Mcn-The American Journal of Maternal-Child Nursing最新文献

Objective: To understand labor and delivery nurses' attitudes and behaviors toward pregnant patients with substance use disorders or mental health concerns. Suicide and overdose are among the leading causes of preventable maternal deaths nationwide, underscoring the urgency of addressing issues related to supporting pregnant patients with behavioral health concerns.

Design: We surveyed a subpopulation of nurses in Colorado to understand attitudes and behaviors toward patients with substance use disorders and mental health concerns.

Results: Survey respondents were 154 nurses from 14 Colorado hospitals that represented 45% of the state's 2022 births: Half of the nurses reported their colleagues have negative attitudes and nearly one-third reported negative behaviors toward patients who disclose substance use. Qualitative data revealed instances of positive behaviors, with examples of supportive clinical care and resource provision for patients disclosing substance use. Negative examples described mistrust, negative attitudes, and lack of compassion toward patients, as well as instances of substandard clinical care.

Conclusions: In this study, negative attitudes and behaviors were noted by some labor and delivery nurses by their colleagues toward pregnant patients with substance use disorders. Positive behaviors were also reported. Comprehensive processes to decrease negative behaviors are necessary to improve care for pregnant patients and to decrease maternal mortality from suicide and accidental overdose.

Abstract: Thre e-dimensional (3D) printing is an emerging technology that expanded quickly into a diverse array of clinical applications over the last decade. 3D printing, often called additive manufacturing, uses specialized printers to create objects through the addition of materials layer-by-layer. Using computer-aided design software via a 3D scanner or a digital camera, objects can be printed to highly precise and specific dimensions. This technology, including both the hardware and software, has applications in surgical procedures, dental implants and crowns, pharmaceuticals, and biomedical products. With the enormous potential of using 3D printing in multiple health care sectors, there is still limited usage for this technology in maternal and child health nursing practice. We provide an overview of 3D printing technology, review the current health care applications, and explore the opportunities and challenges of 3D printing in maternal and child nursing.

Background: The Agency for Healthcare Research and Quality recommends the use of safety huddles, in which clinicians are briefly gathered to review a patient's condition, including new or developing changes in acuity or stability. The Joint Commission describes huddles as a "hallmark" of high-reliability organizations. Previous reviews have confirmed the general utility of huddles, including positive regard by clinicians, but there has not been work specifically looking at huddle use in hospital maternity care settings. Our objective was to identify the ways that huddles have been studied or reported in inpatient maternity settings and synthesize this information with recommendations from professional organizations to identify gaps in the literature published in the United States since 1999.

Methods: We used Arksey and O'Malley's framework to guide our scoping review. Using the time frame from 1999 to 2024, we searched the following databases: PubMed, CINAHL, SCOPUS, Embase, as well as gray literature and the reference lists and citing articles of the included manuscripts.

Results: We found 160 documents, of which 47 met inclusion criteria, including 11 care bundles, 10 quality improvement projects, and 4 research studies. The remaining 22 were a variety of editorials, position papers, and other gray literature.

Discussion: There is scant research on the use of huddles in hospital maternity care settings, and most literature does not define the participants, timing, or agenda of the huddle. Further research is needed to understand how huddles affect outcomes in maternity settings. Researchers should explicitly define the huddles they are studying. The review protocol was registered at Open Science Framework Registries.

Abstract: Predictive analytics has emerged as a promising approach for improving reproductive health care and patient outcomes. During pregnancy and birth, the ability to accurately predict risks and complications could enable earlier interventions and reduce adverse events. However, there are challenges and ethical considerations for implementing predictive models in perinatal care settings. We introduce major concepts in predictive analytics and describe application of predictive modeling to perinatal care topics such as fertility, preeclampsia, labor onset, vaginal birth after cesarean, uterine rupture, induction outcomes, postpartum hemorrhage, and postpartum mood disorders. Although some predictive models have achieved adequate accuracy (AUC 0.7-0.9), most require additional external validation across diverse populations and practice settings. Bias, particularly racial bias, remains a key limitation of current models. Nurses and advanced practice nurses, including nurse practitioners certified registered nurse anesthetists, and nurse-midwives, play a vital role in ensuring high-quality data collection and communicating predictive model outputs to clinicians and users of the health care system. Addressing the ethical challenges and limitations of predictive analytics is imperative to equitably translate these tools to support patient-centered perinatal care.

Abstract: Artificial intelligence (AI), defined as algorithms built to reproduce human behavior, has various applications in health care such as risk prediction, medical image classification, text analysis, and complex disease diagnosis. Due to the increasing availability and volume of data, especially from electronic health records, AI technology is expanding into all fields of nursing and medicine. As the health care system moves toward automation and computationally driven clinical decision-making, nurses play a vital role in bridging the gap between the technological output, the patient, and the health care team. We explore the nurses' role in translating AI-generated output to patients and identify considerations for ensuring informed consent and shared decision-making throughout the process. A brief review of AI technology and informed consent, an identification of power dynamics that underly informed consent, and descriptions of the role of the nurse in various relationships such as nurse-AI, nurse-patient, and patient-AI are covered. Ultimately, nurses and physicians bear the responsibility of upholding and safeguarding the right to informed choice, as it is a fundamental aspect of safe and ethical patient-centered health care.