A Qualitative Descriptive Study of Nurse Manager Decision-Making Associated With RN Hiring.

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.

Moving nursing innovation to prime time through the use of creative partnerships.

Nurses are well-positioned to solve many problems in healthcare through engagement in innovation. Support from healthcare organizations to facilitate creative partnerships may accelerate nurses' ability to innovate and improve job satisfaction. The value of creative partnerships is rooted in the diversity of experiences and skillsets of each project team member. While nurses may be content experts and key stakeholders, they often lack experience with project management, information technology, product development, and other important skills. We describe the use of co-creation approaches in creative partnerships with diverse stakeholders to enhance the ability of nurse-led project teams to build valuable and sustainable products or services.

Evaluation of a proactive hiring model to achieve workforce stability in the intensive care unit.

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.

Development and evaluation of a prospective staffing model to improve retention.

AIMS: To improve predictability and accuracy of hiring using historical staffing data, quality improvement and workforce engagement. BACKGROUND: Twenty-three per cent of newly licensed nurses leave their first job within one year, costing employers $52,100 per nurse replacement. Tools for anticipatory hiring strategies are not available in the literature. METHODS: We used retrospective, secondary data analysis to develop a Prospective Staffing Model and conduct a five-year longitudinal evaluation of the implementation of the model in a convenience sample at a quaternary academic Cardiothoracic Intensive Care Unit. We used a team-based, quality improvement approach to restructure recruitment and hiring strategies, standardize new graduate nurse orientation and implement AACN Healthy Work Environment standards. RESULTS: Over the five-year prospective evaluation period (2014-2018), 388 nurses were hired and included in the evaluation cohort. Retention increased (n = 286 days) and turnover decreased (17.6%) between 2014 and 2018. Improvements in workforce stability were sustained at five years. CONCLUSIONS: Use of a Prospective Staffing Model is associated with improved nurse retention and decreased turnover, and may improve workforce stability. IMPLICATIONS FOR NURSING MANAGEMENT: Results suggest that an innovative tool can mitigate the deleterious effects of turnover, adding to current knowledge and providing a method for anticipatory assessment of local turnover.

Recognizing unique characteristics to create equitable, culturally competent teams.

A new approach to selecting members for an interdisciplinary medical mission trip.

Implementation of an Evidence-Based Onboarding Program to Optimize Efficiency and Care Delivery in an Intensive Care Unit.

Nationally, nurse turnover is 18.7%, and 24.1% of nurses leave their organization within a year of hire. Onboarding 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.

Safe Patient Handling and Pressure Injury Prevention for Patients With Delayed Sternal Closure.

BACKGROUND: Pressure injuries are associated with increased morbidity, mortality, and cost. Patients with delayed sternal closure after cardiac surgery are at increased risk of developing pressure injury because of their reduced mobility and inability to undergo complete skin assessment. LOCAL PROBLEM: Safe patient handling of patients with delayed sternal closure is challenging because of mobility restrictions, leading to increased risk for patient and staff harm. METHODS: An interprofessional team composed of bedside nurses, ergonomic specialists, a wound ostomy continence nurse, and cardiothoracic surgeons developed a patient mobility protocol for patients with delayed sternal closure consisting of levitating the patient for thorough skin assessment and care. A multimodal educational strategy was used to disseminate the new protocol. Patient information was collected on the type of surgery, results of the levitation, completion of the skin assessment, and subsequent interventions. Staff injury related to this work was monitored. RESULTS: After implementation of the protocol, 84 levitations were performed including 50 patients. No staff injuries were reported. Comprehensive skin assessments and care were completed for 98% of patients. Five patients were identified with pressure injury development, and appropriate interventions were applied. DISCUSSION: A standardized patient mobility protocol for patients with delayed sternal closure that leveraged available equipment was successful in reducing the risk for injury among staff and patients. The protocol allowed assessment and care and thus also reduced the risk for pressure injury development in these patients. CONCLUSION: Development of an innovative protocol to safely mobilize patients with delayed sternal closure resulted in improved patient care and increased staff safety.

Using Learning Style Assessments to Effectively Match Preceptors and Orientees.

Ineffective pairings between preceptors and orientees can lead to decreased job satisfaction and burnout. This article describes the results of a study using a learning style assessment tool to pair preceptors and orientees in an intensive care unit for newly hired registered nurses between February 2017 and December 2019 (n = 173).

Implementing AACN's Healthy Work Environment Framework in an Intensive Care Unit.

BACKGROUND: Bedside nurse turnover in the United States is 15.9%, representing a national challenge that has been attributed to poor work environments. Healthy work environments are associated with improved nurse satisfaction and retention as well as positive patient outcomes; unhealthy work environments have the opposite effects. OBJECTIVES: To implement the American Association of Critical-Care Nurses (AACN) healthy work environment (HWE) framework in an intensive care unit and to evaluate staff satisfaction, turnover, and tenure 2 years later. METHODS: A pre-post study design was used to evaluate implementation of the HWE framework in an intensive care unit in a large academic medical facility. Interventions for each of the 6 HWE standards were performed. The AACN HWE assessment survey was used to measure skilled communication, true collaboration, effective decision-making, appropriate staffing, meaningful recognition, and authentic leadership in 2017 and in 2019. RESULTS: Nurse cohorts (n = 165 in 2017; n = 176 in 2019) had a mean age of 31 (median, 27; range, 23-63) years, were predominantly female (76%), and had a mean of 5 (median, 3) years of intensive care unit nursing experience. Statistically significant improvements were found in all standards except the skilled communication and overall measures. Registered nurse turnover remained stable and tenure increased by 79 days in this 2-year period. CONCLUSIONS: Findings from this study suggest that interventions addressing the HWE standards are associated with improved staff satisfaction, turnover, and average tenure, further demonstrating the value of the HWE framework in improving retention.

Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study.

BACKGROUND: Successful integrations of machine learning into routine clinical care are exceedingly rare, and barriers to its adoption are poorly characterized in the literature. OBJECTIVE: This study aims to report a quality improvement effort to integrate a deep learning sepsis detection and management platform, Sepsis Watch, into routine clinical care. METHODS: In 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. RESULTS: Sepsis 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. CONCLUSIONS: Machine 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.