team, while ensuring that the various professionals operate at the peak of their skill envelope. At the base of the team is a physician who devotes himself to direct contact with the patient and his family while applying analytical and humanistic thinking based on a body of knowledge accumulated during his training.

Background: Predicting hospitalization from nurse triage notes has the potential to augment care. However, there needs to be careful considerations for which models to choose for this goal. Specifically, health systems will have varying degrees of computational infrastructure available and budget constraints., Objective: To this end, we compared the performance of the deep learning, Bidirectional Encoder Representations from Transformers (BERT)-based model, Bio-Clinical-BERT, with a bag-of-words (BOW) logistic regression (LR) model incorporating term frequency-inverse document frequency (TF-IDF). These choices represent different levels of computational requirements., Methods: A retrospective analysis was conducted using data from 1,391,988 patients who visited emergency departments in the Mount Sinai Health System spanning from 2017 to 2022. The models were trained on 4 hospitals' data and externally validated on a fifth hospital's data., Results: The Bio-Clinical-BERT model achieved higher areas under the receiver operating characteristic curve (0.82, 0.84, and 0.85) compared to the BOW-LR-TF-IDF model (0.81, 0.83, and 0.84) across training sets of 10,000; 100,000; and ~1,000,000 patients, respectively. Notably, both models proved effective at using triage notes for prediction, despite the modest performance gap., Conclusions: Our findings suggest that simpler machine learning models such as BOW-LR-TF-IDF could serve adequately in resource-limited settings. Given the potential implications for patient care and hospital resource management, further exploration of alternative models and techniques is warranted to enhance predictive performance in this critical domain., International Registered Report Identifier (irrid): RR2-10.1101/2023.08.07.23293699., (©Dhavalkumar Patel, Prem Timsina, Larisa Gorenstein, Benjamin S Glicksberg, Ganesh Raut, Satya Narayan Cheetirala, Fabio Santana, Jules Tamegue, Arash Kia, Eyal Zimlichman, Matthew A Levin, Robert Freeman, Eyal Klang. Originally published in JMIR AI (https://ai.jmir.org), 27.08.2024.)

Background: Hospital-at-home (HAH) is increasingly becoming an alternative for in-hospital stay in selected clinical scenarios. Nevertheless, there is still a question whether HAH could be a viable option for acutely ill patients, otherwise hospitalized in departments of general-internal medicine., Methods: This was a retrospective matched study, conducted at a telemedicine controlled HAH department, being part of a tertiary medical center. The objective was to compare clinical outcomes of acutely ill patients (both COVID-19 and non-COVID) admitted to either in-hospital or HAH. Non-COVID patients had one of three acute infectious diseases: urinary tract infections (UTI, either lower or upper), pneumonia, or cellulitis., Results: The analysis involved 159 HAH patients (64 COVID-19 and 95 non-COVID) who were compared to a matched sample of in-hospital patients (192 COVID-19 and 285 non-COVID). The median length-of-hospital stay (LOS) was 2 days shorter in the HAH for both COVID-19 patients (95% CI: 1-3; p = 0.008) and non-COVID patients (95% CI; 1-3; p < 0.001). The readmission rates within 30 days were not significantly different for both COVID-19 patients (Odds Ratio (OR) = 1; 95% CI: 0.49-2.04; p = 1) and non-COVID patients (OR = 0.7; 95% CI; 0.39-1.28; p = 0.25). The differences remained insignificant within one year. The risk of death within 30 days was significantly lower in the HAH group for COVID-19 patients (OR = 0.34; 95% CI: 0.11-0.86; p = 0.018) and non-COVID patients (OR = 0.38; 95% CI: 0.14-0.9; p = 0.019). For one year survival period, the differences were significant for COVID-19 patients (OR = 0.5; 95% CI: 0.31-0.9; p = 0.044) and insignificant for non-COVID patients (OR = 0.63; 95% CI: 0.4-1; p = 0.052)., Conclusions: Care for acutely ill patients in the setting of telemedicine-based hospital at home has the potential to reduce hospitalization length without increasing readmission risk and to reduce both 30 days and one-year mortality rates., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Zychlinski et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Large language models (LLMs) are transforming the field of natural language processing (NLP). These models offer opportunities for radiologists to make a meaningful impact in their field. NLP is a part of artificial intelligence (AI) that uses computer algorithms to study and understand text data. Recent advances in NLP include the Attention mechanism and the Transformer architecture. Transformer-based LLMs, such as GPT-4 and Gemini, are trained on massive amounts of data and generate human-like text. They are ideal for analysing large text data in academic research and clinical practice in radiology. Despite their promise, LLMs have limitations, including their dependency on the diversity and quality of their training data and the potential for false outputs. Albeit these limitations, the use of LLMs in radiology holds promise and is gaining momentum. By embracing the potential of LLMs, radiologists can gain valuable insights and improve the efficiency of their work. This can ultimately lead to improved patient care., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology.)

Disparities in access to health care are persistent and contribute to poor health outcomes for many populations around the world. Barriers to access are often similar across countries, despite differences in how health systems are structured. Health care leaders can work to address these barriers through bold, evidence-based actions. The Future of Health (FOH), an international community of senior health leaders, collaborated with the Duke-Margolis Institute for Health Policy to identify priority organizational and policy actions needed to improve equitable access to health care through a consensus-building exercise, a targeted literature review, and an expert discussion group. This paper describes four key action areas for health care leaders that FOH members identified as critical to enabling the future of equitable access to health care: ensuring prioritization of and accountability for equitable access to care; establishing comprehensive, organization-wide strategies to address barriers to access; clearly defining and incentivizing improvement on key measures related to reducing disparities in access; and establishing cross-sector partnerships to improve equitable access., Competing Interests: Conflicts of interest Please see ICMJE form(s) for author conflicts of interest. These have been provided as supplementary materials., (© The Author(s) 2024. Published by Oxford University Press on behalf of Project HOPE - The People-To-People Health Foundation, Inc.)

Background: Age and sex can be estimated using artificial intelligence on the basis of various sources. The aims of this study were to test whether convolutional neural networks could be trained to estimate age and predict sex using standard transthoracic echocardiography and to evaluate the prognostic implications., Methods: The algorithm was trained on 76,342 patients, validated in 22,825 patients, and tested in 20,960 patients. It was then externally validated using data from a different hospital (n = 556). Finally, a prospective cohort of handheld point-of-care ultrasound devices (n = 319; ClinicalTrials.gov identifier NCT05455541) was used to confirm the findings. A multivariate Cox regression model was used to investigate the association between age estimation and chronologic age with overall survival., Results: The mean absolute error in age estimation was 4.9 years, with a Pearson correlation coefficient of 0.922. The probabilistic value of sex had an overall accuracy of 96.1% and an area under the curve of 0.993. External validation and prospective study cohorts yielded consistent results. Finally, survival analysis demonstrated that age prediction ≥5 years vs chronologic age was associated with an independent 34% increased risk for death during follow-up (P < .001)., Conclusions: Applying artificial intelligence to standard transthoracic echocardiography allows the prediction of sex and the estimation of age. Machine-based estimation is an independent predictor of overall survival and, with further evaluation, can be used for risk stratification and estimation of biological age., Competing Interests: Disclosures Drs. Maor, Raanani, Klempfner, and Schwammenthal are cofounders of Aisap.ai. Mr. Am-Shalom is the CEO of Aisap.ai. Mr. Fiman and Mrs. Rubin are employed by Aisap.ai., (Copyright © 2024 American Society of Echocardiography. Published by Elsevier Inc. All rights reserved.)

This study was designed to assess how different prompt engineering techniques, specifically direct prompts, Chain of Thought (CoT), and a modified CoT approach, influence the ability of GPT-3.5 to answer clinical and calculation-based medical questions, particularly those styled like the USMLE Step 1 exams. To achieve this, we analyzed the responses of GPT-3.5 to two distinct sets of questions: a batch of 1000 questions generated by GPT-4, and another set comprising 95 real USMLE Step 1 questions. These questions spanned a range of medical calculations and clinical scenarios across various fields and difficulty levels. Our analysis revealed that there were no significant differences in the accuracy of GPT-3.5's responses when using direct prompts, CoT, or modified CoT methods. For instance, in the USMLE sample, the success rates were 61.7% for direct prompts, 62.8% for CoT, and 57.4% for modified CoT, with a p-value of 0.734. Similar trends were observed in the responses to GPT-4 generated questions, both clinical and calculation-based, with p-values above 0.05 indicating no significant difference between the prompt types. The conclusion drawn from this study is that the use of CoT prompt engineering does not significantly alter GPT-3.5's effectiveness in handling medical calculations or clinical scenario questions styled like those in USMLE exams. This finding is crucial as it suggests that performance of ChatGPT remains consistent regardless of whether a CoT technique is used instead of direct prompts. This consistency could be instrumental in simplifying the integration of AI tools like ChatGPT into medical education, enabling healthcare professionals to utilize these tools with ease, without the necessity for complex prompt engineering., (© 2024. The Author(s).)

Objectives: Large language models, including ChatGPT, has the potential to transform the way we approach medical knowledge, yet accuracy in clinical topics is critical. Here we assessed ChatGPT's performance in adhering to the American Academy of Otolaryngology-Head and Neck Surgery guidelines., Methods: We presented ChatGPT with 24 clinical otolaryngology questions based on the guidelines of the American Academy of Otolaryngology. This was done three times (N = 72) to test the model's consistency. Two otolaryngologists evaluated the responses for accuracy and relevance to the guidelines. Cohen's Kappa was used to measure evaluator agreement, and Cronbach's alpha assessed the consistency of ChatGPT's responses., Results: The study revealed mixed results; 59.7% (43/72) of ChatGPT's responses were highly accurate, while only 2.8% (2/72) directly contradicted the guidelines. The model showed 100% accuracy in Head and Neck, but lower accuracy in Rhinology and Otology/Neurotology (66%), Laryngology (50%), and Pediatrics (8%). The model's responses were consistent in 17/24 (70.8%), with a Cronbach's alpha value of 0.87, indicating a reasonable consistency across tests., Conclusions: Using a guideline-based set of structured questions, ChatGPT demonstrates consistency but variable accuracy in otolaryngology. Its lower performance in some areas, especially Pediatrics, suggests that further rigorous evaluation is needed before considering real-world clinical use., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Coronary artery disease (CAD) is a leading health challenge worldwide. Exercise stress testing is a foundational non-invasive diagnostic tool. Nonetheless, its variable accuracy prompts the exploration of more reliable methods. Recent advancements in machine learning (ML), including deep learning and natural language processing, have shown potential in refining the interpretation of stress testing data. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a systematic review of ML applications in stress electrocardiogram (ECG) and stress echocardiography for CAD prognosis. Medical Literature Analysis and Retrieval System Online, Web of Science, and the Cochrane Library were used as databases. We analysed the ML models, outcomes, and performance metrics. Overall, seven relevant studies were identified. Machine-learning applications in stress ECGs resulted in sensitivity and specificity improvements. Some models achieved rates of above 96% in both metrics and reduced false positives by up to 21%. In stress echocardiography, ML models demonstrated an increase in diagnostic precision. Some models achieved specificity and sensitivity rates of up to 92.7 and 84.4%, respectively. Natural language processing applications enabled the categorization of stress echocardiography reports, with accuracy rates nearing 98%. Limitations include a small, retrospective study pool and the exclusion of nuclear stress testing, due to its well-documented status. This review indicates the potential of artificial intelligence applications in refining CAD stress testing assessment. Further development for real-world use is warranted., Competing Interests: Conflict of interest: none declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Objective: We studied the extent of carbapenemase-producing Enterobacteriaceae (CPE) sink contamination and transmission to patients in a nonoutbreak setting., Methods: During 2017-2019, 592 patient-room sinks were sampled in 34 departments. Patient weekly rectal swab CPE surveillance was universally performed. Repeated sink sampling was conducted in 9 departments. Isolates from patients and sinks were characterized using pulsed-field gel electrophoresis (PFGE), and pairs of high resemblance were sequenced by Oxford Nanopore and Illumina. Hybrid assembly was used to fully assemble plasmids, which are shared between paired isolates., Results: In total, 144 (24%) of 592 CPE-contaminated sinks were detected in 25 of 34 departments. Repeated sampling (n = 7,123) revealed that 52%-100% were contaminated at least once during the sampling period. Persistent contamination for >1 year by a dominant strain was common. During the study period, 318 patients acquired CPE. The most common species were Klebsiella pneumoniae , Escherichia coli , and Enterobacter spp. In 127 (40%) patients, a contaminated sink was the suspected source of CPE acquisition. For 20 cases with an identical sink-patient strain, temporal relation suggested sink-to-patient transmission. Hybrid assembly of specific sink-patient isolates revealed that shared plasmids were structurally identical, and SNP differences between shared pairs, along with signatures for potential recombination events, suggests recent sharing of the plasmids., Conclusions: CPE-contaminated sinks are an important source of transmission to patients. Although traditionally person-to-person transmission has been considered the main route of CPE transmission, these data suggest a change in paradigm that may influence strategies of preventing CPE dissemination.

Objectives: With smartphones and wearable devices becoming ubiquitous, they offer an opportunity for large-scale voice sampling. This systematic review explores the application of deep learning models for the automated analysis of voice samples to detect vocal cord pathologies., Methods: We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines. We searched MEDLINE and Embase databases for original publications on deep learning applications for diagnosing vocal cord pathologies between 2002 and 2022. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2)., Results: Out of the 14 studies that met the inclusion criteria, data from a total of 3037 patients were analyzed. All studies were retrospective. Deep learning applications targeted Reinke's edema, nodules, polyps, cysts, unilateral cord paralysis, and vocal fold cancer detection. Most pathologies had detection accuracy above 90%. Thirteen studies (93%) exhibited a high risk of bias and concerns about applicability., Conclusions: Technology holds promise for enhancing the screening and diagnosis of vocal cord pathologies. While current research is limited, the presented studies offer proof of concept for developing larger-scale solutions., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Aims:  Assess the effectiveness of virtual reality (VR) technology, in reducing pain and anxiety, and improving adherence and glycemic control among children with type 1 diabetes (T1D)., Methods: Children with T1D, managed with continuous glucose monitoring and insulin pumps, were recruited for a randomized cross-over trial. Children were randomized to one of two interventions for diabetes management: group 1 used VR glasses first and group 2 listened to vocal-guided affective imagery first (audio). After 1 month, the interventions were crossed over. The outcome measures included pain and anxiety assessment, adherence, glycemic control, and patient-reported outcome measures (PROMs) of VR satisfaction and effectiveness., Results:  Forty children, mean age 11.4 ± 1.8 years, were participated. During the VR part, the monthly mean pain score compared to the baseline improved in both groups by 30% (p = 0.03). A 14% reduction in the state anxiety score was observed from baseline to 1 month in both groups (p = 0.009). Glycemic control measures including time in range, time above range, and glucose management indicator improved in both groups during VR part (p < 0.004 for all), compared to audio part. After one month, the patient-reported outcome measure (PROM) of satisfaction and effectiveness was sixfold higher after 1 month in group 1 compared to group 2 (p = 0.002). Adherence improved for both groups., Conclusions: VR was shown to be effective in reducing pain and anxiety, improving adherence, PROM, and glycemic control among children with T1D. We suggest incorporating VR technology in pediatric diabetes clinics to facilitate and improve coping and management of diabetes., Trial Registration: Trial registration number and date of registration for prospectively registered trials:ClinicalTrials.gov Identifier: NCT05883267, May 10th, 2023., (© 2023. Springer-Verlag Italia S.r.l., part of Springer Nature.)

The increase in hip fractures (HF) due to aging of the population and the rise in attractiveness of services provided at home following the COVID-19 pandemic, emphasize the need to compare outcomes of home versus hospital HF rehabilitation. To date, studies comparing the 2 services have focused primarily on clinical outcomes rather than patient-reported outcomes (PROs). This longitudinal observational study evaluated PROs of older adults with HF in the 2 settings. The SF36 questionnaire was used to measure PROs 3 times after surgery. The first PRO was retrospective and reflected pre-fracture health status. Descriptive statistics and mixed-effect logistic regression were used. Of 86 patients participating in the study, 41 had home rehabilitation and 45 had hospital rehabilitation. In both groups, the mental and physical scores plummeted 2 weeks after the HF, compared to pre-fracture status. The difference in improvement from pre-fracture status to recovery in both groups, were not significantly ( P  < .05) different, except for the pain domain. PROs of home versus hospital rehabilitation were similar, suggesting that rehabilitation at home can be as effective as hospital rehabilitation for suitable patients. This knowledge can improve quality of care in an aging global population., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Hip fracture patients (HFPs) frequently have multiple underlying conditions, necessitating that agreed-upon goals take these complications into consideration. Communication regarding goals between medical-personnel and patients is not always effective. Patient-reported outcomes (PROs) can outline personal goals and help promote quality health care in HFPs. Few studies have been published on this topic. The study's aim was to outline the process of using PROs for goal-directed therapy among HFPs., Methods: This sequential controlled trial was conducted among HFPs from two medical centres. The control and the intervention group received integrative rehabilitation. PROs were measured in both groups using the SF36 questionnaire three times postsurgery: 24-48 hours, 2 weeks and 3 months. During the first round of questioning, only the intervention group was asked 'what matters most to you?' during the rehabilitative process. Accordingly, agreed-upon goals that were determined by the SF36's eight topics and were incorporated into the HFP's rehabilitative process. A Likert scale of 1-5, '1' indicating no-achievement and '5' full-achievement, was used to assess the goal achievement 4-6 months post-fracture., Results: 84 HFPs participated in the study: 40 and 44 in the intervention and control group, respectively. In both groups, PROs declined after the HF, then improved somewhat 3 months later, but did not return to prefracture scores. Among the intervention group, 39% reached their specific goals (Likert level 5). Patients who achieved their goals had better PROs in comparison to others. The intervention group indicated PROs helped them articulate their desires and introduced them to new areas of care., Conclusions: Shifting from asking 'what's the matter?' to 'what matters most to you?' can improve the understanding of HFPs' own priorities, promote quality outcomes and enhance patient-centred care. Using PROs as a guide for goal-directed therapy can create a more inclusive process that includes the patients' most important health determinants and needs., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Digital transformation in healthcare during the COVID-19 pandemic led to the development of new hybrid models integrating physical and virtual care. The ability to provide remote care by telemedicine technologies and the need to better manage and control hospitals' occupancy accelerated growth in hospital-at-home programs. The Sheba Medical Center restructured to create Sheba Beyond as the first virtual hospital in Israel. These transformations enabled them to deliver hybrid services in their internal medicine unit by managing inpatient hospital-care with remote home-care based on the patients' medical condition. The hybrid services evolved to integrate care pathways multiplied by the mode of delivery-physical (in person) or virtual (technology enabled)-and the location of care-at the hospital or the patient home. The study examines this home hospitalization program pilot for internal medicine at Sheba Medical Center (MC). The research is based on qualitative semi-structured interviews with Sheba Beyond management, medical staff from the hospital and the Health Maintenance Organization (HMO), Architects, Information Technology (IT), Telemedicine and Medtech organizations. We investigated the implications of the development of hybrid services for the future design of the physical built-environment and the virtual technological platform. Our findings highlight the importance of designing for flexibility in the development of hybrid care services, while leveraging synergies across the built environment and digital platforms to support future models of care. In addition to exploring the potential for scalability in accelerating the flexibility of the healthcare system, we also highlight current barriers in professional, management, logistic and economic healthcare models., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Pilosof, Barrett, Oborn, Barkai, Zimlichman and Segal.)

Background and Objectives : Since its invention in the 1970s, the cochlear implant (CI) has been substantially developed. We aimed to assess the trends in the published literature to characterize CI. Materials and Methods : We queried PubMed for all CI-related entries published during 1970-2022. The following data were extracted: year of publication, publishing journal, title, keywords, and abstract text. Search terms belonged to the patient's age group, etiology for hearing loss, indications for CI, and surgical methodological advancement. Annual trends of publications were plotted. The slopes of publication trends were calculated by fitting regression lines to the yearly number of publications. Results : Overall, 19,428 CIs articles were identified. Pediatric-related CI was the most dominant sub-population among the age groups, with the highest rate and slope during the years (slope 5.2 ± 0.3, p < 0.001), while elderly-related CIs had significantly fewer publications. Entries concerning hearing preservation showed the sharpest rise among the methods, from no entries in 1980 to 46 entries in 2021 (slope 1.7 ± 0.2, p < 0.001). Entries concerning robotic surgery emerged in 2000, with a sharp increase in recent years (slope 0.5 ± 0.1, p < 0.001). Drug-eluting electrodes and CI under local-anesthesia have been reported only in the past five years, with a gradual rise. Conclusions : Publications regarding CI among pediatrics outnumbered all other indications, supporting the rising, pivotal role of CI in the rehabilitation of children with sensorineural hearing loss. Hearing-preservation publications have recently rapidly risen, identified as the primary trend of the current era, followed by a sharp rise of robotic surgery that is evolving and could define the next revolution.

Background: Goal-oriented patientcare is a key element in qualityhealthcare. Medical-caregiver's (MC) are expected to generate a shared decision-making process with patients regarding goals and expected health-outcomes. Hip-fracture patients (HFP) are usually older-adults with multiple health-conditions, necessitating that agreed-upon goals regarding the rehabilitation process, take these conditions into consideration. This topic has yet to be investigated by pairing and comparing the perception of expected outcomes and therapeutic goals of multidisciplinary MCs and their HF patient's. Our aim was to assess in a quantitative method whether HFPs and their multidisciplinary MCs agree upon target health-outcomes and their most important goals as they are reflected in the SF12 questionnaire., Methods: This was a cross-sectional, multi-center, study of HFPs and their MCs. Patients and MCs were asked to rate their top three most important goals for rehabilitation from the SF12 eight subscales: physical functioning, physical role limitation, bodily pain, general health, vitality, social functioning, emotional role limitation and mental health, and indicate their expected outcome. Descriptive statistics and mixed effect logistic-regression were used to compare concordance of the ratings. Agreement between patients and MCs was assessed using interclass coefficients (ICCs)., Results: A total of 378 ratings were collected from 52 patients, 12 nurses, 12 physicians and 6 paramedical personnel. Each patient had between 3 and 9 raters. Patients considered physical functioning and physical role limitation more important than did MCs. Physicians and nurses emphasized the importance of bodily pain while patients referred to it as relatively less significant. The total ICC was low (2%) indicating poor agreement between MCs and patients. With the exception of physical-functioning, MCs predicted a less optimistic outcome in all of the SF12's subscales in comparison to HFPs., Conclusion: Effective intervention in HFPs requires constructive communication between MCs and patients. The study suggests that caregivers have an insufficient understanding of the expectations of HFPs. More effective communication channels are required in order to better understand HFPs' needs and expectations., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2023.)

Digital transformation of healthcare systems should rely on decentralized computer networks and take advantage of the unique characteristics of blockchain technology. Decentralization ensures process transparency and data transparency for all relevant stakeholders. These values are essential in the realms of populations' healthcare information communications and processing, control and tracking of medical logistics supply chains, clinical research management, and control of certified healthcare services organizations. Mounting decentralized processes onto a blockchain-based computerized network will endow the values of immutability, improved cybersecurity, and potential for incentivizing stakeholders for relevant, pre-determined activities. One of the most relevant processes that would benefit from a decentralized, blockchain-based architecture is the submission, review, and publishing of scientific manuscripts. Current structures and processes in this world are non-transparent, poorly incentivizing significant stakeholders such as manuscripts' reviewers, and many are potentially corrupted. In this review, we suggest a blockchain-based architecture for such systems and advocate further research and development in several domains of modern healthcare systems-offering medicine to become "the new guy on the block (chain).", Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2023.)

Introduction: In recent year, many attempts have been made to provide patients with alternatives to psychiatric hospitalization during acute distress. Although several hospitalization alternatives have been offered, most of them still require patients to be distanced from their families, friends, and the social environment., Methods: In this report we describe the implementation of a novel approach to psychiatric care termed "Technologically assisted Intensive Home Treatment", where patients arriving to emergency settings are directed to home care with technological aids that enable close monitoring and ongoing contact with their therapists., Results: We describe the rationale and treatment principles of the treatment, and provide an elaborative description of the implementation process during the first year of implementation., Discussion: Additional attention is given to factors associated with early dropout from the program, in order to inform readers of predictors to optimal care. Limitations and directions for future research and practice are discussed. Clinical Trial Registration: The study was registered in the database of clinical trials (registration number SHEBA-19-6555-MW-CTIL) and in the Ministry of Health (registration number MOH&#95;2022-08-22&#95;011992)., Competing Interests: DT received research grants from the American Psychological Foundation and from Pfizer. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Caspi, Tzur Bitan, Halaly, Hallaly, Friedlander, Barkai, Zimlichman, Stein, Shani, Amitai, Ansbacher and Weiser.)

Background: Deep learning techniques can accurately detect and grade inflammatory findings on images from capsule endoscopy (CE) in Crohn's disease (CD). However, the predictive utility of deep learning of CE in CD for disease outcomes has not been examined., Objectives: We aimed to develop a deep learning model that can predict the need for biological therapy based on complete CE videos of newly-diagnosed CD patients., Design: This was a retrospective cohort study. The study cohort included treatment-naïve CD patients that have performed CE (SB3, Medtronic) within 6 months of diagnosis. Complete small bowel videos were extracted using the RAPID Reader software., Methods: CE videos were scored using the Lewis score (LS). Clinical, endoscopic, and laboratory data were extracted from electronic medical records. Machine learning analysis was performed using the TimeSformer computer vision algorithm developed to capture spatiotemporal characteristics for video analysis., Results: The patient cohort included 101 patients. The median duration of follow-up was 902 (354-1626) days. Biological therapy was initiated by 37 (36.6%) out of 101 patients. TimeSformer algorithm achieved training and testing accuracy of 82% and 81%, respectively, with an Area under the ROC Curve (AUC) of 0.86 to predict the need for biological therapy. In comparison, the AUC for LS was 0.70 and for fecal calprotectin 0.74., Conclusion: Spatiotemporal analysis of complete CE videos of newly-diagnosed CD patients achieved accurate prediction of the need for biological therapy. The accuracy was superior to that of the human reader index or fecal calprotectin. Following future validation studies, this approach will allow for fast and accurate personalization of treatment decisions in CD., Competing Interests: UK: research support – Medtronic, Janssen, Takeda. Speaker/advisory fees – AbbVie, BMS, Janssen, Pfizer, Takeda, MSD, Rafa. RE: Speaker for Takeda, Janssen, and Medtronic. SBH: received consulting and advisory board fees and/or research support from AbbVie, MSD, Janssen, Takeda, Pfizer, GSK, and CellTrion. EK, SheS, RMY, OB, and EZ: no competing interests. RK, AB, EA, and ShiS: employees of Intel Inc., (© The Author(s), 2023.)

Developing rapid tools for early detection of viral infection is crucial for pandemic containment. This is particularly crucial when testing resources are constrained and/or there are significant delays until the test results are available - as was quite common in the early days of Covid-19 pandemic. We show how predictive analytics methods using machine learning algorithms can be combined with optimal pre-test screening mechanisms, greatly increasing test efficiency (i.e., rate of true positives identified per test), as well as to allow doctors to initiate treatment before the test results are available. Our optimal test admission policies account for imperfect accuracy of both the medical test and the model prediction mechanism. We derive the accuracy required for the optimized admission policies to be effective. We also show how our policies can be extended to re-testing high-risk patients, as well as combined with pool testing approaches. We illustrate our techniques by applying them to a large data reported by the Israeli Ministry of Health for RT-PCR tests from March to September 2020. Our results demonstrate that in the context of the Covid-19 pandemic a pre-test probability screening tool with conventional RT-PCR testing could have potentially increased efficiency by several times, compared to random admission control., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objective: To directly compare the prevalence of chemosensory dysfunction (smell and taste) in geographically distinct regions with the same questionnaires., Methods: A cross-sectional study was performed to evaluate the self-reported symptoms among adults (older than 18 years) who underwent COVID-19 testing at an ambulatory assessment center in Canada and at a hospital in Israel between March 16, 2020, and August 19, 2020. The primary outcome was the prevalence of self-reported chemosensory dysfunction (anosmia/hypomsia and dysgeusia/ageusia). Subgroup analysis was performed to evaluate the prevalence of chemosensory deficits among the outpatients., Results: We identified a total of 350 COVID-19-positive patients (138 Canadians and 212 Israelis). The overall prevalence of chemosensory dysfunction was 47.1%. There was a higher proportion of chemosensory deficits among Canadians compared to Israelis (66.7% vs 34.4%, P < .01). A subgroup analysis for outpatients (never hospitalized) still identified a higher prevalence of chemosensory dysfunction among Canadians compared to Israelis (68.2% vs 36.1%, P < 0.01). A majority of patients recovered their sense of smell after 4 weeks of symptom onset., Conclusion: Although the prevalence of chemosensory deficit in COVID-19 was found to be similar to previously published reports, the prevalence can vary significantly across different geographical regions. Therefore, it is important to obtain regionally specific data so that the symptom of anosmia/dysgeusia can be used as a guide for screening for the clinical diagnosis of COVID-19.

Data about in-hospital AKI in RTRs is lacking. We conducted a retrospective study of 292 RTRs, with 807 hospital admissions, to reveal predictors and outcomes of AKI during admission. In-hospital AKI developed in 149 patients (51%). AKI in a previous admission was associated with a more than twofold increased risk of AKI in subsequent admissions (OR 2.13, p < 0.001). Other major significant predictors for in-hospital AKI included an infection as the major admission diagnosis (OR 2.93, p = 0.015), a medical history of hypertension (OR 1.91, p = 0.027), minimum systolic blood pressure (OR 0.98, p = 0.002), maximum tacrolimus trough level (OR 1.08, p = 0.005), hemoglobin level (OR 0.9, p = 0.016) and albumin level (OR 0.51, p = 0.025) during admission. Compared to admissions with no AKI, admissions with AKI were associated with longer length of stay (median time of 3.83 vs. 7.01 days, p < 0.001). In-hospital AKI was associated with higher rates of mortality during admission, almost doubled odds for rehospitalization within 90 days from discharge and increased the risk of overall mortality in multivariable mixed effect models. In-hospital AKI is common and is associated with poor short- and long-term outcomes. Strategies to prevent AKI during admission in RTRs should be implemented to reduce re-admission rates and improve patient survival., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Hod, Oberman, Scott, Levy, Shlomai, Beckerman, Cohen-Hagai, Mor, Grossman, Zimlichman and Shashar.)

Hip-fractures (HF) in older adults are associated with poor outcomes and high costs. Measuring quality-of-care of HF patients has focused on clinical definitions rather than on measuring outcomes that are meaningful to the patient. Healthcare systems worldwide are increasingly interested in patient-reported outcome measures (PROs). The Short-form (SF36) questionnaire is a recommended measure among older adults however it's comprehensiveness and uniqueness for specific patients after a HF is not clear. The aims of this study were to: understand the perspective of the older adults experience following HF, to assess the suitability of the SF36 as a PRO for HF and to determine the best timing for questioning. A qualitative description approach was used. This took place in 2 large academic medical-centers in Israel. The inquiry was done in 2 parts by semi-structured interview. A total 15 HF patients were interviewed. Categories and themes emerging from their responses were similar to the 8 domains of the SF36 questionnaire, but the participants added clarity regarding their own needs for setting goals. In the second part, participants agreed that the SF36 reflected common issues and served as an adequate measure for personal-goal setting. The study encourages patient-centered care in older adults recovering from HF, providing evidence that the SF36 is a suitable tool for measuring PROs in HF patients. Healthcare systems focus on clinical-outcome indicators and do not reflect how the patient views his outcomes. This study provides evidence that care should be customized for each person.

Failure mode and effect analysis (FMEA) is a leading tool for risk management in health care. The term "blanket" approach FMEA describes a comprehensive simultaneous look at the variety of interrelated factors that may directly and indirectly affect patient safety. Applying FMEA with the "blanket" approach is not common, due to FMEA's limitations. Algorithmic prediction of failure modes in health care (APFMH) is leaner and enables the application of the "blanket" approach, but, like FMEA, it lacks formal validation. The authors set out to validate the APFMH method while applying a "blanket" approach. They analyzed the sterile supply handling at a 1900-bed academic medical center. The study's first step took place in the operating room (OR) aspect of the process. An APFMH analysis was performed using the "blanket" approach, to identify the hazards and define the common root causes for predicted hazards. The second step took place a year later at the sterile supply and equipment department (SSED) and aimed to validate these root causes, thus validating the reliability of APFMH. The "blanket" approach analysis with the APFMH method consisted of categorization into 3 risk-dimensions: patient safety, equipment damage, and time management. Root causes were defined for 8 high-ranking hazards. All the root causes for failures, identified by APFMH at the OR department, were revealed as actual hazards in the processes of the SSED. The independent findings at the SSED level validated the list of identified hazards that was formed at the target department (ie, the OR). APFMH methodology is a lean in time and human resources process that ensures comprehensive hazard analysis, which can include the "blanket" approach, and which was validated in this study. The authors suggest using the APFMH methodology for any organizational analysis method that requires the inclusion of "blanket" approaches., (Copyright © 2022 the American College of Medical Quality.)

Background: The coronavirus 2019 (COVID-19) pandemic affected health-care systems worldwide, leading to fewer admissions and raising concerns about the quality of care. The objective of this study was to investigate the early effects of the COVID-19 pandemic on quality of care among stroke and ST-elevation myocardial infarction (STEMI) patients, focusing on clinical outcomes and direct treatment costs., Method: This retrospective, observational study was based on the 10-week period that included the first wave of the COVID-19 pandemic in Israel (15 February 2020-30 April 2020). Emergency department admissions for stroke and STEMI were compared with parallel periods in 2017-2019, focusing on demographics, risk and severity scores, and the effect of clinical outcomes on hospitalization costs., Results: The 634 stroke and 186 STEMI cases comprised 16% and 19% fewer admissions, respectively, compared to 2019. No significant changes were detected in demographics, most disease management parameters, readmission and mortality outcomes. The mean door-to-balloon time increased insignificantly by 33%, lowering the health quality indicator (HQI) for treatment in <90 min from 94.7% in 2017-2019 to 83% in 2020 (P = 0.022). Among suspected stroke patients, 97.2% underwent imaging, with 28% longer median time from admission (P = 0.05). Consequently, only 24.3% met the HQI of imaging in <29 min, compared to 45.5% in 2017-2019 (P < 0.01). Increased length of stay and more intensive care unit admissions were the leading causes of 6.5% increased mean cost of STEMI patients' initial hospitalization, which totaled $29 300 in the COVID-19 period (P = 0.008)., Conclusion: The initial pandemic period caused a decline in HQIs linked to diagnostic and treatment protocols, without changes in outcomes, but with increased hospitalization costs. Medical information and awareness of life-threatening conditions among patients and caregivers should be increased to enable proper diagnosis and management., (© The Author(s) 2022. Published by Oxford University Press on behalf of International Society for Quality in Health Care. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)