Tag: nevin manimala

JMIR Hum Factors. 2025 May 30;12:e67884. doi: 10.2196/67884.

ABSTRACT

BACKGROUND: The transfer of patients between hospitals, that is, interhospital transfer (IHT), introduces discontinuity of care, including gaps in health information transfer, which may worsen patient outcomes.

OBJECTIVE: This is the first phase of a 5-year research study. Our goals are (1) to understand the gaps in health information exchange (HIE) and the clinician experience in accessing and using the electronic health record (EHR) during IHT and (2) to identify clinician user requirements for the development of an internal EHR solution for IHT.

METHODS: We used prior work on HIE during IHT, coupled with a user-centered design (UCD) process to engage in discussions with clinical users and gather input on EHR workflow during IHT patient admission and planning. A total of 8 UCD sessions were held between February and July 2023, involving 18 clinicians who interact with the EHR during IHT, including 3 medicine residents, 10 advanced practice providers (APPs), and 5 direct care attendings-all responsible for caring for IHT patients at Brigham and Women’s Hospital Cardiology, Medicine, Oncology, and intensive care unit services. Discussions highlighted facilitators and barriers and suggested improvements for data access and availability at the time of transfer. UCD sessions were recorded, analyzed, and coded by 2 independent reviewers to identify common themes driving suboptimal HIE. User requirements were derived from the sessions with users and iteratively refined throughout the process.

RESULTS: Qualitative analysis revealed that a significant number of frontline clinicians experience suboptimal availability of clinical information in the EHR at the time of IHT, including gaps in communication, incomplete data, and inefficient access to clinical data. User requirements emerged from these themes and primarily focused on information prioritization, data accessibility, and workflow and efficiency.

CONCLUSIONS: Notable levels of missing information and inefficient access to clinical data were reported by end users caring for IHT patients at the time of transfer. Conducting user research to understand the current process of IHT, involving users in conceptual design and information architecture, and generating prototypes for feedback from users can aid in designing a solution that meets user needs. The results of these early UCD activities will be used to develop and implement a data platform to support clinicians during IHT.

PMID:40446323 | DOI:10.2196/67884

JMIR Form Res. 2025 May 30;9:e65559. doi: 10.2196/65559.

ABSTRACT

BACKGROUND: In the field of mental health care, the incorporation of wearable devices into routine clinical practice continues to face significant challenges, despite the presence of supporting scientific evidence. Crossing the wasteland between the trial world and the real world is full of obstacles that often only become apparent during the implementation process.

OBJECTIVE: The objective of this paper was to evaluate the feasibility of using wearables in real-world clinical settings for children with severe developmental problems to help understand and manage disruptive behavior and to gain insights for the development of forthcoming implementation strategies.

METHODS: A mixed methods design was used to examine two different aspects of the use of wearables in a clinical setting. The first quantitative part of this study focuses on the feasibility of using wearables to collect reliable data on psychophysiological measures during daily activities in children at a children’s psychiatric center. The second qualitative part focuses on the evaluation of the implementation process using the Consolidated Framework for Implementation Research (CFIR) to identify essential steps to successfully incorporate wearable technology in clinical care for children with severe behavioral problems. Empatica E4 wristbands collected data on children’s psychophysiological arousal (eg, heart rate [HR] and skin conductance level [SCL]). Staff reported aggressive behavior and daily activities. Data were processed and visualized in a dashboard. User experiences were assessed through interviews with clinical staff. The implementation process was evaluated using the CFIR.

RESULTS: A total of 30 children (27 boys and 3 girls, aged 6 to 14 y; mean age 9.3 y, SD 1.95) wore the wearable for 5 consecutive days. As expected, the children found it easy to wear the device and the clinical staff predominantly expressed positive attitudes toward its use. The data collection proceeded relatively smoothly, and the collected data were of sufficient quality. In total, 315 observations of aggressive behavior were reported, including 54 red incidents (from 18 unique participants) and 261 orange incidents (from 26 unique participants). An exploratory analysis on the association between psychophysiological measures and aggressive behavior revealed that children’s HR was significantly higher during aggressive incidents compared to nonaggressive incidents (P=.007). Although not statistically significant, there was a trend suggesting higher peaks per minute during aggressive incidents (P=.07). No significant differences between aggressive and nonaggressive incidents were found for SCL and movement (P=.33 and P=.60). The most challenging CFIR domains in our study were the “characteristics of the intervention” and “the inner setting,” reflected in the fact that that the majority of implementation activities were focused on these two domains.

CONCLUSIONS: The use of wearables in a real-world study setting is considered feasible and valuable. However, for broader scaling in daily clinical practice, coherent actions on different domains of implementation are required.

PMID:40446322 | DOI:10.2196/65559

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