Search

PURPOSE: The purpose of this systematic review was to assess risk of bias in existing prognostic models of hospital-induced delirium for medical-surgical units. METHODS: APA PsycInfo, CINAHL, MEDLINE, and Web of Science Core Collection were searched on July 8, 2022, to identify original studies which developed and validated prognostic models of hospital-induced delirium for adult patients who were hospitalized in medical-surgical units. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was used for data extraction. The Prediction Model Risk of Bias Assessment Tool was used to assess risk of bias. Risk of bias was assessed across four domains: participants, predictors, outcome, and analysis. RESULTS: Thirteen studies were included in the qualitative synthesis, including ten model development and validation studies and three model validation only studies. The methods in all of the studies were rated to be at high overall risk of bias. The methods of statistical analysis were the greatest source of bias. External validity of models in the included studies was tested at low levels of transportability. CONCLUSIONS: Our findings highlight the ongoing scientific challenge of developing a valid prognostic model of hospital-induced delirium for medical-surgical units to tailor preventive interventions to patients who are at high risk of this iatrogenic condition. With limited knowledge about generalizable prognosis of hospital-induced delirium in medical-surgical units, existing prognostic models should be used with caution when creating clinical practice policies. Future research protocols must include robust study designs which take into account the perspectives of clinicians to identify and validate risk factors of hospital-induced delirium for accurate and generalizable prognosis in medical-surgical units.

In 2019, the Florida Health Sciences Library Association (FHSLA) and the Tampa Bay Library Consortium, Inc. (TBLC) joined together to increase awareness of reliable vetted health information resources for Florida residents. FHSLA, composed of approximately 75 academic/health sciences/hospital librarians, contacted TBLC, composed of public librarians and various library personnel in Florida, to explore opportunities for their members to work together and network through monthly health-focused webinars. This article describes their collaborative initiative. [ABSTRACT FROM AUTHOR]

Objectives Electronic health records (EHRs) user interfaces (UI) designed for data entry can potentially impact the quality of patient information captured in the EHRs. This review identified and synthesized the literature evidence about the relationship of UI features in EHRs on data quality (DQ). Materials and methods We performed an integrative review of research studies by conducting a structured search in 5 databases completed on October 10, 2022. We applied Whittemore & Knafl's methodology to identify literature, extract, and synthesize information, iteratively. We adapted Kmet et al appraisal tool for the quality assessment of the evidence. The research protocol was registered with PROSPERO (CRD42020203998). Results Eleven studies met the inclusion criteria. The relationship between 1 or more UI features and 1 or more DQ indicators was examined. UI features were classified into 4 categories: 3 types of data capture aids, and other methods of DQ assessment at the UI. The Weiskopf et al measures were used to assess DQ: completeness (n  = 10), correctness (n  = 10), and currency (n  = 3). UI features such as mandatory fields, templates, and contextual autocomplete improved completeness or correctness or both. Measures of currency were scarce. Discussion The paucity of studies on UI features and DQ underscored the limited knowledge in this important area. The UI features examined had both positive and negative effects on DQ. Standardization of data entry and further development of automated algorithmic aids, including adaptive UIs, have great promise for improving DQ. Further research is essential to ensure data captured in our electronic systems are high quality and valid for use in clinical decision-making and other secondary analyses. [ABSTRACT FROM AUTHOR]

Results are reported of a small-scale sampling experiment on the performance of robust estimators of location. The underlying populations are taken to be Normal contaminated unsymmetrically. The relative behaviour of the estimators depends on the aspect of the distribution it is required to estimate. [ABSTRACT FROM AUTHOR]

Objective: To synthesize the literature on skin failure and pressure injuries among hospitalized patients with COVID-19., Data Sources: An electronic literature search using relevant keywords and controlled vocabulary was conducted in March 2023 on MEDLINE/PubMed, Embase, and CINAHL. Manual citation searches of included articles and grey literature, including the Wound, Ostomy, and Continence Nurses Society website were performed. Articles published in English between 2020 and April 2023 were considered., Study Selection: Articles were included if they reported on COVID-19 positive hospitalized adults with wounds that were not present upon admission. A total of 31 articles met these criteria., Data Extraction: Covidence was used to extract the data and was reviewed by multiple team members., Data Synthesis: Of the 31 studies, 27 reported new onset skin lesions during hospitalization. Wounds were classified as pressure injuries, skin failure, livedo racemosea and/or, retiform purpura, and associated with microvascular thrombosisthrombotic vasculopathy. Most pressure injuries were associated with prone position and affected patients often had multiple comorbidities including hypertension, diabetes mellitus, end-stage renal disease, heart disease, and COPD. Four articles highlighted an increased risk of new onset wounds, and three emphasized the importance of distinguishing deep tissue pressure injuries from ischemic-related lesions in patients with COVID-19., Conclusions: The evidence suggests an increased risk of ischemic lesions and pressure injuries (PI) in patients with COVID-19 infection. This phenomenon may have inflated the numbers of PI during the pandemic and adversely affected nursing quality measures in acute care environments., Competing Interests: There are no conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives: Electronic health records (EHRs) user interfaces (UI) designed for data entry can potentially impact the quality of patient information captured in the EHRs. This review identified and synthesized the literature evidence about the relationship of UI features in EHRs on data quality (DQ)., Materials and Methods: We performed an integrative review of research studies by conducting a structured search in 5 databases completed on October 10, 2022. We applied Whittemore & Knafl's methodology to identify literature, extract, and synthesize information, iteratively. We adapted Kmet et al appraisal tool for the quality assessment of the evidence. The research protocol was registered with PROSPERO (CRD42020203998)., Results: Eleven studies met the inclusion criteria. The relationship between 1 or more UI features and 1 or more DQ indicators was examined. UI features were classified into 4 categories: 3 types of data capture aids, and other methods of DQ assessment at the UI. The Weiskopf et al measures were used to assess DQ: completeness (n = 10), correctness (n = 10), and currency (n = 3). UI features such as mandatory fields, templates, and contextual autocomplete improved completeness or correctness or both. Measures of currency were scarce., Discussion: The paucity of studies on UI features and DQ underscored the limited knowledge in this important area. The UI features examined had both positive and negative effects on DQ. Standardization of data entry and further development of automated algorithmic aids, including adaptive UIs, have great promise for improving DQ. Further research is essential to ensure data captured in our electronic systems are high quality and valid for use in clinical decision-making and other secondary analyses., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Introduction: Information and communication technology (ICT) has emerged as promising to support health care consumers, including informal caregivers. This systematic review seeks to evaluate the state of the science of ICT interventions on the health of informal dementia caregivers., Methods: We searched PubMed, CINAHL, Web of Science, and PsycINFO using concepts associated with ICT, dementia, and caregiver. Studies were assessed using the Quality Assessment Tool for Quantitative Studies., Results: We identified 657 full-text publications. After removal of duplicates and title, abstract, and full-text screening, the quality of 12 studies was assessed. Studies varied in technology, implementation, results, and intervention evaluation., Discussion: The methodological quality of the ICT intervention studies among dementia family caregivers was moderate to strong, yet outcome measurement was not uniform. The evidence is strongest for various forms of telephone-based interventions. However, there is a need for research that includes heterogeneous participants based on gender, race, and ethnicity.