Wgu D031 Elam Evidence-Based Innovation Plan Rated 100% Correct

Disruptive innovation is the driver of change, transforming healthcare provision and results via evidence-based approaches. It introduces cost-saving, accessible, and inexpensive means of enhancing efficacy, challenging conventional care systems. Further, evidence-based practice allows these solutions to be in line with research current at the time as well as current clinical standards. Further, nearly 80% of current healthcare leaders in America focus on innovation with a perspective onWSM2: Evidence-Based Innovation Proposal Evidence-Based Innovation Plan Template Western Governors University 2 | Page Accessibility Review Completed and Approved for Distribution and Use. [AEC0006874] Academic Product Management [VD - Based on review completed 03/17/25.] improving treatment for chronic diseases and health inequity. Thus, more educated nurses, including those with MSN degrees, are best suited to spearhead change through clinical expertise, data, and research. Utilization of technology, big data, and ethical practice also empowers nurses as agents of innovation aligned with patient needs. Thus, with evidence-based approaches, nurses can drive care change that improves healthcare quality and enables enhanced patient outcomes within various care settings and populations. Disruptive Innovations in Healthcare: Telehealth and Point-of-Care Testing Two of the most notable disruptive health innovations are telehealth services and point-of-care rapid testing. Telehealth consists of healthcare provision through digital platforms from a distance, and it came into focus with the sudden outbreak of the COVID-19 pandemic. Virtual consultations grew by more than 154% between the years 2019 and 2020, particularly for those in areas with a lack of accessibility (Koonin et al., 2020). Further, telehealth facilitated the management of chronic diseases, with hospital readmissions down by 35% and medication compliance improved. Concurrently, pointof-care rapid diagnostic testing, or performing diagnostic tests at or close to sites of patient treatment, enhanced emergency services with a 40% decrease in waiting times and a decrease in unnecessary antibiotics by 28% (Bouzid et al., 2020). These innovations challenged traditional in-person care models by offering timely, efficient, and accessible services. They demonstrate how technology reshapes healthcare delivery for better outcomes. Role Selected – Nurse as Scientist The MSN-trained nurse as scientist uses systematic inquiry and data analysis to plan, implement, and evaluate evidence-based patient care interventions. This job demands a critically appraisal of research, statistical methods, and quality improvement project conduct. For instance, one MSN-trained nurse implemented an effective hand-washing program based on Centers for Disease Control and Prevention (CDC) recommendations, which decreased hospital-acquired infections by 37% within one year. Moreover, these nurses apply logic models and outcome measurements in testing interventions. In the Western Governors University (WGU) model, the nurse scientist applies research competencies with clinical application to enhance safety and efficiency. Additionally, evidence-based results show that these nurses often inform policy, revise clinical policy guidelines, and facilitate ongoing staffWSM2: Evidence-Based Innovation Proposal Evidence-Based Innovation Plan Template Western Governors University 3 | Page Accessibility Review Completed and Approved for Distribution and Use. [AEC0006874] Academic Product Management [VD - Based on review completed 03/17/25.] education. This process guarantees that change is both measurable and sustainable. The nurse scientist advances the connection between practice and theory by applying well-trialed research-based innovations. Benefits and Challenges of Big Data in Nursing Innovation Utilization of big data in nursing has predictive benefits but is also problematic in terms of privacy and complexity. One key benefit is predictive analytics, which helps detect health risks early. For example, early identification of sepsis using big data has reduced mortality by 17% (Batko & Ślęzak, 2022). Additionally, big data supports trend analysis in chronic disease care, helping nurses design targeted interventions. However, it also creates difficulties for clinicians, especially when information is excessive. About 45% of nurses experience decision fatigue due to the high volume of alerts from digital systems (Merriweather, 2023). Furthermore, ensuring compliance with the Health Insurance Portability and Accountability Act (HIPAA) remains a constant concern, especially with integrating AI and wearable health devices. The usefulness of the information has to be weighed against straightforward, effective design. Better results are promised through integrating big data, but it hinges on mitigating risk and integrating it into the clinical workflow in a user-friendly design