The challenge for educational programs, both academic and practice, is the limited number of educators to learners. There may be one educator to 1,000 nurses or more in clinical practice. This makes individualizing professional development and learning. There are limited hours, skills practice space, and simulation space. To meet the learning needs, a structured curriculum is used to educate the masses without consideration for the learner’s knowledge or experience. So, how do you address the need, limited resources, and individualization? The answer is HealthStream’s jane AI to improve nursing objective assessment and clinical judgment wherever the learner’s starting baseline. This HealthySimulation.com article will explore how jane AI impacts learning-based nursing objective assessment and clinical judgment.
Training and Orientation Dilemma
The question remains – How do educators help nurses practice at the top of their license or cross-train in limited time constraints without compromising their personal and professional confidence to provide high-quality, safe patient care? HealthStream’s jane AI is the answer. HealthStream’s jane AI is an artificial intelligence (AI) tool that helps measure clinical judgment, offers clinical development analytics, and personalizes competency development for nurses and other clinical teams. HealthStream’s jane AI will help healthcare organizations assess and discover where their nurses excel and where there’s an opportunity for improvement, focus learning to improve competence, and provide insights on clinical performance and potential risk areas.
While regulations are in place, they need more standardization, which makes the ability to measure the return on investment (ROI) for competency management and development difficult. The American Nurses Association (ANA) recently published an article urging nursing educators to incorporate artificial intelligence-based clinical practice methodologies into their teaching to prepare nurses for the future (Grube, 2022).
Standardized Documentation for Knowledge Assessment and Clinical Judgment
Here are a few questions for educators to ponder: who might benefit from simulation to spend more time focusing on their problem-solving skills development versus everyone completing the required online courses, which require multiple hours in front of a computer to check a compliance requirement? Educators would love to have the time to individualize the education and training for their learners for new hires with experience and those transitioning into their professional roles. This is not feasible most of the time. So, let jane AI help solve this dilemma with standardized documentation of each nurse’s knowledge and clinical judgment. Benefits of jane AI for learners:
- Less time could be spent on generalized compliance, with more time to focus on those individuals who could benefit from more simulation.
- Standard documentation to measure individuals and cohorts and an overall comparison of knowledge and clinical judgment allows the educator to compare apples to apples and oranges to oranges.
- The documentation allows the educator to determine if the individual nurse has a deficit in knowledge or a clinical judgment.
- With jane AI, the learner watches a series of short videos or clinical scenarios, then signs, symptoms, and some clinical data, followed by an open-ended AI chat. This allows for a more comprehensive assessment of the learner.
HealthStream’s jane AI Analytics
HealthStream shares best practices from all their partnering organizations to share lessons learned to benefit all. From experience, jane AI analytics knows that peak knowledge reassessment occurs at two to three months, and peak clinical judgment with the use of HealthStream’s interventions and programs occurs at about five to six months.
There is a difference between how knowledge and clinical judgment are acquired and how knowledge improves once learners have that foundational clinical judgment skill. The simulations can occur in situ without an advanced sim lab or in a sim lab, depending on available resources. The main focus is to complete the scenario and use the reflection tool to determine what questions should have been asked during the simulation. This teaches the learners to consider what could be done better and what could be done to escalate the interaction to the next level of competency. Learning and performance can be measured and compared with national benchmarks to assess performance objectively.
After the simulation experience, the learner goes to the clinical environment with their preceptor. The preceptors encourage the orientee to use the reflection tool in practice with their patient assignments. This process has been so effective that two organizations have experienced tremendous gains in ROI. What was the common factor for these two organizations? The two organizations had a full-time coach who was coaching the preceptors. So, this coach position is a crucial factor to ensure the preceptors have some consistent understanding of how to develop that clinical judgment and how to work with them on that tool at the bedside to start asking questions and develop that sort of muscle memory and neural pathways over time. The personalized journey for the learners has led to a significant reduction in classroom time, meaning the learner sitting in a classroom versus in the clinical environment.
View the LEARN CE/CME Platform Webinar The Impact of Objective Assessments and Clinical Judgment in Nursing with AI to learn more!
More About HealthStream
HealthStream has been in business for about 34 years with a singular focus on the healthcare industry. So we have about five and a half million healthcare staff on our platform, where leaders manage their learning, scheduling, and credentialing workflows, and over 70 world class partners to ensure that together we can fully empower healthcare leaders across the country. Our CEO and co-founder, Bobby Frist, has had a really consistent vision over the years, and this started with his interest in the intersection of human development and technology.
