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Abstract: The nonlinearity of the current–voltage characteristics has been observed in thin films of the tin oxide exposed to strong electric fields. The experimental data are discussed assuming the drift-type behavior of the absorbed ions along the surface. The observed phenomena allow application for both air contamination detection and the contamination identification. [Copyright &y& Elsevier]

The article is devoted to the substantiation of regional information management development priorities based on the digitalisation system. The paper substantiates the sequence of managerial actions in the development of Ukraine’s economic digitalisation, taking into account existing changes in regional management, outlining the accumulated potential of science and practice, forming compliance with global progress of digitalisation and requiring a scientifically sound platform for regional information management. The directions of digitalisation intensification and information management development in Ukraine are formulated. The expediency of creating a specialised automated information system, the purpose of which should be to assess regional economic processes and prospects for their development in terms of economic growth, is substantiated. Requirements to information support of administrative activity in the system of regional management are formulated. The main tasks of information management support for the achievement of regional information management efficiency are defined.

Abstract: Dependence of the conductivity of a thin-film gas sensor on the concentration of the ethanol or acetone vapors in air has been studied. We found that power law describes this dependence only in a part of the whole vapor concentration range. Theoretical model of the gas response of thin-film gas sensor has been proposed. The basic model parameters, such as desorption heat of the gas particles from the sensor surface or the separation between the energy levels induced by the gas species and levels induced by the oxygen particles in the energy gap of the sensor film material, can be used as identifying agents. The identification ability of a single-gas sensor based on SnO2 thin film is demonstrated by processing the concentration dependence of the sensor conductivity. [Copyright &y& Elsevier]

Nursing's economic value is presently framed within the dominant "outcomes-over-cost" value framework. Within this context, organizations employing nurses often use nursing budget reductions as a cost-minimization strategy, with the intent of retaining high quality outcomes. However, persistent issues such as nurse understaffing, burnout, and turnover threaten healthcare systems' capacity to deliver the quality, equitable, affordable patient care that the public requires. In this paper, we propose a new conceptual model and definition of nursing's economic value. The model development is guided by the convergence of three classic economic frameworks: human capital theory, production theory, and value theory. Grounded in these theories, we envision nursing as a value-adding human capital asset and explicitly link nursing staff characteristics and allocation to the production of healthcare services and organizational financial outcomes. We redefine nursing's economic value as the return on investment (ROI) in nursing human capital reflected in the improvement of consumer, nurse, and organizational outcomes. This new conceptual model, termed the Nursing Human Capital Value Model, presents a cycle of value creation that starts with investments in growing, developing and sustaining an organization's nursing human capital. Nurses, as a human capital asset, deliver nursing care-a foundational ingredient to the production of healthcare services and consumer outcomes. Improved outcomes, subsequently, drive organizational revenue growth. Finally, the accrued revenue is reinvested in nursing, further propelling the cycle's continuation. This innovative model, which is applicable across health systems financed through both governmental and private/non-governmental payor sources, highlights that investment in nursing human capital development is essential for sustainable value generation, identifying opportunities for optimizing nurses' contributions to the value cycle. By directly incorporating economic theories of human capital, production, and value, our model paves the way for future research on the dynamic scope of nursing's economic contribution within healthcare organizations and systems and underscores its necessity for the long-term sustainability and growth of the nursing profession. Tweetable abstract: The economic value of nursing lies in the return on investment in nursing human capital. #nurses #ROI #healthcare., Competing Interests: Declaration of Competing Interest The author is an Editorial Board Member/Editor-in-Chief/Associate Editor/Guest Editor for The International Journal of Nursing Studies and was not involved in the editorial review or the decision to publish this article. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Olga Yakusheva and Marianne Weiss were commissioned by the American Nurses Association and its subsidiaries, American Nurses Credentialing Center and American Nurses Foundation; together known as the American Nurses Association Enterprise (“ANA Enterprise”). The authors are solely responsible for the content of this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Rationale: Organizing ICU interprofessional teams is a high priority because of workforce needs, but the role of interprofessional familiarity remains unexplored. Objectives: Determine if mechanically ventilated patients cared for by teams with greater familiarity have improved outcomes, such as lower mortality, shorter duration of mechanical ventilation (MV), and greater spontaneous breathing trial (SBT) implementation. Methods: We used electronic health records data of five ICUs in an academic medical center to map interprofessional teams and their ICU networks, measuring team familiarity as network coreness and mean team value. We used patient-level regression models to link team familiarity with patient outcomes, accounting for patient and unit factors. We also performed a split-sample analysis by using 2018 team familiarity data to predict 2019 outcomes. Measurements and Main Results: Team familiarity was measured as the average number of patients shared by each clinician with all other clinicians in the ICU (i.e., coreness) and the average number of patients shared by any two members of the team (i.e., mean team value). Among 4,485 encounters, unadjusted mortality was 12.9%, average duration of MV was 2.32 days, and SBT implementation was 89%; average team coreness was 467.2 (standard deviation [SD], 96.15), and average mean team value was 87.02 (SD, 42.42). A 1-SD increase in team coreness was significantly associated with a 4.5% greater probability of SBT implementation, 23% shorter MV duration, and 3.8% lower probability of dying; the mean team value was significantly associated with lower mortality. Split-sample results were attenuated but congruent in direction and interpretation. Conclusions: Interprofessional familiarity was associated with improved outcomes; assignment models that prioritize familiarity might be a novel solution.

Background: The Awakening, Breathing Coordination, Delirium monitoring and Early mobility bundle (ABCDE) is associated with lower mortality for intensive care unit (ICU) patients. However, efforts to improve ABCDE are variably successful, possibly due to lack of clarity about who are the team members interacting when caring for each patient, each shift. Lack of patient shift-level information regarding who is interacting with whom limits the ability to tailor interventions to the specific ICU team to improve ABCDE., Objective: Determine the number and types of individuals (i.e., clinicians and family members) interacting in the care of mechanically ventilated (MV) patients, as reported by the patients' assigned physician, nurse, and respiratory therapist (RT) each shift, using a network science lens., Methods: We conducted a prospective, patient-shift-level survey in 2 medical ICUs. For each patient, we surveyed the assigned physician, nurse, and RT each day and night shift about who they interacted with when providing ABCDE for each patient-shift. We determined the number and types of interactions, reported by physicians, nurses, and RTs and day versus night shift., Results: From 1558 surveys from 404 clinicians who cared for 169 patients over 166 shifts (65% response rate), clinicians reported interacting with 2.6 individuals each shift (physicians: 2.65, nurses: 3.33, RTs: 1.86); this was fewer on night shift compared to day shift (1.99 versus 3.02). Most frequent interactions were with the bedside nurse, attending, resident, intern, and RT; family member interactions were reported in less than 1 in 5 surveys (12.2% of physician surveys, 19.7% of nurse surveys, 4.9% of RT surveys)., Interpretation: Clinicians reported interacting with 3-4 clinicians each shift, and fewer on nights. Nurses interacted with the most clincians and family members. Interventions targeting shift-level teams, focusing on nurses and family members, may be a way to improve ABCDE delivery and ICU teamwork., Competing Interests: Authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

Since the COVID-19 pandemic, nurses and nurse leaders are increasingly vocal about chronic understaffing and the impact the staffing crisis continues to have on nurses' well-being and patient outcomes. The American Nurses Association's Nurse Staffing Task Force addressed the importance of staffing standards as a critically needed step toward improving patient and population health outcomes. Against the backdrop of ongoing nursing shortages, hospital leaders have been hesitant to embrace staffing ratios, expressing concerns about their ability to hire and retain sufficient nursing staff, as operational revenue margins remain thin and nursing labor is costly. This article explicates structural issues within the current nursing reimbursement model that harms hospitals' business case for investments in nurse staffing and work environments. We argue that nurses must advocate for nursing reimbursement reform to increase the nursing workforce and improve nurse staffing and work environments. Such reform is necessary to support sustained hospital investments, financial philosophies, and approaches to meaningfully address and improve nurse staffing., Competing Interests: Declaration of Conflicting InterestsThe authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Katie Boston-Leary is an employee of the American Nurses Association where she serves as Director of Nursing Programs overseeing Nursing Practice and Work Environment and Healthy Nurse Healthy Nation. Opinions expressed here are her own and may not represent the American Nurses Association or any of its affiliates. Olga Yakusheva is Professor of Public Health and Nursing at the University of Michigan. She currently serves as a member of the Research Council of the American Nurses Association Enterprise and an independent expert for American Nurses Credentialing Center. All views are her own and do not necessarily reflect any of her affiliations.

Background: Home health care (HHC) services following hospital discharge provide essential continuity of care to mitigate risks of posthospitalization adverse outcomes and readmissions, yet patients from racial and ethnic minority groups are less likely to receive HHC visits., Objective: To examine how the association of nurse assessments of patients' readiness for discharge with referral to HHC services at the time of hospital discharge differs by race and ethnic minority group., Research Design: Secondary data analysis from a multisite study of the implementation of discharge readiness assessments in 31 US hospitals (READI Randomized Clinical Trial: 09/15/2014-03/31/2017), using linear and logistic models adjusted for patient demographic/clinical characteristics and hospital fixed effects., Subjects: All Medicare patients in the study's intervention arm (n=14,684)., Measures: Patient's race/ethnicity and discharge disposition code for referral to HHC (vs. home) from electronic health records. Patient's Readiness for Hospital Discharge Scale (RHDS) score (0-10 scale) assessed by the discharging nurse on the day of discharge., Results: Adjusted RHDS scores were similar for non-Hispanic White (8.21; 95% CI: 8.18-8.24), non-Hispanic Black (8.20; 95% CI: 8.12-8.28), Hispanic (7.92; 95% CI: 7.81-8.02), and other race/ethnicity patients (8.09; 95% CI: 8.01-8.17). Non-Hispanic Black patients with low RHDS scores (6 or less) were less likely than non-Hispanic White patients to be discharged with an HHC referral (Black: 26.8%, 95% CI: 23.3-30.3; White: 32.6%, 95% CI: 31.1-34.1)., Conclusions: Despite similar RHDS scores, Black patients were less likely to be discharged with HHC. A better understanding of root causes is needed to address systemic structural injustice in health care settings., Competing Interests: The authors declare no conflict of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Accurate in-hospital mortality prediction can reflect the prognosis of patients, help guide allocation of clinical resources, and help clinicians make the right care decisions. There are limitations to using traditional logistic regression models when assessing the model performance of comorbidity measures to predict in-hospital mortality. Meanwhile, the use of novel machine-learning methods is growing rapidly. In 2021, the Agency for Healthcare Research and Quality published new guidelines for using the Present-on-Admission (POA) indicator from the International Classification of Diseases, Tenth Revision, for coding comorbidities to predict in-hospital mortality from the Elixhauser's comorbidity measurement method. We compared the model performance of logistic regression, elastic net model, and artificial neural network (ANN) to predict in-hospital mortality from Elixhauser's measures under the updated POA guidelines. In this retrospective analysis, 1,810,106 adult Medicare inpatient admissions from six US states admitted after September 23, 2017, and discharged before April 11, 2019 were extracted from the Centers for Medicare and Medicaid Services data warehouse. The POA indicator was used to distinguish pre-existing comorbidities from complications that occurred during hospitalization. All models performed well (C-statistics >0.77). Elastic net method generated a parsimonious model, in which there were five fewer comorbidities selected to predict in-hospital mortality with similar predictive power compared to the logistic regression model. ANN had the highest C-statistics compared to the other two models (0.800 vs. 0.791 and 0.791). Elastic net model and AAN can be applied successfully to predict in-hospital mortality., (© 2023 Wiley Periodicals LLC.)

With the ongoing transition to value-based health care, a strong command of foundational economic concepts, like cost and value, and the ability to thoughtfully engage in value-informed nursing practice have become essential for the future of the nursing profession. Earlier in this six-part series, we explained value-informed nursing practice, its historical, economic, and ethical foundation, its promise for an environmentally responsible, innovation-driven future health care, and why its adoption requires a reframing of some of the nursing's professional norms and behaviors. This paper concludes the series with one of the most important issues-education for value-informed nursing practice. We begin by setting forth our vision of how nursing students will learn and apply value informed nursing practice, consider challenges that nurse educators will face, and offer some suggestions for engraining value into the consciousness of the nursing profession., (Copyright © 2022. Published by Elsevier Inc.)

Corynebacterium spp. are part of the human microbiome, but can cause the development of inflammatory diseases of various localization. Purpose - to evaluate the relationship between pathogenic properties and resistance to antimicrobial drugs (AMD) of Corynebacterium spp. from patients with inflammatory diseases of the respiratory tract. Strains of Corynebacterium spp. isolated from patients with inflammatory diseases of the respiratory tract (99 pcs.) and practically healthy individuals (33 pcs.). Isolates were identified by mass spectrometric method (MALDI-ToFMS), their adhesive and invasive activity on Hep-2 cells, cytopathic effect (CPE) in CHO-K1 cell culture, and resistance to antimicrobial drugs (AMD) were determined. Indicators of adhesion (3.65±0.679(CFU±m)x102/ml), invasion (1.72±0.230 (CFU±m)x102/ml), cytotoxicity (69.1±3.8% of dead CHO-K1 cells ) Corynebasterium spp. strains isolated from patients are higher (p≤0.05) than similar indicators in practically healthy people. 90.9% of isolates from patients had resistance to AMD, in most cases (57.6±4.9%) resistance to only one AMP was noted, less often to two (25.2±4.3%), three or more (8.08±2.7%). According to the results of correlation-regression analysis, pathogenic properties (adhesiveness, invasiveness, cytotoxicity) of Corynebacterium spp. strains isolated from patients are in close direct relationship with resistance to AMD. This indicates the importance of identifying strains of non-diphtheria corynebacteria resistant to AMDs, which, under the influence of developing resistance to AMDs, can increase their pathogenic potential, moving from commensalism to parasitism., Competing Interests: The authors declare no conflict of interest.

With the adoption of value-based payments which tie reimbursement to patient outcomes and costs, days when nursing is viewed primarily as a cost to hospitals will soon be over. Already the backbone of high-quality care delivery and patient outcomes, nurses are becoming key drivers of health care organizations' financial outcomes, too. The first three articles published in this 6-part series on value-informed nursing practice-practice that considers both the outcomes and the cost of producing the outcomes-described what value-informed nursing practice means, its economic, policy, and ethical impetuses, and how value-informed nursing practice helps improve environmental sustainability of health systems. Here, in Part 4, we focus on the importance of nursing innovation in implementing value-informed nursing practice. We begin by discussing how innovation is connected to value and then examine the false dichotomy, perceived by many, between innovation and evidence-based care. Following this, we examine how health care organizations and systems can support nursing innovation, before concluding with recommendations for nursing educators., (Copyright © 2022 Elsevier Inc. All rights reserved.)

In this 3rd part of our 6-part series on value-informed nursing practice-practice that focuses on both achieving desired patient outcomes and minimizing the use of costly resources to achieve these outcomes-we focus on the importance of nurses in improving environmental outcomes and reducing costly environmental waste. We also propose how nursing education needs to change to prepare the next generation of nurses to effectively address environmental problems through providing value-informed nursing practice., (Copyright © 2022. Published by Elsevier Inc.)