High tech solutions were incorporated into the electronic medical record system to enhance communication across phases of care and inform the interdisciplinary team of patient progress.Machine learning-based early warning systems (EWSs) can detect clinical deterioration more accurately than point-score tools. In patients with sepsis, however, the timing and scope of sepsis interventions relative to an advanced EWS alert are not well understood. The objectives of this study were to evaluate the timing and frequency of fluid bolus therapy, new antibiotics, and Do Not Resuscitate (DNR) status relative to the time of an advanced EWS alert. We conducted 2 rounds of chart reviews of patients with an EWS alert admitted to community hospitals of a large integrated health system in Northern California (round 1 n = 21; round 2 n = 47). We abstracted patient characteristics and process measures of sepsis intervention and performed summary statistics. Sepsis decedents were older and sicker at admission and alert time. Most EWS alerts occurred near admission, and most sepsis interventions occurred before the first alert. Of 14 decedents, 12 (86%) had a DNR order before death. Fluid bolus therapy and new intravenous antibiotics frequently occurred before the alert, suggesting a potential overlap between sepsis care in the emergency department and the first alert following admission. Two tactics to minimize alerts that may not motivate new sepsis interventions are (1) locking out the alert during the immediate time after hospital admission; and (2) triaging and reviewing patients with alerts outside of the unit before activating a bedside response. Some decedents may have been on a palliative/end-of-life trajectory, because DNR orders were very common among decedents. Nurse leaders sponsoring or leading machine learning projects should consider tactics to reduce false-positive and clinically meaningless alerts dispatched to clinical staff.The future of leadership and nurse engagement requires the use of technology. The nursing workforce is becoming more mobile, flexible, and dynamic. Technology can support nurses to build trusted professional relationships and enhance their professional practice. Nursing leaders need to learn how to effectively use technology in addition to in-person interactions to support their nursing workforce. This article provides tips and tricks for nurse leaders to build relationships with their teams through technology.Matching resources to demand is a daily challenge for hospital leadership. In interdisciplinary collaboration, nurse leaders and data scientists collaborated to develop advanced machine learning to support early proactive decisions to improve ability to accommodate demand. When hundreds or even thousands of forecasts are made, it becomes important to let machines do the hard work of mathematical pattern recognition, while efficiently using human feedback to address performance and accuracy problems. Nurse leaders and data scientists collaborated to create a usable, low-error predictive model to let machines do the hard work of pattern recognition and model evaluation, while efficiently using nurse leader domain expert feedback to address performance and accuracy problems. During the evaluation period, the overall census mean absolute percentage error was 3.7%. ALEx's predictions have become part of the team's operational norm, helping them anticipate and prepare for census fluctuations. This experience suggests that operational leaders empowered with effective predictive analytics can take decisive proactive staffing and capacity management choices. (-)-Nuciferine Predictive analytic information can also result in team learning and ensure safety and operational excellence is supported in all aspects of the organization.Over the next 10 years, the World Health Organization estimates that there will be global shortage of 18 000 000 health care workers. A perfect storm of an aging demographic, long-term drop in birth rate, and a retiring workforce has all the factors contributing to this impending crisis. In nursing, efforts to narrow the shortage gap through strategies that increase the number of admissions to nursing schools, such as increasing faculty members, clinical sites, preceptors, and scholarships, will likely not be enough to offset the shortfall. Solutions, in part, will be to make nurses more productive by reducing waste through the use of technology. This article evaluates how various types of technology such as electronic health records, data analytics, predictive modeling, artificial intelligence, speech recognition, natural language processing, robotics, the Internet of Things, and others can improve nurse productivity by using a Lean framework to eliminate waste and create value. To share Thailand's journey to develop and implement a national response to measure and tackle HIV-related stigma and discrimination and key implementation lessons learned. A national response to stigma and discrimination including policy, measurement, and interventions. Intervention activities began in health facilities because of their key role in achieving health outcomes. Three building blocks were implemented policy and its translation into a roadmap for action; measurement development and routinization to inform intervention design and track progress; and intervention development and implementation. Thailand has successfully integrated a response to stigma and discrimination into its national HIV response. Stigma and discrimination-reduction was included as a key strategic goal for the first time in the 2014-2016 National AIDS Strategic Plan. A costed national stigma and discrimination-reduction roadmap incorporated into the operational plan provided clear strategic direction on how to move forward. The development of HIV-related stigma and discrimination measures and their incorporation into the national HIV monitoring and evaluation framework has led to routine data collection to monitor stigma and discrimination in health facilities, key populations, and the general population. Development and successful piloting of a health facility stigma-reduction package - the 3 × 4 approach - has led to national scale-up of the approach through a modified approach. Thailand continues to evolve and innovate the program, including developing new activities to tackle stigma and discrimination beyond the health system. Thailand's experience demonstrates the key elements needed to make addressing stigma and discrimination a priority in the national HIV response.Thailand's experience demonstrates the key elements needed to make addressing stigma and discrimination a priority in the national HIV response.