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            "creatorSummary": "Oleribe et al.",
            "parsedDate": "2026-03-19",
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            "key": "8J74G8K3",
            "version": 27194,
            "itemType": "journalArticle",
            "title": "Healthcare Providers' Perspectives on Generative Artificial Intelligence (GenAI) Adoption, Adaptation, Assimilation, and Use in the United States.",
            "creators": [
                {
                    "creatorType": "author",
                    "firstName": "Obinna O.",
                    "lastName": "Oleribe"
                },
                {
                    "creatorType": "author",
                    "firstName": "Marissa",
                    "lastName": "Brash"
                },
                {
                    "creatorType": "author",
                    "firstName": "Adati",
                    "lastName": "Tarfa"
                },
                {
                    "creatorType": "author",
                    "firstName": "Ricardo",
                    "lastName": "Izurieta"
                },
                {
                    "creatorType": "author",
                    "firstName": "Simon D.",
                    "lastName": "Taylor-Robinson"
                }
            ],
            "abstractNote": "Background: Generative artificial intelligence (GenAI) is rapidly permeating healthcare; yet, U.S. clinicians still report mixed feelings about its  reliability, impact on workflow, and ethical implications. Current data on  provider sentiment are needed to guide safe, patient-centered AI implementation  in healthcare. Objective: This study aimed to assess U.S. healthcare providers'  perceptions of generative AI adoption, perceived usefulness, training needs,  barriers, and strategies for safe integration. Methods: A nationwide,  IRB-approved, cross-sectional survey was administered to healthcare professionals  using Qualtrics. A convenience sample of clinicians was recruited via  professional listservs and e-mail invitations. The 20-page questionnaire captured  demographics, GenAI exposure, organizational adoption status, perceived  usefulness (5-point scale), barriers, and mitigation strategies. SPSS v27 and  Microsoft Excel were used for statistical analysis. Results: Of 130 respondents,  109 completed the core survey (completion rate 83.8%). Participants were 38.5%  physicians, 16.5% nurses, 12.8% allied professionals, and 32.2% other providers;  54.2% were women, and 64.8% were ≥50 years. Overall, 86.9% agreed that GenAI is  useful in current patient care, rising to 92.9% when asked about future  usefulness. Only 42.4% had received formal GenAI training, and just 23.2%  reported that their organization had begun adopting AI. The top perceived  benefits were improved documentation/clerking (57.0%) and error reduction  (49.4%). Dominant barriers included limited AI knowledge (24.7%) and fear of job  loss (16.9%). Despite concerns, 72% expressed willingness to support broader  GenAI adoption, favoring human oversight (67.1%) and staff training (60.8%) as  key safeguards. There were statistically significant findings in perceived AI  usefulness by gender (χ(2) = 29.2; p < 0.001); organizational adoption of AI  (χ(2) = 31.6.2; p = 0.047) and where AI is most useful (χ(2) = 101.1; p < 0.001)  by qualifications; and support for AI adoption by age (χ(2) = 18.0; p = 0.02).  Conclusions: U.S. clinicians in our survey viewed GenAI as useful but reported  limited training and organizational infrastructure needed for confident use while  also expressing concerns regarding data privacy and ethical risk. Education  programs and transparent, provider-led implementation strategies may accelerate  responsible GenAI assimilation while addressing ethical and workforce concerns.  Also, health administrators should use the efficiency gains to improve  provider-patient relationships and clinicians' work-life balance while reducing  clinician burnout rates.",
            "publicationTitle": "Healthcare (Basel, Switzerland)",
            "publisher": "",
            "place": "Switzerland",
            "date": "2026 Mar 19",
            "volume": "14",
            "issue": "6",
            "section": "",
            "partNumber": "",
            "partTitle": "",
            "pages": "",
            "series": "",
            "seriesTitle": "",
            "seriesText": "",
            "journalAbbreviation": "Healthcare (Basel)",
            "DOI": "10.3390/healthcare14060775",
            "citationKey": "",
            "url": "",
            "accessDate": "",
            "PMID": "41897226",
            "PMCID": "PMC13027095",
            "ISSN": "2227-9032",
            "archive": "",
            "archiveLocation": "",
            "shortTitle": "",
            "language": "eng",
            "libraryCatalog": "",
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            "rights": "",
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            "tags": [
                {
                    "tag": "adoption and integration"
                },
                {
                    "tag": "challenges and opportunities"
                },
                {
                    "tag": "generative artificial intelligence"
                },
                {
                    "tag": "healthcare"
                },
                {
                    "tag": "provider perspectives"
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        "data": {
            "key": "JFPMW2F4",
            "version": 27193,
            "itemType": "journalArticle",
            "title": "Current State of Artificial Intelligence Adoption and Implementation in Neuroradiology Departments: Insights from a U.S. National Survey.",
            "creators": [
                {
                    "creatorType": "author",
                    "firstName": "Max",
                    "lastName": "Wintermark"
                },
                {
                    "creatorType": "author",
                    "firstName": "Jason W.",
                    "lastName": "Allen"
                },
                {
                    "creatorType": "author",
                    "firstName": "Rahul",
                    "lastName": "Bhala"
                },
                {
                    "creatorType": "author",
                    "firstName": "Colin",
                    "lastName": "Derdeyn"
                },
                {
                    "creatorType": "author",
                    "firstName": "Ajay",
                    "lastName": "Gupta"
                },
                {
                    "creatorType": "author",
                    "firstName": "Christopher P.",
                    "lastName": "Hess"
                },
                {
                    "creatorType": "author",
                    "firstName": "Joseph M.",
                    "lastName": "Hoxworth"
                },
                {
                    "creatorType": "author",
                    "firstName": "Thierry",
                    "lastName": "Huisman"
                },
                {
                    "creatorType": "author",
                    "firstName": "Mahesh V.",
                    "lastName": "Jayaraman"
                },
                {
                    "creatorType": "author",
                    "firstName": "Ajay",
                    "lastName": "Malhotra"
                },
                {
                    "creatorType": "author",
                    "firstName": "Alexander M.",
                    "lastName": "McKinney"
                },
                {
                    "creatorType": "author",
                    "firstName": "Mahmud",
                    "lastName": "Mossa-Basha"
                },
                {
                    "creatorType": "author",
                    "firstName": "Bruno",
                    "lastName": "Policeni"
                },
                {
                    "creatorType": "author",
                    "firstName": "Tina Young",
                    "lastName": "Poussaint"
                },
                {
                    "creatorType": "author",
                    "firstName": "Amit",
                    "lastName": "Saindane"
                },
                {
                    "creatorType": "author",
                    "firstName": "Achala",
                    "lastName": "Vagal"
                },
                {
                    "creatorType": "author",
                    "firstName": "Christopher",
                    "lastName": "Whitlow"
                }
            ],
            "abstractNote": "BACKGROUND AND PURPOSE: Artificial intelligence (AI) is rapidly transforming medical imaging, yet its integration into neuroradiology remains uneven. This  survey-based study assesses AI usage, tools, applications, barriers, and future  expectations among U.S. neuroradiology departments. MATERIALS AND METHODS: This  cross-sectional survey questionnaire comprised 19 items, blending  multiple-choice, multi-select, and open-ended formats. Descriptive statistics  were used to identify patterns in data. RESULTS: Most departments (81%) reported  AI use, primarily for stroke-related applications, with smaller numbers using  tools for report generation, segmentation, and image quality enhancement. Most  clinical tools were FDA-approved. AI had minimal perceived impact on workload,  and performance was viewed as variable, with concerns about accuracy and false  positives. Cost, integration challenges, and limited efficacy evidence were the  main barriers to adoption. Despite these limitations, most respondents  anticipated increased AI use over the next five years. CONCLUSIONS: Findings  underscore the need for clinician-vendor collaboration to realize AI's potential  in reducing workload and improving outcomes.",
            "publicationTitle": "AJNR. American journal of neuroradiology",
            "publisher": "",
            "place": "United States",
            "date": "2026 Mar 6",
            "volume": "",
            "issue": "",
            "section": "",
            "partNumber": "",
            "partTitle": "",
            "pages": "ajnr.A9279",
            "series": "",
            "seriesTitle": "",
            "seriesText": "",
            "journalAbbreviation": "AJNR Am J Neuroradiol",
            "DOI": "10.3174/ajnr.A9279",
            "citationKey": "",
            "url": "",
            "accessDate": "",
            "PMID": "41791836",
            "PMCID": "",
            "ISSN": "1936-959X 0195-6108",
            "archive": "",
            "archiveLocation": "",
            "shortTitle": "",
            "language": "eng",
            "libraryCatalog": "",
            "callNumber": "",
            "rights": "© 2026 by American Journal of Neuroradiology.",
            "extra": "",
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            "dateAdded": "2026-04-15T15:31:09Z",
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    {
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            "version": 27192,
            "itemType": "journalArticle",
            "title": "Artificial Intelligence Length-of-Stay Forecasting and Pediatric Surgical Capacity.",
            "creators": [
                {
                    "creatorType": "author",
                    "firstName": "Jay G.",
                    "lastName": "Berry"
                },
                {
                    "creatorType": "author",
                    "firstName": "Derek",
                    "lastName": "Mathieu"
                },
                {
                    "creatorType": "author",
                    "firstName": "Steven J.",
                    "lastName": "Staffa"
                },
                {
                    "creatorType": "author",
                    "firstName": "Ben Y.",
                    "lastName": "Reis"
                },
                {
                    "creatorType": "author",
                    "firstName": "Peter",
                    "lastName": "Hong"
                },
                {
                    "creatorType": "author",
                    "firstName": "Gabor",
                    "lastName": "Asztalos"
                },
                {
                    "creatorType": "author",
                    "firstName": "Lynne",
                    "lastName": "Ferrari"
                }
            ],
            "abstractNote": "IMPORTANCE: Hospitals are increasingly experiencing challenges with variable and unpredictable inpatient loads, including days with excessively high and  excessively low capacity for surgical patients. Artificial intelligence has the  potential to facilitate postoperative hospital bed management and stabilize  capacity. OBJECTIVES: To predict hospital length of stay (LOS) following elective  surgical procedures using machine learning methods, and to implement the LOS  prediction model in a perioperative clinical setting and evaluate its ability to  optimize elective surgical scheduling and hospital bed capacity. DESIGN, SETTING,  AND PARTICIPANTS: This preimplementation and postimplementation cohort study was  conducted at a tertiary, freestanding, US children's hospital among patients of  any age undergoing an elective surgical procedure requiring inpatient recovery.  For LOS prediction, a retrospective analysis was performed on elective surgical  cases from January 1, 2018, to March 31, 2022, using Extreme Gradient Boosting  (XGBoost) to predict postoperative LOS based on in-training and holdout datasets,  with hyperparameter tuning using 5-fold cross-validation. For implementation and  evaluation of the LOS prediction model, a preimplementation and  postimplementation analysis was performed from July 1, 2022, to April 30, 2024.  Data analysis was conducted from June 1 to October 31, 2024. EXPOSURES: Patients'  type of surgery, chronic conditions, and demographic characteristics. MAIN  OUTCOMES AND MEASURES: Postoperative LOS, day-to-day variance in bedded days for  elective surgical procedures, and days with excessively high capacity (>75th  percentile of historical elective surgical census) or excessively low capacity  (<25th percentile of historical elective surgical census). RESULTS: There were  21 352 elective surgical cases (mean [SD] age, 10.2 [7.4] years; 10 804 [50.6%]  female) for patients included in the retrospective analysis of postoperative LOS  prediction and 12 522 elective surgical cases in the pretest and posttest  analysis of the prediction model (premodel implementation, n = 5867; postmodel  implementation, n = 6655). The postoperative LOS model had 85.6% accuracy with a  1-night leniency. The model's mean absolute error was 0.6 days. After  implementation of the LOS model in elective surgery scheduling and hospital bed  capacity management, the median number of elective surgical procedures increased  by 5 (IQR, 4.5-5) for each weekday. Variation in postoperative bedded days across  days of the week decreased significantly. The magnitude of the IQR of bedded days  decreased the most during midweek: 43% and 44% reductions in the IQR occurred on  Wednesdays and Thursdays, respectively. The percentage of weekdays with underused  capacity (<84 patients) decreased from 33% to 10% (P < .001), without a  significant increase in days with excessive capacity. CONCLUSIONS AND RELEVANCE:  In this cohort study, use of a machine-learning, postoperative LOS model helped  to reduce day-to-day variation in the number of elective surgical procedures  performed, increase the total number of elective surgical procedures, and  decrease underuse of hospital beds.",
            "publicationTitle": "JAMA pediatrics",
            "publisher": "",
            "place": "United States",
            "date": "2026 Mar 1",
            "volume": "180",
            "issue": "3",
            "section": "",
            "partNumber": "",
            "partTitle": "",
            "pages": "306-313",
            "series": "",
            "seriesTitle": "",
            "seriesText": "",
            "journalAbbreviation": "JAMA Pediatr",
            "DOI": "10.1001/jamapediatrics.2025.5385",
            "citationKey": "",
            "url": "",
            "accessDate": "",
            "PMID": "41490015",
            "PMCID": "PMC12771386",
            "ISSN": "2168-6211 2168-6203",
            "archive": "",
            "archiveLocation": "",
            "shortTitle": "",
            "language": "eng",
            "libraryCatalog": "",
            "callNumber": "",
            "rights": "",
            "extra": "",
            "tags": [
                {
                    "tag": "*Artificial Intelligence"
                },
                {
                    "tag": "*Elective Surgical Procedures/statistics & numerical data"
                },
                {
                    "tag": "*Hospital Bed Capacity/statistics & numerical data"
                },
                {
                    "tag": "*Hospitals, Pediatric/statistics & numerical data"
                },
                {
                    "tag": "*Length of Stay/statistics & numerical data/trends"
                },
                {
                    "tag": "Adolescent"
                },
                {
                    "tag": "Child"
                },
                {
                    "tag": "Child, Preschool"
                },
                {
                    "tag": "Female"
                },
                {
                    "tag": "Forecasting"
                },
                {
                    "tag": "Humans"
                },
                {
                    "tag": "Infant"
                },
                {
                    "tag": "Machine Learning"
                },
                {
                    "tag": "Male"
                },
                {
                    "tag": "Retrospective Studies"
                }
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            "itemType": "journalArticle",
            "title": "A practical framework for operationalising responsible and equitable artificial intelligence in health care: tackling bias, inequity, and implementation  challenges.",
            "creators": [
                {
                    "creatorType": "author",
                    "firstName": "Mattea L.",
                    "lastName": "Welch"
                },
                {
                    "creatorType": "author",
                    "firstName": "Benjamin",
                    "lastName": "Grant"
                },
                {
                    "creatorType": "author",
                    "firstName": "Christopher",
                    "lastName": "Deutschman"
                },
                {
                    "creatorType": "author",
                    "firstName": "Clare",
                    "lastName": "McElcheran"
                },
                {
                    "creatorType": "author",
                    "firstName": "Adam",
                    "lastName": "Badzynski"
                },
                {
                    "creatorType": "author",
                    "firstName": "Jennifer A. H.",
                    "lastName": "Bell"
                },
                {
                    "creatorType": "author",
                    "firstName": "Andrew",
                    "lastName": "Hope"
                },
                {
                    "creatorType": "author",
                    "firstName": "Robert C.",
                    "lastName": "Grant"
                },
                {
                    "creatorType": "author",
                    "firstName": "Tran",
                    "lastName": "Truong"
                },
                {
                    "creatorType": "author",
                    "firstName": "Kelly",
                    "lastName": "Lane"
                },
                {
                    "creatorType": "author",
                    "firstName": "Patti",
                    "lastName": "Leake"
                },
                {
                    "creatorType": "author",
                    "firstName": "Divya",
                    "lastName": "Sharma"
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                {
                    "creatorType": "author",
                    "firstName": "Ian",
                    "lastName": "Stedman"
                },
                {
                    "creatorType": "author",
                    "firstName": "Mike",
                    "lastName": "Lovas"
                },
                {
                    "creatorType": "author",
                    "firstName": "Jeremy",
                    "lastName": "Petch"
                },
                {
                    "creatorType": "author",
                    "firstName": "Alejandro",
                    "lastName": "Berlin"
                },
                {
                    "creatorType": "author",
                    "firstName": "Benjamin",
                    "lastName": "Haibe-Kains"
                },
                {
                    "creatorType": "author",
                    "firstName": "James A.",
                    "lastName": "Anderson"
                }
            ],
            "abstractNote": "Artificial intelligence (AI) has the potential to transform health care; however, successful integration of AI into health care requires overcoming obstacles, such  as biases in data and AI models, and addressing challenges in generating  sufficient clinical evidence for deployment. In this Viewpoint, we present a  community-based, actionable framework for responsible and ethical development,  deployment, and integration of AI-based solutions in health care, emphasising  bias mitigation and clinical evidence generation. Our framework is intended for  all members of the health-care team who interact with AI-based solutions,  including software developers, data scientists, researchers, clinicians, hospital  administrators, and institutional ethics and regulatory teams. We critically  discuss the challenges associated with the use of such AI frameworks in health  care. The framework, informed by multidisciplinary expertise, consists of four  stages: (1) problem identification and study design, (2) model training and  development, (3) silent deployment and clinical evaluation, and (4) operational  deployment and lifecycle monitoring. This framework aligns with reporting  standards such as SPIRIT-AI, CONSORT-AI, and TRIPOD+AI, offering practical steps  for addressing biases, ensuring fairness, and validating clinical effectiveness.  The framework provides action-oriented guidelines that can be used by  institutions to support the ethical and efficient integration of AI into health  care and equitable patient outcomes, either directly or by tailoring the  guidelines with institution-specific resources.",
            "publicationTitle": "The Lancet. Digital health",
            "publisher": "",
            "place": "England",
            "date": "2026 Mar 20",
            "volume": "",
            "issue": "",
            "section": "",
            "partNumber": "",
            "partTitle": "",
            "pages": "100957",
            "series": "",
            "seriesTitle": "",
            "seriesText": "",
            "journalAbbreviation": "Lancet Digit Health",
            "DOI": "10.1016/j.landig.2025.100957",
            "citationKey": "",
            "url": "",
            "accessDate": "",
            "PMID": "41864789",
            "PMCID": "",
            "ISSN": "2589-7500",
            "archive": "",
            "archiveLocation": "",
            "shortTitle": "",
            "language": "eng",
            "libraryCatalog": "",
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            "rights": "Copyright © 2025 The Author(s). Published by Elsevier Ltd.. All rights reserved.",
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            "creators": [
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                    "creatorType": "author",
                    "firstName": "Qëndresa Rramani",
                    "lastName": "Dervishi"
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                {
                    "creatorType": "author",
                    "firstName": "Yannick",
                    "lastName": "Blum"
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                {
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                    "firstName": "Larissa",
                    "lastName": "Brust"
                },
                {
                    "creatorType": "author",
                    "firstName": "Matthias",
                    "lastName": "Weigl"
                }
            ],
            "abstractNote": "OBJECTIVE: Enhancing patient safety is a global priority and active involvement of patient and family advisory councils (PFACs) has been proposed as a key strategy in this endeavour. Preparing and supporting PFAC's activities in the field of patient safety is a crucial step in sustainably implementing patient engagement in healthcare organisations. However, to what extent and through which means remains unclear. Considering these shortcomings, we aimed to synthesise the current literature base, identify and map existing resources designed to educate and support PFACs in patient safety within healthcare organisations.\nMETHODS: We conducted a rapid scoping review. Drawing on predefined eligibility criteria, we reviewed peer-reviewed and grey literature on educational resources on patient-safety-related topics aimed at PFACs in healthcare organisations. We searched three databases (PubMed, Web of Science and Scopus) as well as websites of relevant stakeholders and institutions.\nRESULTS: Overall, we obtained 13 eligible sources. They featured resources ranging from reading materials to more intensive workshop sessions-combining active and passive learning approaches. Definition of key concepts and stakeholder roles were common topics in most sources. Yet, contents specific to patient safety showed considerable variability, likely due to context-specific approaches, local practices and lack of overall guidelines. Standardised resources tailored to PFACs' unique needs in patient safety were largely absent.\nCONCLUSION: This review highlights the limited availability of comprehensive, well-documented resources for PFACs. It further corroborates the need for systematic approaches to support patient engagement activities and meaningful involvement in patient safety.\nPRACTICE IMPLICATIONS: Our findings provide a first synthesis of the literature, inform future research as well as the development of respective patient engagement initiatives in patient safety. Moreover, they underscore the importance of developing standardised, yet adaptable resources to equip PFACs for their role in fostering safer healthcare systems.\nTRIAL REGISTRATION NUMBER: DRKS00034733.",
            "publicationTitle": "BMJ open quality",
            "publisher": "",
            "place": "",
            "date": "2026-03-31",
            "volume": "15",
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            "pages": "e004040",
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                    "lastName": "Finch"
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            "abstractNote": "BACKGROUND: Safety event reporting plays an important role in improving patient safety. The US Department of Veterans Affairs has focused on minimizing or eliminating harm to patients.\nOBSERVATIONS: The patient safety (PS) and high reliability organization (HRO) teams at the Central Texas Veterans Health Care System (CTVHCS) developed an education model that integrates the Joint Patient Safety Reporting System (JPSR) into huddles. JPSR Huddle Cards provide frontline staff and management with guidance on the purpose of the JPSR, the difference between an adverse event and a near miss, what root causes are, the lifespan of a JPSR, how to celebrate reporting, and key fact checks. Six cards were presented to staff (1 per week) during morning safety huddles to support the CTVHCS journey to high reliability. Participants completed a survey on safety before and after receiving the cards.\nCONCLUSIONS: Posttest scores were consistently higher than pretest scores, with an average increase of around 2 standard deviations across all questions.",
            "publicationTitle": "Federal practitioner: for the health care professionals of the VA, DoD, and PHS",
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            "pages": "336-340",
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            "seriesTitle": "",
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            "creators": [
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                    "creatorType": "author",
                    "firstName": "Ling",
                    "lastName": "Wang"
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                    "creatorType": "author",
                    "firstName": "Xizhao",
                    "lastName": "Li"
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                    "creatorType": "author",
                    "firstName": "Cailiang",
                    "lastName": "Qiu"
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                    "creatorType": "author",
                    "firstName": "Jianjian",
                    "lastName": "Wang"
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                    "creatorType": "author",
                    "firstName": "Lina",
                    "lastName": "Zeng"
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                    "creatorType": "author",
                    "firstName": "Ting",
                    "lastName": "Liu"
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                {
                    "creatorType": "author",
                    "firstName": "Ziqing",
                    "lastName": "Zhong"
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                {
                    "creatorType": "author",
                    "firstName": "Yeqing",
                    "lastName": "Wu"
                },
                {
                    "creatorType": "author",
                    "firstName": "Lin",
                    "lastName": "Xu"
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                {
                    "creatorType": "author",
                    "firstName": "Yuping",
                    "lastName": "Liu"
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                {
                    "creatorType": "author",
                    "firstName": "Mary",
                    "lastName": "Leamy"
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                {
                    "creatorType": "author",
                    "firstName": "Muli",
                    "lastName": "Hu"
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            ],
            "abstractNote": "Introduction: Healthcare workers (HCWs) are routinely exposed to distressing and potentially traumatic events during frontline care, yet receive limited structure training to manage such exposure. Psychological first aid (PFA) training has been widely disseminated among emergency responders and is increasingly being adapted for HCWs; however, its acceptability, implementation, and use in routine care remain underexplored. This study aimed to explore how an adapted PFA training programme was perceived, implemented, and used by HCWs in routine care, and to identify the mechanisms and contextual factors that influenced its impact.Method: A qualitative process evaluation was conducted alongside the delivery of the READ-Y (Rapport, Evaluation, Aid, Disposition, Yourself) PFA training programme in a Chinese tertiary hospital. Data collection included 3 months of observations of training delivery and 19 semi-structured interviews with HCW trainees, ward managers, and trainers/facilitators. Data were analysed using framework analysis.Results: Training was logistically feasible, consistently delivered, and characterized by high engagement in simulation and debriefing. Key fidelity elements of PFA training were maintained. Five key themes emerged: (1) positive reception of the adapted, blended-format training; (2) PFA was viewed as a pragmatic, stepwise tool for strengthening basic psychosocial support in routine care; (3) reported improvement in empathy, communication, and normalization of self-care among staff; (4) targeted skills development and peer learning as key mechanisms of reported change, fostering a shared language and support; and (5) multi-level contextual enablers (e.g. leadership endorsement, protected time) were identified and structural barriers (e.g. workload, burnout) existed that may hinder uptake.Conclusion: The adapted PFA training programme was perceived as feasible, relevant, and acceptable for HCWs, facilitating the integration of psychosocial support into routine care while promoting their own well-being. System-level integration and longitudinal evaluation of PFA training are recommended to assess sustained impact on patient and staff outcomes.",
            "publicationTitle": "European Journal of Psychotraumatology",
            "publisher": "",
            "place": "",
            "date": "2026-12",
            "volume": "17",
            "issue": "1",
            "section": "",
            "partNumber": "",
            "partTitle": "",
            "pages": "2627705",
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            "seriesTitle": "",
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            "journalAbbreviation": "Eur J Psychotraumatol",
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            "PMID": "41873497",
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            "shortTitle": "'Being empathetic to my patients and ourselves'",
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            "title": "Back-to-Basics Hospital-Wide Training Improves Inpatient Pediatric Care Experiences",
            "creators": [
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                    "firstName": "Denise D.",
                    "lastName": "Quigley"
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                {
                    "creatorType": "author",
                    "firstName": "Maria",
                    "lastName": "Panayotou"
                },
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                    "creatorType": "author",
                    "firstName": "Mary E.",
                    "lastName": "Slaughter"
                }
            ],
            "abstractNote": "OBJECTIVE: Hospital leaders strive to improve care experiences. Many use training sessions to improve clinician-patient interactions.\nMETHODS: We examined whether a hospital-wide Back-to-Basics training for clinical and nonclinical providers that focused on communication behaviors and safety protocols improved inpatient pediatric care experiences. The curriculum used didactics, video demonstrations, and role-play through interactive, fast-paced stations delivering content tailored to employee role. Child Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores were compared 24 months before (n = 544) and 13 months after the training (n = 643) to assess changes in inpatient pediatric care experiences.\nRESULTS: We observed a statistically significant increase in the overall mean top-box scores after Back-to-Basics implementation for 5 (of 8 targeted) Child HCAHPS measures: Communication between you and child's nurses (3-percentage point [pp] pre-post increase, P = .04), How well nurses communicate with child (7-pp increase, P = .001), How well doctors communicate with child (6-pp increase, P = .007), Preventing mistakes and helping report concerns (4-pp increase, P = .03), and Helping child feel comfortable (4-pp, P = .03). We found no significant differences in models that tested whether the effect of Back-to-Basics differed by service line or by patient race, ethnicity, and language groups.\nCONCLUSIONS: A small investment of curricular time devoted to communication skills and safety procedures across all staffing roles over 3 months improved Child HCAHPS communication-specific and safety scores. Because this training could be implemented in a variety of pediatric and inpatient settings to improve patient and family care experiences, further evaluation of the intervention in different pediatric hospital sites, settings (ie, hospital within a hospital), and contexts (urban, rural) is warranted.",
            "publicationTitle": "Hospital Pediatrics",
            "publisher": "",
            "place": "",
            "date": "2025-03-01",
            "volume": "16",
            "issue": "3",
            "section": "",
            "partNumber": "",
            "partTitle": "",
            "pages": "219-229",
            "series": "",
            "seriesTitle": "",
            "seriesText": "",
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            "abstractNote": "BACKGROUND AND OBJECTIVES: Health-related social needs (HRSN) affect a wide range of short- and long-term outcomes, health care use, functioning, and quality of life. Although HRSN screening is valuable, it is likely ineffective unless coupled with interventions. This project integrated HRSN screening and intervention(s) across 9 inpatient and outpatient divisions, varied in composition, patient populations, process flows, and resource availability.\nMETHODS: A quality improvement team with a standardized change package facilitated a multimodal initiative for divisions spanning hospital medicine, newborn nurseries, and specialty outpatient clinics at an urban, quaternary pediatric hospital system. We aimed for 80% of patient encounters to be screened for HRSN and that each positive screen receive an agreed upon resource intervention using a centralized resource bank.\nRESULTS: From January 2021 to October 2023, 31 834 screenings were conducted across 9 divisions. Performance increased to a mean 92%, with positive screens receiving interventions. The last 7 months of the project were sustained at 92% or higher. Food insecurity was identified in 17.6% of encounters (n = 10 007, 1765 positive), with a 56% decrease in prevalence on repeat screening after identification/intervention.\nCONCLUSIONS: A centralized quality team and change package can facilitate successful implementation of HRSN screening and connection to resources across multiple disciplines and sites. These interventions may lead to a decrease in subsequent HRSN positivity. Rescreening patients over time is important to capture the full spectrum of HRSN needs of a family.",
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