Tag: pubmed

JMIR Med Inform. 2025 May 30;13:e58125. doi: 10.2196/58125.

ABSTRACT

BACKGROUND: Digital health is a critical driver of quality, safety, and efficiency in health care. However, poor quality of clinical information in digital health technologies (DHTs) can compromise the quality and safety of care. The Clinical Information Quality (CLIQ) framework was developed, based on a systemic review of literature and an international eDelphi study, as a tool to assess the quality of clinical information in DHTs.

OBJECTIVES: The aim of this study is to assess the applicability, internal consistency, and construct validity of the CLIQ framework.

METHODS: This study was conducted as a cross-sectional survey of health care professionals across the United Kingdom who regularly use SystmOne electronic health records. Participants were invited through emails and social media platforms. The CLIQ questionnaire was administered as a web-based survey. Spearman correlation coefficients were computed to investigate the linear relationship between the dimensions in the CLIQ framework. The Cronbach α coefficients were computed to assess the internal consistency of the global scale (ie, CLIQ framework) and the subscales (ie, the informativeness, availability, and usability categories). Confirmatory factor analysis was used to assess the extent to which the survey data supported the construct validity of the CLIQ framework.

RESULTS: A total of 109 health care professionals completed the survey, of which two-thirds (67, 61.5%) were doctors and a quarter (26, 23.9%) were nurses or advanced nurse practitioners. Overall, the CLIQ dimensions had good quality scores except for portability, which had a modest score. The inter-item correlations were all positive and not likely due to chance. The Cronbach α coefficient for the overall CLIQ framework was 0.89 (95% CI 0.85-0.92). The confirmatory factor analysis provided a modest support for the construct validity of the CLIQ framework with the comparative fit index of 0.86 and standardized root mean square residual of 0.08.

CONCLUSIONS: The CLIQ framework demonstrated a high reliability and a modest construct validity. The CLIQ framework offers a pragmatic approach to assessing the quality of clinical information in DHTs and could be applied as part of information quality assurance systems in health care settings to improve quality of health information.

PMID:40446314 | DOI:10.2196/58125

JMIR Hum Factors. 2025 May 30;12:e59558. doi: 10.2196/59558.

ABSTRACT

BACKGROUND: Abnormal brain activity is the source of epileptic seizures, which can present a variety of symptoms and influence patients’ quality of life. Therefore, it is critical to track epileptic seizures, diagnose them, and provide potential therapies to manage people with epilepsy. Electroencephalography (EEG) is helpful in the diagnosis and classification of the seizure type, epilepsy, or epilepsy syndrome. Ictal EEG is rarely recorded, whereas interictal EEG is more often recorded, and the results can be abnormal or normal even in the case of epilepsy. The current digital care pathway for epilepsy (DCPE) lacks the integration of data-driven seizure detection, which could potentially enhance epilepsy treatment and management.

OBJECTIVE: This study aimed to determine the requirements for integrating data-driven medical software into the DCPE to meet the project’s goals and demonstrate practical feasibility regarding resource availability, time constraints, and technological capabilities. This adjustment emphasized ensuring that the proposed system is realistic and achievable. Perspectives on the feasibility of data-driven medical software that meets the project’s goals and demonstrates practical feasibility regarding resource availability, time constraints, and technological capabilities are presented.

METHODS: A 4-round Delphi study using focus group discussions was conducted with 7 diverse panels of experts from Oulu University Hospital to address the research questions and evaluate the feasibility of data-driven medical software for monitoring individuals with epilepsy. This collaborative approach fostered a thorough understanding of the topic and considered the perspectives of various stakeholders. In addition, a qualitative study was carried out using semistructured interviews.

RESULTS: Drawing from the findings of the thematic analytics, a detailed set of guidelines was created to facilitate the seamless integration of the proposed data-driven medical software for EEG seizure monitoring into the DCPE. These guidelines encompass system requirements, data collection and analysis, and user training, offering a comprehensive road map for the effective implementation of the software.

CONCLUSIONS: The study outcome presents a comprehensive strategy for improving the quality of care, providing personalized solutions, managing health care resources, and using artificial intelligence and sensor technology in clinical settings. The potential of artificial intelligence and sensor technology to revolutionize health care is exciting. The study identified practical strategies, such as real-time EEG seizure monitoring, predictive modeling for seizure occurrence, and data-driven analytics integration to enhance decision-making. These strategies were aimed at reducing diagnostic delays and providing personalized care. We are actively working on integrating these features into clinical workflows. However, further case studies and pilot implementations are planned for future studies. The results of this study will guide system developers in the meticulous design and development of systems that meet user needs in the DCPE.

PMID:40446306 | DOI:10.2196/59558

JMIR Res Protoc. 2025 May 30;14:e69577. doi: 10.2196/69577.

ABSTRACT

BACKGROUND: The COVID-19 pandemic has had disproportionate economic and health impacts on self-employed workers in Nigeria, particularly self-employed women and youth. Though uniquely different, the COVID-19 pandemic shares similarities with events such as childbirth, family, and health emergencies. Self-employed young women lack adequate support structures to cope with disruptive life events, which have negative consequences for their well-being. This is concerning, as 86% of women in the Nigerian labor force are self-employed.

OBJECTIVE: The project’s first objective is to conduct a gendered situational analysis to address the question of how the COVID-19 pandemic and other life events affect the paid and unpaid work and the physical, mental, and social well-being of self-employed young women in Nigeria; their strategies for coping with such events; and how their experiences compare with those of self-employed young men. Informed by this analysis, the second objective is to coproduce and pilot-test a gender-transformative intervention that integrates social protection and promotes well-being.

METHODS: This multimethod project has 3 components. The first is a situational analysis of the impact of the pandemic and other significant life events on the work and well-being of self-employed young women vis-à-vis self-employed young men. This involves qualitative interviews with approximately 60 self-employed young women and men and a digital storytelling initiative to represent some of these stories in video format. Secondary data analysis of the Nigerian General Household Survey and the COVID-19 Longitudinal Phone Survey will be conducted. Furthermore, a scoping review of the impact of significant life events, including the COVID-19 pandemic, on self-employed workers in low- and middle-income countries will be conducted. The second component is the coproduction of interventions involving qualitative interviews with self-employed young women, members of their support network, and policy makers to find out their views on how to support self-employed women. It also entails an analysis of policies relevant to self-employed women in Nigeria and theory of change workshops to create a map for achieving the long-term goal of improving their resilience. Furthermore, a systematic review of interventions to improve the job quality and well-being of self-employed workers will be conducted. The third component is a pilot of the coproduced interventions in a quasi-experimental study involving 300 participants to assess feasibility, acceptability, cost, and potential effectiveness.

RESULTS: This project was funded in October 2022. Data collection for the project commenced in May 2023 and will end in November 2025. Data collection for the situational analysis and coproduction of intervention phases have been completed while the pilot of intervention packages is underway.

CONCLUSIONS: This project will advance knowledge of the impact of the COVID-19 pandemic and other significant disruptive life events on the work and well-being of self-employed young Nigerian women and provide coproduced solutions to mitigate their effects.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/69577.

PMID:40446295 | DOI:10.2196/69577

JMIR Med Inform. 2025 May 30;13:e74940. doi: 10.2196/74940.

ABSTRACT

BACKGROUND: The development of sepsis in the intensive care unit (ICU) is rapid, the golden rescue time is short, and the effective way to reduce mortality is rapid diagnosis and early warning. Therefore, real-time prediction models play a key role in the clinical diagnosis and management of sepsis. However, the existing sepsis prediction models based on artificial intelligence still have limitations, such as poor real-time performance and insufficient interpretation.

OBJECTIVE: Our objective is to develop a real-time sepsis prediction model that integrates high timeliness and clinical interpretability. The model is designed to dynamically predict the risk of sepsis in ICU patients and establish a practical, tailored sepsis prediction platform.

METHODS: Within a retrospective analysis framework, the model comprises a real-time prediction module and an interpretability module. The real-time prediction module leverages 3-hour dynamic temporal features derived from 8 noninvasive, real-time physiological indicators: heart rate, respiratory rate, blood oxygen saturation, mean arterial pressure, systolic blood pressure, diastolic blood pressure, body temperature, and blood glucose. Three linear parameters (mean, SD, and endpoint value) were calculated to construct the prediction model using multiple ML algorithms. The interpretability module uses the TreeSHAP (Tree-Based Shapley Additive Explanations) method to enhance model transparency through both individual prediction and global explanations. Further, it added a link between the output interpretation of the explainable artificial intelligence method and its potential physiological or pathophysiological significance, including the relationship among the output interpretation and the patient’s hemodynamics, thermoregulatory response, and the balance between oxygen delivery and oxygen consumption. Finally, a web-based platform was developed to integrate prediction and interpretability functions.

RESULTS: The sepsis prediction model demonstrated robust performance in the test cohort (224 patients), achieving an accuracy of 0.7 (95% CI 0.68-0.71), precision of 0.69 (95% CI 0.68-0.71), F₁-score of 0.69 (95% CI 0.67-0.70), and area under the receiver operating characteristic curve of 0.76 (95% CI 0.74-0.77). The TreeSHAP method effectively visualized feature contributions, enabling clinicians to interpret the model’s prediction logic and identify anomalies. The link between the output interpretation of the model and its potential physiological or pathophysiological significance improved the interpretability and credibility of the explainable artificial intelligence method. The web-based platform significantly enhanced clinical utility by providing real-time risk assessment, statistical summaries, trend analysis, and actionable insights.

CONCLUSIONS: This platform provides real-time dynamic warnings for sepsis risk in critically ill ICU patients, supporting timely clinical decision-making.

PMID:40446292 | DOI:10.2196/74940

J Med Internet Res. 2025 May 30;27:e62696. doi: 10.2196/62696.

ABSTRACT

BACKGROUND: The 21st Century Cures Act mandated instant digital access for patients to see their test results and clinical notes (eg, via patient portals). Entirely using and understanding such health information requires some degree of personal health literacy.

OBJECTIVE: This study aims to assess the associations between ease of understanding online health information and various factors, including sociodemographics, health-related variables, numeracy, and technology-related factors.

METHODS: This cross-sectional study used data from the National Cancer Institute’s 2022 Health Information National Trends Survey (HINTS), a nationally representative survey of US adults that tracks individuals’ access and use of their health information. Data was collected from March to December 2022. The survey was conducted across various US settings using a stratified multistage sampling technique to ensure national representation. Our analysis included 3016 respondents with data for all variables of interest. We conducted bivariate and multivariate analyses to assess the odds of finding health information in online medical records or patient portals as “very easy” to understand compared with “not very easy.”

RESULTS: In the multivariate analysis, age group (with the 35-49 years group being 1.9 times more likely compared to the ≥75 years group; P=.03), female birth sex (1.4 times more likely; P=.04), ease of understanding medical statistics (8.5 times more likely for those finding it “very easy”; P<.001), patient-provider communication score (increase of 1.1 odds per 1 unit increase; P<.001), and mode of accessing online records (1.8 times more likely via an app and 1.4 times more likely via both an app and website, P=.01 and P=.003, respectively, versus using a website alone) were significant predictors for finding health information “very easy” to understand.

CONCLUSIONS: Sociodemographic factors, numeracy, patient-provider communication, and method of accessing online records were associated with ease of understanding health information in online medical records or patient portals. Findings from this study may inform interventions to make patient portals and online medical records more patient-centered and easier to navigate.

PMID:40446288 | DOI:10.2196/62696

Phys Rev Lett. 2025 May 16;134(19):191901. doi: 10.1103/PhysRevLett.134.191901.

ABSTRACT

Using (2712.4±14.3)×10^{6} ψ(3686) events collected with the BESIII detector at BEPCII, a partial wave analysis of the decay ψ(3686)→ϕηη^{‘} is performed with the covariant tensor approach. In addition to the established states h_{1}(1900) and ϕ(2170), an axial-vector state with a mass near 2.3 GeV/c^{2} is observed for the first time. Its mass and width are measured to be 2316±9_{stat}±30_{syst} MeV/c^{2} and 89±15_{stat}±26_{syst} MeV, respectively. The product branching fractions of B[ψ(3686)→X(2300)η^{‘}]B[X(2300)→ϕη] and B[ψ(3686)→X(2300)η]B[X(2300)→ϕη^{‘}] are determined to be (4.8±1.3_{stat}±0.7_{syst})×10^{-6} and (2.2±0.7_{stat}±0.7_{syst})×10^{-6}, respectively. The branching fraction B[ψ(3686)→ϕηη^{‘}] is measured for the first time to be (3.14±0.17_{stat}±0.24_{syst})×10^{-5}. The first uncertainties are statistical and the second are systematic.

PMID:40446269 | DOI:10.1103/PhysRevLett.134.191901

Phys Rev Lett. 2025 May 16;134(19):196302. doi: 10.1103/PhysRevLett.134.196302.

ABSTRACT

A novel localization phenomenon, termed erratic non-Hermitian skin localization, has been identified in disordered globally reciprocal non-Hermitian lattices. Unlike conventional non-Hermitian skin effect and Anderson localization, it features macroscopic eigenstate localization at irregular, disorder-dependent positions with subexponential decay. Using the Hatano-Nelson model with disordered imaginary gauge fields as a case study, this effect is linked to stochastic interfaces governed by the universal order statistics of random walks. Finite-size scaling analysis confirms the localized nature of the eigenstates. This discovery challenges conventional wave localization paradigms, offering new avenues for understanding and controlling localization phenomena in non-Hermitian physics.

PMID:40446237 | DOI:10.1103/PhysRevLett.134.196302

Phys Rev Lett. 2025 May 16;134(19):190602. doi: 10.1103/PhysRevLett.134.190602.

ABSTRACT

This Letter augments the recently introduced stabilizer tensor network (STN) protocol with magic state injection, reporting a new framework with significantly enhanced ability to simulate circuits with an extensive number of non-Clifford operations. Specifically, for random T-doped N-qubit Clifford circuits the computational cost of circuits prepared with magic state injection scales as O[poly(N)] when the circuit has t≲N T gates compared to an exponential scaling for the STN approach, which is demonstrated in systems of up to 200 qubits. In the case of the hidden bit shift circuit, a paradigmatic benchmarking system for extended stabilizer methods with a tunable amount of magic, we report that our magic state injected STN framework can efficiently simulate 4000 qubits and 320T gates. These findings provide a promising outlook for the use of this protocol in the classical modeling of quantum circuits that are conventionally difficult to simulate efficiently.

PMID:40446235 | DOI:10.1103/PhysRevLett.134.190602

JMIR Mhealth Uhealth. 2025 May 30;13:e58703. doi: 10.2196/58703.

ABSTRACT

BACKGROUND: Patients undergoing major surgery are at risk of complications and delayed recovery. Prehabilitation has shown promise in improving postoperative outcomes. Offering prehabilitation by means of mHealth can help overcome barriers to participating in prehabilitation and empower patients prior to major surgery. We developed the Be Prepared mHealth app, which has shown potential in an earlier pilot study.

OBJECTIVE: This study aims to evaluate the effectiveness of the Be Prepared app on postoperative recovery of physical functioning (PF) in patients undergoing major elective surgery.

METHODS: This study was a multicenter randomized controlled trial with 2 arms. Adults scheduled for major elective surgery were randomly assigned to the control (usual care) or intervention group (Be Prepared app in addition to usual care). The Be Prepared app is a smartphone app with pre- and postoperative information and instructions on changing risk behavior for patients undergoing major elective surgery. The primary outcome was recovery of postoperative PF up to 12 weeks after hospital discharge measured with the Computer Adaptive Test Patient-Reported Outcomes Measurement Information System-PF. Secondary outcomes included social participation, self-reported recovery, health-related quality of life, postoperative outcomes, and patient satisfaction. Measurements were performed at 5 time points: before random assignment and 1, 3, 6, and 12 weeks after hospital discharge.

RESULTS: A total of 369 patients were analyzed, 181 in the control group and 188 in the intervention group. The result of the linear mixed effects model showed a mean slope difference in recovery of PF over 12 weeks of 2.97 (95% CI 0.90-5.02) in favor of the intervention group. However, this effect was not clinically relevant and was negated by the significantly lower PF score 1 week after hospital discharge in the intervention group (mean difference -1.72, 95% CI -3.38 to -0.07). Most secondary outcome measures did not show significantly greater improvements in the intervention group compared to the control group. Patient satisfaction with overall perioperative care was significantly higher in the intervention group compared to the control group and satisfaction with the Be Prepared app was high.

CONCLUSIONS: The use of the Be Prepared app as a stand-alone intervention does not seem beneficial for improving postoperative recovery in patients undergoing major surgery. However, satisfaction with perioperative care was higher in patients using the app. Given the advantages of digital technology in health care, it can be considered a basis for prehabilitation care pathways, complemented by guidance from health care professionals as needed.

PMID:40446217 | DOI:10.2196/58703

Neurology. 2025 Jun 24;104(12):e213782. doi: 10.1212/WNL.0000000000213782. Epub 2025 May 30.

ABSTRACT

BACKGROUND AND OBJECTIVES: Stroke remains a major global public health concern, particularly in China, where particulate matter (PM_2.5) pollution is a significant contributor to stroke mortality. This study systematically analyzes changes in stroke and subtype-specific mortality attributable to PM_2.5 in China from 1990 to 2021 and projects trends up to 2030.

METHODS: Data were obtained from the Global Burden of Disease database. The mortality and standardized data of stroke and its subtypes attributable to PM_2.5 in China were collected. Long-term trends were assessed using the joinpoint regression model. The age-period-cohort (a-p-c) model was applied to evaluate the effects of age, period, and birth cohort on stroke mortality. In addition, the Bayesian age-period-cohort model was used to forecast age-standardized mortality rate (ASMR) trends through 2030.

RESULTS: From 1990 to 2021, the ASMR of stroke attributable to PM_2.5 in China showed a declining trend and was projected to decrease to 32.0 per 100,000 by 2030. However, significant differences were observed across stroke subtypes, age groups, and sexes. Subarachnoid hemorrhage (SAH) exhibited the largest decline while ischemic stroke (IS) had the smallest reduction. Local drift analysis showed that IS and intracerebral hemorrhage (ICH) declined fastest in those aged 45-60 years while SAH declined most in those aged 75-80 years. a-p-c model analysis demonstrated that stroke mortality increased with age, with IS mortality surpassing that of ICH in individuals aged 75 years and older and the gap widening with age. Stroke mortality risk declined over time, with younger cohorts showing greater reductions. The stroke burden remained higher in men than in women.

DISCUSSION: China has made significant progress in stroke prevention and air pollution control; however, disparities remain in the effectiveness of prevention across stroke subtypes and population groups. Further efforts should focus on strengthening pollution control, optimizing prevention strategies for each stroke subtype, enhancing hypertension management in middle-aged populations, improving metabolic risk control in older adults, and ensuring efficient health care resource allocation. Priority should be given to high-risk populations, particularly older individuals and men, to address the challenges posed by aging and the increasing burden of chronic diseases.

PMID:40446199 | DOI:10.1212/WNL.0000000000213782