International Journal of Clinical Case Reports, 2025, Vol.15, No.2, 90-97 http://medscipublisher.com/index.php/ijccr 94 significantly improved clinical practice, attention must be given to patient-related factors and logistical challenges that may impact adherence (Richards et al., 2019). Future implementations should consider these factors and develop strategies to mitigate their effects. These case study demonstrates that with the right framework and support, rapid assessment tools can be effectively integrated into stroke units to enhance patient care and outcomes. Table 1 Investigator developed survey questions (Adopted from Moore et al., 2020) Questions Added to the 2015, 2016, and 2018 Survey, With the 2015 Results Described Please read the following questions on the effect of the FIRST project on your measurement related practice. Indicate whether you completely disagree (1), disagree (2), are neutral (3, neither agree nor disagree), agree (4), or completely agree (5) 1. I am familiar with the Berg Balance Scale 4.5 (4.0-5.0) 2. I use the Berg Balance Scale in routine practice 5.0 (4.0-5.0) 3. I am familiar with the 10-meter walk test 4.0 (3.25-4.0) 4. I use the 10-meter walk test as part of routine practice 3.0 (2.0-4.0) 5. I am familiar with the 6-minute walk test 4.0 (4.0-5.0) 6. I use the 6-minute walk test as part of routine practice 4.0 (4.0-4.75) Questions Added to the 2018 Survey, With 2018 Results Described Please read the following questions on the effect of the project on your measurement related practice. Indicate whether you completely disagree (1), disagree (2), are neutral (3, neither agree nor disagree), agree (4), or completely agree (5) 1. As a result of this project, I have increased the use of outcome measures in my clinical practice 5.0 (4.25-5.0) 2. As a result of this project, I use the outcome measure results to guide my clinical decision-making 5.0 (4.0-5.0) 3. As a result of this project, I have more discussions with my patients about their outcome measurement results 5.0 (4.0-5.0) 4. As a result of this project, I have more discussions with colleagues about outcome measurement results 4.5 (4.0-5.0) 5. As a result of this project, the culture in our department has shifted to discuss patient-related data (ie, outcome measurement results) instead of patient observations (ie, patient walks slowly, has poor balance, etc) 4.0 (4.0-5.0) 6. As a result of this project, I better understand the value outcome measures add to clinical practice 5.0 (4.0-5.0) 7. After this project is over, I plan to use outcome measures at the same or higher frequency that I currently use them 4.0 (4.0-5.0) Note: Results provided in median (range) (Adopted from Moore et al., 2020) 6 Future Directions in Rapid Assessment for CVA Nursing Care 6.1 Development of new and improved assessment tools The development of new and improved assessment tools for nursing care in patients with acute cerebrovascular accidents (CVA) is crucial. Current digital health systems often fail to capture the full scope of nursing work, which can hinder clinical decision-making and limit the visibility of nursing roles in patient care interventions (Hants et al., 2023). Future tools should aim to integrate comprehensive nursing processes, including assessment, planning, intervention, and outcome evaluation, to enhance the accuracy and efficiency of patient care (Wang, 2024). Additionally, these tools should be designed to support early disease detection and clinical decision-making, thereby improving patient outcomes and workflow optimization (Martinez-Ortigosa et al., 2023). 6.2 Potential for integration with artificial intelligence and telemedicine Artificial intelligence (AI) and telemedicine hold significant potential for transforming CVA nursing care. AI can support nurses in clinical decision-making, patient monitoring, and workflow optimization, thereby enhancing the quality and efficiency of care. AI-enabled robotics and telehealth solutions can expand the reach of nursing care, improving the accessibility of healthcare services and enabling remote monitoring of patients' health conditions (Rony et al., 2023). However, the integration of AI in nursing care must address ethical, legal, and social implications, including data privacy, safety, and technology acceptance (Seibert et al., 2020). Future research should focus on real-world applications of AI in nursing to evaluate its effectiveness and benefits in clinical settings.
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