Category: Nevin Manimala

JMIR Med Inform. 2026 May 19;14:e84916. doi: 10.2196/84916.

ABSTRACT

BACKGROUND: This exploratory study investigated the impact of computer use on physician performance during clinical simulations. Standardized patient (SP) scenarios used in family practice certification examinations were adapted to include the use of the electronic health record (EHR).

OBJECTIVE: The goal was to compare the impact of EHR use during simulated virtual patient encounters on resident physicians’ and staff physicians’ patient-centeredness (PC) and overall clinical performance, as well as to measure the cognitive load (CL) imposed by EHR use.

METHODS: Sixteen participants each completed 2 video telemedicine simulations with SPs. One simulation case included limited past medical history for the SP in the EHR, while the other did not. Participants were instructed to completely document the encounter using the EHR. Participants’ self-perceived CL was measured using the raw National Aeronautics and Space Administration Task Load Index (NASA-TLX). Video recordings were analyzed for participant PC and overall clinical performance. In addition to interacting with the EHR, multiple participants also conducted internet searches. The proportion of time that participants spent interacting with the computer, either using the EHR or searching the internet, was calculated. Inductive qualitative coding of a subset of video recordings (18 of 32 encounters) was performed, with a focus on signs of stress/CL. All videos were assessed for usability problems.

RESULTS: Staff physicians (n=6) scored higher on PC compared to resident physicians (n=10) for both cases, though differences were not statistically significant after correction for multiple comparisons (family-wise error rate). Physicians’ overall CL, as measured by the raw NASA-TLX, was not significantly correlated with computer use. Exploratory qualitative data analysis found both verbal and nonverbal signs of stress/CL due to computer use while interacting with the SPs. The proportion of time displaying nonverbal signs of stress/CL was calculated for a subset of participants (6 resident physicians and 3 staff physicians). Participant interpretations of instructions to completely document the encounter using the EHR varied widely. It is likely that participants’ usual style of documenting, either primarily during or after patient encounters, impacted their use of the EHR while SPs were present.

CONCLUSIONS: Use of the computer during video telemedicine appointments may negatively impact physician PC and overall clinical performance. Exploratory qualitative coding identified both verbal and nonverbal signs of stress/CL when participants interacted with the computer and the patient simultaneously. Increased clinical experience helped to mitigate the negative impact of computer use. If the use of the EHR is included in physician certification examinations, clear instructions regarding which tasks must be completed in the EHR during interactions with SPs should be provided.

PMID:42155120 | DOI:10.2196/84916

JMIR Med Educ. 2026 May 19;12:e84942. doi: 10.2196/84942.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) tools have revolutionized various aspects of education and health care in recent years. Their influence extends across multiple domains of medical education, from traditional learning to research and foreign language acquisition.

OBJECTIVE: This study aims to evaluate the experiences and perceptions of AI tools usage in a low-resource setting and identify the factors influencing their adoption.

METHODS: A cross-sectional study was conducted to evaluate the experiences with AI tools and perceptions regarding their future applications in education and health care among medical students in Syria. The sample was equally divided between clinical-year students and graduates. Chi-square tests analyzed differences based on demographics and experience, while Mann-Whitney U tests compared group perceptions of AI’s future role. Factors studied included academic year, gender, German language learning, computer access, and research experience.

RESULTS: Among 400 participants, AI tools were widely used for study preparation (228/400, 57% of participants), assignments (160/400, 40% of participants), and research. Clinical students used AI more than graduates for examination preparation (P<.001), creating cases (P=.03), and writing tasks (P<.001). Males used AI more for research (P=.004) or anatomy (P=.02); German learners relied on AI for language tasks. Despite 76% (304/400) of students believing AI would enhance residency training and 71.8% (287/400) of students supporting institutional policies, only 25.5% (102/400) of students expected career benefits. Ethical concerns were higher among females and researchers.

CONCLUSIONS: This study highlights the increasing reliance on AI tools among medical students and graduates for academic and clinical purposes. The highest usage was reported in study preparation, writing tasks, and clinical simulations. Significant differences in AI usage were observed based on academic level, gender, access to technology, and research experience. While perceptions were largely positive, concerns remained around ethical use, potential job displacement, and diminished human interaction in medicine. These findings underscore the importance of developing institutional policies to guide the ethical and effective integration of AI in medical education.

PMID:42155108 | DOI:10.2196/84942

JMIR Form Res. 2026 May 19;10:e91021. doi: 10.2196/91021.

ABSTRACT

BACKGROUND: Clinical decision-making training in operative dentistry commonly relies on real or standardized patients to develop undergraduate students’ ability to deliver safe, effective, and patient-centered care. However, training with real or standardized patients can be limited in scalability, cost-effectiveness, and accessibility. Large language models, with their human-like language capabilities, may have the potential to simulate patients in clinical encounters and help overcome some limitations associated with traditional training approaches.

OBJECTIVE: This study aimed to evaluate the feasibility of using large language model-based standardized virtual patients to support undergraduate dental students’ clinical decision-making training in operative dentistry.

METHODS: This mixed methods cross-sectional feasibility study was conducted during a simulation-based clinical decision-making training session in the Operative Dentistry and Cariology course at the College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia. Eligible participants were second-year undergraduate dental students enrolled in the course. A convenience sampling approach was used, with all eligible students (N=50) invited to participate. A total of 41 students completed the study, 23 (56%) of whom were male. The students were divided into 8 groups. Each group interacted with 2 standardized virtual patients powered by ChatGPT-4o (OpenAI) through the Chatbase platform to complete comprehensive history-taking and then reviewed the standardized virtual patients’ intraoral photographs and bitewing radiographs. For each standardized virtual patient, students as a group recorded diagnoses, performed a risk assessment, and formulated a treatment plan. Students then completed the Student Satisfaction and Self-Confidence in Learning questionnaire. The quality of the standardized virtual patient responses and overall dialogue realism were evaluated using the Dialogue Authenticity Scale. The dialogues were also thematically analyzed to identify authenticity-undermining response features and explore their context and underlying causes.

RESULTS: Students perceived the simulation-based training session positively, with all questionnaire items showing high median scores (4.00-5.00 on a 5-point scale), and both item-level IQRs and 95% CIs spanning no more than 1.0 scale point. In addition, standardized virtual patient responses were largely authentic, with an overall median authenticity rating of 4.50 (IQR 4.00-5.00; 95% CI 4.00-5.00) on a 6-point scale across all interactions. However, several authenticity-undermining response features were identified, including responses that were inconsistent with typical human behavior, contained information beyond a patient’s likely knowledge, or were factually incorrect.

CONCLUSIONS: This proof-of-concept study supports the feasibility of implementing large language model-based standardized virtual patients in undergraduate simulation-based clinical decision-making training in operative dentistry. In a dental context where this application has been only minimally evaluated, this study provides early evidence of positive student perceptions, acceptability, and largely authentic dialogue, while also identifying important performance limitations. Further research is warranted to optimize performance and to evaluate the educational effectiveness of this approach in improving undergraduate students’ clinical skills and knowledge.

PMID:42155101 | DOI:10.2196/91021

JMIR Form Res. 2026 May 19;10:e86025. doi: 10.2196/86025.

ABSTRACT

BACKGROUND: Growing evidence suggests that disruptions in rest-activity rhythms may serve as relevant markers of posttraumatic stress disorder (PTSD). Despite the emergence of machine learning methods applied to actigraphy and self-report data, few studies have used these approaches to identify individuals with clinically diagnosed PTSD. Prior work has focused on predicting probable PTSD based on self-report measures, yet discrepancies exist between clinical diagnoses and probable PTSD derived from self-reports.

OBJECTIVE: This study explored whether wrist actigraphy and sleep logs could be used to accurately predict clinician-rated PTSD diagnosis and probable diagnosis of PTSD based on established self-report cutoffs (PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [PCL-5] ≥31 and ≥38) among trauma-exposed service members and veterans. We also explored which features were most strongly predictive of each outcome and whether models were able to predict PTSD diagnosis even when accounting for other mental health disorders.

METHODS: Wrist actigraphy data and daily sleep logs were collected over 1 week from trauma-exposed male service members and veterans (N=36; mean age 41, SD 5.3 y). Candidate features were identified using univariate feature selection. Extreme gradient boosting models were trained using leave-one-subject-out cross-validation to predict the diagnosis of PTSD and probable diagnosis of PTSD based on 2 self-report cutoffs (PCL-5≥31 and ≥38). Performance metrics were then calculated at the person level. Linear regression was used to assess the discriminant validity of model-predicted scores and each PTSD outcome specifically, relative to other mental health diagnoses.

RESULTS: Machine learning models predicting PTSD diagnosis and probable PTSD based on the PCL-5≥31 threshold demonstrated satisfactory performance in this sample. The diagnosis model achieved an area under the curve (AUC) of 0.83 (95% CI 0.61-1.00), with high accuracy (88%) and specificity (96%) and moderate sensitivity (63%). The PCL-5≥31 model yielded comparable performance (AUC=0.84, 95% CI 0.71-0.98) with balanced sensitivity (73%) and specificity (82%). For both models, a combination of subjective and objective features was the most impactful. These models were able to predict PTSD even when accounting for non-PTSD mental health diagnoses, as model-predicted scores were significantly associated with 2 outcomes: clinician-rated PTSD (B=0.19; P=.002) and probable PTSD based on a PCL-5≥31 cutoff (B=0.24; P=.003). In contrast, the model predicting probable PTSD based on the PCL-5≥38 threshold performed poorly (AUC=0.47, 95% CI 0.24-0.69), with a nonsignificant relationship between model-predicted scores and the outcome (B<0.01; P=.89).

CONCLUSIONS: Both subjective and objective rest-activity features may improve the prediction of PTSD. Further research is needed to validate these findings and explore the use of integrating wearable sensor data and subjective information to support PTSD assessment.

PMID:42155096 | DOI:10.2196/86025

JMIR Form Res. 2026 May 19;10:e89306. doi: 10.2196/89306.

ABSTRACT

BACKGROUND: Pediatric emergency departments see a high volume of patients. Given that children often cannot describe their condition and there is a shortage of nursing staff, it is essential to identify the early warning signs of adverse conditions among children as quickly as possible. Current targeted care needs to be improved.

OBJECTIVE: This study aimed to investigate the application of an artificial intelligence (AI)-based version of the Pediatric Early Warning Score (PEWS) in a pediatric emergency observation unit, analyze the relationship between PEWS and disease severity, and assess its impact on the length of hospital stay and hospitalization costs after admission, thereby providing a reference for targeted nursing care.

METHODS: We performed a retrospective study. A total of 1233 pediatric patients admitted via the pediatric emergency department of a tertiary specialty hospital in Guangzhou from September 2023 to March 2024 were included. The patients were divided according to whether they triggered a PEWS early warning into an early warning group (PEWS ≥1) and a non-early warning group (PEWS=0) during emergency observation. Length of stay and hospitalization costs were compared between the early warning group and the non-early warning group. Differences between groups were assessed using the Mann-Whitney U test. We performed multivariable logistic regression to discuss the association of resource use metrics and PEWS status, adjusted by age, sex, and disease category (respiratory, neurological, and hematologic).

RESULTS: Of 1233 patients, 597 (48.4%) triggered the PEWS early warning (mean score 2.44, SD 1.41), and 636 (51.6%) did not. In the early warning group, 68 children were transferred to the intensive care unit, with a mean PEWS of 3.32 (SD 1.73). Compared with the non-early warning group, the early warning group had a longer hospital stay (z=-5.180; P<.001) and higher hospitalization costs (z=-6.500; P<.001), and the differences between groups were statistically significant (P<.001). Among the top 3 admission categories-respiratory, neurological, and hematologic diseases-children in the early warning group had significantly longer hospital stays and higher hospitalization costs (all P<.01). The β coefficient for length of hospital stay was 0.053 (SE 0.010; Wald χ²1=5.533; odds ratio 1.055, 95% CI 1.035-1.075), while the β coefficient for hospitalization costs was 0.001 (SE 0.000; Wald χ²1=6.075; odds ratio 1.001, 95% CI 1.001-1.001).

CONCLUSIONS: Compared with the non-early warning group, the early warning group had significantly longer hospital stays and higher hospitalization costs (P<.001); similar patterns were observed within respiratory, neurological, and hematologic disease categories (all P<.01). These findings show differences between children who triggered the warning and children who did not, providing a reference for identifying critically ill children for targeted care.

PMID:42155091 | DOI:10.2196/89306

JMIR Public Health Surveill. 2026 May 19;12:e88473. doi: 10.2196/88473.

ABSTRACT

BACKGROUND: Rural US communities experience disproportionately high rates of visual disability yet have limited access to ophthalmologists. Teleophthalmology may help address these gaps, but its effectiveness depends on broadband connectivity. The relationship between broadband access and ophthalmologist density has not been well characterized.

OBJECTIVE: The aim of this study is to quantify the association between household broadband access-defined as subscription rates or connection prevalence-and county-level ophthalmologist density and to identify sociodemographic predictors of access.

METHODS: We conducted an ecological study of all 3141 US counties using 2019 data from the American Community Survey, Area Health Resources File, and National Center for Health Statistics (NCHS). Broadband access was the primary exposure; ophthalmologist count with county population as an offset was the outcome. The primary analysis used negative binomial regression, adjusting for urbanicity, income, education, age, sex, race/ethnicity, unemployment, and insurance status. Sensitivity analyses included population-weighted linear regression and state fixed effects models. County-level heatmaps illustrated geographic patterns.

RESULTS: Median household broadband access was 56.6%, ranging from 72.2% in the most urban counties (NCHS category 1) to 49.1% in the most rural (NCHS category 6). In unadjusted negative binomial regression, each 10-percentage-point increase in broadband access was associated with a 68% higher ophthalmologist rate (incidence rate ratio=1.68, 95% CI 1.61-1.76; P<.001). After adjustment, each 10-percentage-point increase was associated with a 46% higher rate (incidence rate ratio=1.46, 95% CI 1.37-1.56; P<.001). Sensitivity analyses were consistent with primary analysis. Regions with both low broadband access and zero ophthalmologist density were concentrated in the South, Mountain West region, and Alaska.

CONCLUSIONS: Broadband access is strongly associated with ophthalmologist availability across US counties, independent of sociodemographic factors. Areas lacking ophthalmologists also tend to lack broadband adoption, creating compounded barriers to both in-person and teleophthalmic care. Efforts to expand broadband may support more equitable access to vision services in underserved regions.

PMID:42155086 | DOI:10.2196/88473

Adv Skin Wound Care. 2026 May 18. doi: 10.1097/ASW.0000000000000465. Online ahead of print.

ABSTRACT

OBJECTIVE: The Canadian Wound Program Information, Services, and Evaluation (CAN-WISE) questionnaire was developed to assess and evaluate acute wound care programs across Canada, addressing variations in service accessibility and program effectiveness.

METHODS: An expert panel of 9 acute care wound management program leads reviewed and assessed the CAN-WISE questionnaire. Content Validity Ratios (CVR), Scale Content Validity Index (S-CVI), Modified Kappa, Gwet’s AC1, and Brennan-Prediger tests were utilized to evaluate the relevance and reliability of both questions and responses. Percentage agreement metrics were also used to assess the clarity and vocabulary of the questions and responses.

RESULTS: The results of this validation study demonstrated high consensus among the expert panel for the final questionnaire, with question CVRs ranging from 0.777 to 1, elevated I-CVI scores of 0.888 to 1, and high, statistically relevant inter-rater reliability scores (AC1=0.966, Brennan-Prediger=0.934), demonstrating that the questions included were both essential and well constructed. Questionnaire responses demonstrated similarly high CVR scores (0.777 to 1), I-CVI scores (0.888 to 1), and statistically relevant reliability (AC1=0.988, Brennan-Prediger=0.976). Clarity and vocabulary analysis showed high absolute agreement levels of 95.88% and 98.96%, respectively. These reliability metrics were further supported by high, statistically relevant Gwet’s AC1 and Brennan-Prediger test results for clarity and vocabulary, confirming the tool’s comprehensibility.

CONCLUSIONS: The CAN-WISE questionnaire has demonstrated validity and reliability in its ability to evaluate acute care wound management programs in Canada. It provides a valuable tool for assessing program strengths and areas for improvement to improve acute wound care programs throughout the nation.

PMID:42155084 | DOI:10.1097/ASW.0000000000000465

Arch Endocrinol Metab. 2026 Aug 1;70(4). doi: 10.20945/2359-4292-2026-0047.

ABSTRACT

OBJECTIVE: To assess the relationship between night work, metabolic syndrome (MS) prevalence, and dietary patterns in healthcare workers at a large hospital in southern Brazil.

SUBJECTS AND METHODS: A cross-sectional study was conducted with 156 healthcare workers (90 day-shift and 66 night-shift) from July 2023 to March 2024. Sociodemographic and occupational, sleep, dietary patterns, meal timing, anthropometric data, blood pressure, and lab test data were collected.

RESULTS: Night-shift workers had higher blood pressure, lower HDL cholesterol, and 135% greater likelihood of developing MS than those who worked during the day. They consumed more fats and less fiber. Chrononutrition analysis showed night workers had later last meals (p < 0.001), longer intervals between first and last meals (p < 0.001), and shorter night fasting (p < 0.001). Ultra-processed food consumption was similar across shifts. A shorter interval between the first and last meal in night workers was linked to a 7% lower risk of MS. Findings suggest an association between night work and higher MS prevalence, with hypertension, abdominal obesity, unfavorable lipid profile, and disrupted eating timing. Circadian rhythm disruption and misaligned eating patterns, particularly prolonged eating windows and reduced nocturnal fasting, may contribute to the increased metabolic risk.

CONCLUSION: Interventions targeting diet and chrononutrition are essential. Occupational health programs should address the specific challenges of night work.

PMID:42155078 | DOI:10.20945/2359-4292-2026-0047

J Strength Cond Res. 2026 May 20. doi: 10.1519/JSC.0000000000005468. Online ahead of print.

ABSTRACT

Aguila Camacho, A, Ochoa Ahmed, F, Rowlett, M, Ríos-Gallardo, PT, Cabarkapa, DV, Cabarkapa, D, and Montalvo, S. Longitudinal changes in countermovement jump and isometric mid-thigh pull metrics and their relationship with on-court basketball performance in elite female collegiate basketball players. J Strength Cond Res XX(X): 000-000, 2026-To quantify within-subject relationships among countermovement jump (CMJ) and isometric mid-thigh pull (IMTP) variables, describe their temporal behavior across a competitive season, and examine their association with game performance in female collegiate basketball. Eighteen athletes (21.6 ± 1.9 years; 69.9 ± 9.1 kg; 171.6 ± 8.2 cm) completed CMJ and IMTP testing for 33 weeks. Within-subject (demeaned) Spearman correlation matrices were used to identify functional clusters. Representative CMJ (jump height, peak power, contraction time, reactive strength index(mod) and IMTP (peak force, impulse 100 ms, rate of force development [RFD] 50 ms) variables were modeled longitudinally with linear mixed-effects models (random intercepts; autoregressive (1) for a week). Game outcomes from 24 contests were linked to the nearest prior test and evaluated with bivariate Spearman correlations and leave-one-out cross-validated (LOOCV) linear models. Countermovement jump variables clustered into (a) force-production measures (e.g., impulses/peak power; moderate-high positive associations), (b) performance outcomes (jump height with relative power), and (c) temporal measures (contraction/phase durations). Isometric mid-thigh pull variables formed tight early impulse and RFD clusters, with peak-force measures grouping separately. Cross-test associations were generally small (|ρ| ≤ ∼0.30), indicating complementary constructs. Mixed-model marginal means showed modest, nonuniform seasonal trends-modified reactive strength index (mRSI) tended to increase and contraction time to decrease late season, while IMTP peak force exhibited a mid-season rise-amid substantial within-player variability. For game performance, only mRSI showed a statistically significant positive correlation with point differential (ρ = 0.46, p = 0.024); other variables were small and nonsignificant. Single-variable LOOCV models explained little variance in point differential (R2_cv ≤ 0.04). Countermovement jump and IMTP capture largely independent neuromuscular qualities and exhibit modest, test-specific seasonal changes. Although mRSI relates positively to game point differential, single neuromuscular metrics showed limited standalone predictive value, supporting their use primarily for monitoring readiness in conjunction with contextual factors rather than for game-to-game prediction.

PMID:42155075 | DOI:10.1519/JSC.0000000000005468

JCO Oncol Pract. 2026 May 19:OP2501275. doi: 10.1200/OP-25-01275. Online ahead of print.

ABSTRACT

PURPOSE: Over 70% of cancer survivors also have chronic conditions requiring coordinated care throughout survivorship. Few intervention studies have focused on improving care coordination between oncology and primary care during active treatment, specifically among under- and uninsured survivors. This study evaluated the effectiveness of Project CONNECT, a system-level intervention using an EHR-based registry and an oncology nurse coordinator, in improving patient-reported care coordination among breast and colorectal cancer survivors with at least one chronic condition seen in a large, urban, safety-net health system.

METHODS: Using a pre-post quasi-experimental design, 294 patients diagnosed with stage I to III breast or colorectal cancer and ≥1 chronic condition were administered a telephone survey before intervention and 6 and 12 months after intervention to measure patient-reported care coordination. Summary statistics described patient characteristics, and generalized estimating equation assessed adjusted population-average changes in care coordination.

RESULTS: The mean age of eligible patients was 56 years (standard deviation = 9.54). The majority of the survivors were women (78.6%). Race/ethnicity distribution of the sample represented the patient population of the safety-net health system with 45% White, 34% Black, and 51% Hispanic. Nearly 80% had ≥1 chronic conditions. After the intervention, patient-reported care coordination scores demonstrated significant improvement (β = -.05, P = .016). Notably, the proportion of survivors reporting never or rarely receiving confusing or differing information about their health or treatments decreased by 11% after the intervention.

CONCLUSION: An EHR-based registry of cancer survivors with chronic conditions supported by an oncology nurse coordinator assisting survivors to establish or maintain primary care during active cancer treatment is a promising strategy to bridge the transition between oncology and primary care for cancer survivors receiving care in a safety-net heath system.

PMID:42155068 | DOI:10.1200/OP-25-01275