Background: With the recent developments in automated tools, smaller and cheaper machines for lung ultrasound (LUS) are leading us toward the potential to conduct POCUS tele-guidance for the early detection of pulmonary congestion. This study aims to evaluate the feasibility and accuracy of a self-lung ultrasound study conducted by hemodialysis (HD) patients to detect pulmonary congestion, with and without artificial intelligence (AI)-based automatic tools. Methods: This prospective pilot study was conducted between November 2020 and September 2021. Nineteen chronic HD patients were enrolled in the Soroka University Medical Center (SUMC) Dialysis Clinic. First, we examined the patient’s ability to obtain a self-lung US. Then, we used interrater reliability (IRR) to compare the self-detection results reported by the patients to the observation of POCUS experts and an ultrasound (US) machine with an AI-based automatic B-line counting tool. All the videos were reviewed by a specialist blinded to the performer. We examined their agreement degree using the weighted Cohen’s kappa (Kw) index. Results: A total of 19 patients were included in our analysis. We found moderate to substantial agreement between the POCUS expert review and the automatic counting both when the patient performed the LUS (Kw = 0.49 [95% CI: 0.05–0.93]) and when the researcher performed it (Kw = 0.67 [95% CI: 0.67–0.67]). Patients were able to place the probe in the correct position and present a lung image well even weeks from the teaching session, but did not show good abilities in correctly saving or counting B-lines compared to an expert or an automatic counting tool. Conclusions: Our results suggest that LUS self-monitoring for pulmonary congestion can be a reliable option if the patient’s count is combined with an AI application for the B-line count. This study provides insight into the possibility of utilizing home US devices to detect pulmonary congestion, enabling patients to have a more active role in their health care.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Evidence-based clinical intake tools (EBCITs) are structured assessment tools used to gather information about patients and help healthcare providers make more informed decisions. The growing demand for personalized medicine, along with the big data revolution, have rendered EBCITs a promising solution. EBCITs have the potential to provide comprehensive and individualized assessments of symptoms, enabling accurate and timely diagnosis, while contributing to the grounding of medical care. Objective: This work examines whether EBCITs cover data concerning disorders and symptoms to a similar extent as physicians, thus can reliably address medical conditions in clinical settings. We also explore the potential of EBCITs to discover and ground real prevalence of symptoms in different disorders thereby expanding medical knowledge and further support medical diagnoses made by physicians. Methods: Between August 1, 2022, and January 15, 2023, patients who used the services of a virtual healthcare (VH) provider in the USA were first assessed by the Kahun EBCIT. Kahun platform gathered, documented, and analyzed the information from the sessions and its clinical findings. In this study, we estimated the prevalence of patients' symptoms in medical disorders, using two datasets. The first set analyzed symptoms prevalence, as determined by the Kahun's knowledge engine. The second set analyzed symptoms prevalence, relying solely on data from the VH patients gathered by Kahun. The difference in variance between these two prevalence datasets, helped us assess Kahun's ability to incorporate new data, while integrating existing knowledge. To analyze the comprehensiveness of the Kahun's knowledge engine, we compared how well it covers weighted data for the symptoms and disorders found in the 2019 National Ambulatory Medical Care Survey (NAMCS). To assess Kahun's diagnosis accuracy, physicians independently diagnosed 250 of Kahun-VH’s sessions. Their diagnoses were compared with Kahun's diagnoses. Results: As part of this work, 2,550 patients used Kahun to complete a full assessment, among them 1,714 females and 836 males. Kahun collected 314 different chief complaints and proposed 108,523 suggestions related to symptoms during the intake process. At the end of the intake process, 6,496 conditions were presented to the caregiver. Kahun covered 94% (526,157,569/562,150,572) of the weighted symptoms and 91% (1,582,637,476/173,4783,244) of the weighted disorders in NAMCS 2019. In 90% (224/250) of the sessions, at least one identical disorder suggested by both the physicians and Kahun, with total accuracy rate of 72% (367/507). Kahun’s engine yielded 519 prevalences while the Kahun-VH cohort yielded 599; 156 prevalences were unique to the latter and 443 prevalences were shared by both databases. Conclusions: ECBITs, such as Kahun, encompass extensive amounts of knowledge and could serve as a reliable database for inferring medical insights and diagnosis. Using this credible database, potential prevalence of symptoms in different disorders were discovered or grounded. This highlights the ability of ECBITs to refine the understanding of relationships between disorders and symptoms, which further supports physicians in medical diagnosis.

BACKGROUND Evidence-based clinical intake tools (EBCITs) are structured assessment tools used to gather information about patients and help health care providers make informed decisions. The growing demand for personalized medicine, along with the big data revolution, has rendered EBCITs a promising solution. EBCITs have the potential to provide comprehensive and individualized assessments of symptoms, enabling accurate diagnosis, while contributing to the grounding of medical care. OBJECTIVE This work aims to examine whether EBCITs cover data concerning disorders and symptoms to a similar extent as physicians, and thus can reliably address medical conditions in clinical settings. We also explore the potential of EBCITs to discover and ground the real prevalence of symptoms in different disorders thereby expanding medical knowledge and further supporting medical diagnoses made by physicians. METHODS Between August 1, 2022, and January 15, 2023, patients who used the services of a digital health care (DH) provider in the United States were first assessed by the Kahun EBCIT. Kahun platform gathered and analyzed the information from the sessions. This study estimated the prevalence of patients’ symptoms in medical disorders using 2 data sets. The first data set analyzed symptom prevalence, as determined by Kahun’s knowledge engine. The second data set analyzed symptom prevalence, relying solely on data from the DH patients gathered by Kahun. The variance difference between these 2 prevalence data sets helped us assess Kahun’s ability to incorporate new data while integrating existing knowledge. To analyze the comprehensiveness of Kahun’s knowledge engine, we compared how well it covers weighted data for the symptoms and disorders found in the 2019 National Ambulatory Medical Care Survey (NMCAS). To assess Kahun’s diagnosis accuracy, physicians independently diagnosed 250 of Kahun-DH’s sessions. Their diagnoses were compared with Kahun’s diagnoses. RESULTS In this study, 2550 patients used Kahun to complete a full assessment. Kahun proposed 108,523 suggestions related to symptoms during the intake process. At the end of the intake process, 6496 conditions were presented to the caregiver. Kahun covered 94% (526,157,569/562,150,572) of the weighted symptoms and 91% (1,582,637,476/173,4783,244) of the weighted disorders in the 2019 NMCAS. In 90% (224/250) of the sessions, both physicians and Kahun suggested at least one identical disorder, with a 72% (367/507) total accuracy rate. Kahun’s engine yielded 519 prevalences while the Kahun-DH cohort yielded 599; 156 prevalences were unique to the latter and 443 prevalences were shared by both data sets. CONCLUSIONS ECBITs, such as Kahun, encompass extensive amounts of knowledge and could serve as a reliable database for inferring medical insights and diagnoses. Using this credible database, the potential prevalence of symptoms in different disorders was discovered or grounded. This highlights the ability of ECBITs to refine the understanding of relationships between disorders and symptoms, which further supports physicians in medical diagnosis.

Background: Acute otitis media (AOM) is a leading cause of antibiotic prescriptions in children. The “weekend effect”—reduced care quality outside standard hours due to lower senior supervision—has been reported for various pediatric conditions, but its impact on AOM management remains unexplored. Methods: We conducted a retrospective cross-sectional study of children aged 6 months to 10 years diagnosed with uncomplicated AOM in a secondary academic pediatric emergency department (PED) between 2017 and 2024. Treatment appropriateness was assessed against the 2023 Israeli AOM guidelines. Care optimality was stratified by visit timing and physician seniority into 3 levels: level 0 (weekday, attending present), level 1 (after-hours, attending present) and level 2 (after-hours, no attending). Results: Of 288 children included, weekend and weekday visitors were clinically and demographically comparable. The presence of attending physicians was significantly higher on weekdays (82.8% vs. 55.6%, P < 0.001). Guideline-appropriate treatment was achieved in 72.9% of cases overall, with amoxicillin as the first-line chosen antibiotic therapy. No significant differences in treatment appropriateness were found between weekend and weekday visits, nor across care optimality levels ( P = 0.363), indicating that physician supervision level did not influence guideline adherence. Conclusions: AOM management was guideline-adherent regardless of visit timing or physician supervision level, with no weekend effect identified. These findings suggest effective standardization of clinical practice in our pediatric emergency department and support maintaining current protocols as a model for consistent AOM care in emergency settings.