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BackgroundSuccessful integrations of machine learning into routine clinical care are exceedingly rare, and barriers to its adoption are poorly characterized in the literature. ObjectiveThis study aims to report a quality improvement effort to integrate a deep learning sepsis detection and management platform, Sepsis Watch, into routine clinical care. MethodsIn 2016, a multidisciplinary team consisting of statisticians, data scientists, data engineers, and clinicians was assembled by the leadership of an academic health system to radically improve the detection and treatment of sepsis. This report of the quality improvement effort follows the learning health system framework to describe the problem assessment, design, development, implementation, and evaluation plan of Sepsis Watch. ResultsSepsis Watch was successfully integrated into routine clinical care and reshaped how local machine learning projects are executed. Frontline clinical staff were highly engaged in the design and development of the workflow, machine learning model, and application. Novel machine learning methods were developed to detect sepsis early, and implementation of the model required robust infrastructure. Significant investment was required to align stakeholders, develop trusting relationships, define roles and responsibilities, and to train frontline staff, leading to the establishment of 3 partnerships with internal and external research groups to evaluate Sepsis Watch. ConclusionsMachine learning models are commonly developed to enhance clinical decision making, but successful integrations of machine learning into routine clinical care are rare. Although there is no playbook for integrating deep learning into clinical care, learnings from the Sepsis Watch integration can inform efforts to develop machine learning technologies at other health care delivery systems.

The article discusses the selection of members for a medical mission with consideration of their unique qualities to ensure the team's wide cultural range and competence. Topics covered include the Synergy Model that pairs a nurse and patient based on common qualities, the required criteria for medical mission trip candidates, the scoring of candidates, and the team's composition. Also noted is a culturally competent team's ability to meet the needs of specific healthcare populations.

Nationally, nurse turnover is 18.7%, and 24.1% of nurses leave their organizationwithin a year of hire. On boarding is a key component of a nurse's intent to stay and job satisfaction. This article describes the implementation and results of an onboarding program in a large intensive care unit. [ABSTRACT FROM AUTHOR]

Aim: The purpose was to evaluate an innovative Prospective Hiring Equation to determine registered nurse hiring needs. Background: The American Organization for Nursing Leadership identifies human resource management as a competency for nurse managers, yet calculations to determine when and how many registered nurses to hire are not readily available. Methods: We implemented an educational intervention to teach nurse mangers the Prospective Hiring Equation. We evaluated the processes (adoption and confidence) and outcomes (vacancy rates) using a pre‐evaluation/postevaluation design in a single cohort of nurse managers (n = 9). We used a statistical process control chart to depict mean differences in vacancy rate at baseline and 6‐month postimplementation. Results: Participants (n = 9) were on average 43 years' old, female, and had 2.94 (SD = 2.66) years' of nurse manager experience. Following implementation of the intervention, the combined vacancy rates of the intensive care units improved by 11.8% (SD = 7), and use of agency nurses decreased by 42.5% (premedian = 7.2, interquartile ratio = 3.6, 10.8; postmedian = 1.8, interquartile ratio 0.9, 8.55). Conclusions: The Prospective Hiring Equation may be a useful tool to improve nurse managers human resource management competencies. Implications for Nursing Management: The Prospective Hiring Equation may help nurse managers improve accuracy when evaluating hiring needs. [ABSTRACT FROM AUTHOR]

Objective: To explore the practices and self-confidence of nurse manager (NM) decision-making related to evaluating RN hiring needs., Background: Evidence-based hiring strategies to sustain workforce stability were identified as a gap in the literature. Locally, inconsistencies in the method that NMs use to determine how many RNs to hire and at what frequency to hire them were observed, posing a threat to strategic workforce planning., Methods: Using a mixed-methods, qualitative descriptive design, researchers used in-depth interviews and surveys to assess current practices and NM confidence related to evaluating hiring needs., Results: The overarching theme among the 10 participants was false confidence. Subthemes reflected the dissonance in confidence and high variability in the hiring process. NM stress, time spent, and confidence were not correlated with years' experience., Conclusions: NMs experience stress, spend excessive time, and use varying approaches to evaluate hiring needs. Years of managerial experience are unrelated to practical skills or level of confidence in hiring decision-making., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)