Week 6 informatics replies
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ClaudiaAvilainfrmaticsreply.docx
MaikelNaranjoInformaticsreply.docx
ClaudiaAvilainfrmaticsreply.docx
Claudia Avila
9 hours ago, at 12:08 PM
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Enhancing EHR Competency through Simulation Training
Oct 09, 2024
Integrate performance metrics with reflective practice to assess nurses' effective use of the EHR system. The assessment framework will include simulation software analytics to track and analyze key performance indicators such as time spent on tasks, error rates in medication orders, and navigation efficiency within the EHR system (Zheng et al., 2020). Peer review and self-assessment will also play a crucial role in the evaluation process, fostering a culture of continuous improvement and self-directed learning. Zheng et al. (2020) highlighted that reflective practice encourages learners to reflect on their actions and decisions, promoting deeper learning and professional growth. In this sense, assessment tools will focus on technical skills and the ability to evaluate one's practice and learn from simulated experiences critically.
Determining the optimal time for each nurse to engage in the simulation exercises is critical to the training program's success. Considering the diversity in learning paces and the need for thorough engagement with the simulated cases, approximately 45 minutes per session will be adequate (Al-Elq, 2020). This estimate includes a brief introduction, the simulation exercise, and a structured debriefing period to facilitate reflection and learning.
A staggered scheduling approach will be used to efficiently manage these sessions within the constraints of the simulation center's schedule, allowing multiple sessions to be conducted simultaneously in different simulation bays (Archana et al., 2021). This strategy, coupled with online booking and scheduling tools, will maximize the utilization of the simulation center's resources while minimizing disruption to clinical services, as Archana et al. (2021) recommended. This approach ensures that training is effective and efficient, accommodating the nursing staff's needs and the hospital's operational requirements.
Implementing a manual data-loading process for simulating clinical scenarios in the EHR system is part of our strategy. This method facilitates the creation of tailored scenarios that reflect the most common and impactful medication and documentation errors (Hamad & Bah, 2022). It's a deliberate choice to ensure that the training is as relevant and practical as possible, allowing nurses to apply what they learn daily directly. Literature supports the effectiveness of targeted simulation exercises in improving clinical competencies and patient safety outcomes (Carayon et al., 2019). This approach aims to directly address the gaps in EHR competency that have been identified, making the training highly relevant to our nurses' needs.
As an alternative to labor-intensive manual data entry, the integration of pre-existing clinical case databases with the simulation software. These databases, often developed by educational and healthcare institutions, contain many case studies that can be adapted to simulate the specific medication and documentation errors they aim to address (Miller & Brown, 2018). Leveraging such resources could provide a rich, varied, and cost-effective means of enriching the simulation experience. Love-Kohet al. (2019) highlight the value of utilizing diverse educational resources to enhance learning and adaptability in clinical settings.
The challenges associated with manual data loading include the potential for inaccuracies and the considerable time investment required to develop realistic scenarios. Ensuring these scenarios' clinical relevance and accuracy is paramount but can be resource-intensive (Love-Koh et al., 2019). Conversely, while using pre-existing clinical case databases offers efficiency and variety, it may not fully capture the unique context of our institution's patient population and specific error patterns. The challenge lies in customizing these cases to fit our learning objectives and the specific issues our nurses encounter. As noted by Love-Kohet al. (2019), the success of simulation training hinges on its ability to mimic real-life challenges accurately, necessitating a careful balance between generic and customized content.
References
Al-Elq A. H. (2020). Simulation-based medical teaching and learning. Journal of family & community medicine, 17(1), 35–40. https://doi.org/10.4103/1319-1683.68787
Archana, S., Nilakantam, S. R., Hathur, B., & Dayananda, M. (2021). The need and art of establishing skill and simulation centers to strengthen skill-based medical education: Learning insights and experience. Annals of African medicine, 20(4), 247–254. https://doi.org/10.4103/aam.aam_53_20
Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. Artificial Intelligence in Healthcare, 25–60. https://doi.org/10.1016/B978-0-12-818438-7.00002-2
Carayon, P., Du, S., Brown, R., Cartmill, R., Johnson, M., & Wetterneck, T. B. (2019). EHR-related medication errors in two ICUs. Journal of healthcare risk management : the journal of the American Society for Healthcare Risk Management, 36(3), 6–15. https://doi.org/10.1002/jhrm.21259
Hamad, M. M. E., & Bah, S. (2022). Impact of Implementing Electronic Health Records on Medication Safety at an HIMSS Stage 6 Hospital: The Pharmacist's Perspective. The Canadian journal of hospital pharmacy, 75(4), 267–275. https://doi.org/10.4212/cjhp.3223
Love-Koh, J., Peel, A., Rejon-Parrilla, J. C., Ennis, K., Lovett, R., Manca, A., ... & Taylor, M. (2019). The future of precision medicine: potential impacts for health technology assessment. Pharmacoeconomics, 36, 1439-1451.
Miller, D. D., & Brown, E. W. (2019). Artificial intelligence in medical practice: the question to the answer?. The American journal of medicine, 131(2), 129-133.
Zheng, K., Ratwani, R. M., & Adler-Milstein, J. (2020). Studying Workflow and Workarounds in Electronic Health Record-Supported Work to Improve Health System Performance. Annals of internal medicine, 172(11 Suppl), S116–S122. https://doi.org/10.7326/M19-0871
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MaikelNaranjoInformaticsreply.docx
Maikel Naranjo
Florida National University
Health Care Informatics-DAX-DL01
Dr. Professor Deborah Crevecoeur
10/09/24
To address the identified competency issues with electronic health record (EHR) documentation and patient safety, it is critical to develop a robust strategy for EHR training that utilizes simulation. This approach allows for an immersive experience where nurses can learn and refine skills in a realistic yet safe environment. Below, I discuss strategies related to two key areas: replicating the EHR production domain and evaluating nurse performance during simulation.
1. Strategies to Implement a Practice Domain Replicating the EHR Production Domain
To fully implement a simulation that replicates the EHR production domain, it is essential to create a realistic practice environment where nurses can learn to navigate EHR functionalities that mirror their clinical setting. One effective strategy is to employ simulation-based experiences that replicate actual clinical workflows, including the common challenges related to medication errors. According to Wilbanks and Aroke (2020), integrating EHRs into realistic, simulated clinical environments can significantly improve EHR-related skills and foster confidence in nurses, contributing to patient safety improvements.
Best practices in simulation-based learning suggest that activities should reflect the room setup, including interprofessional interactions and contextual elements like time constraints, interruptions, and other distractors, which mimic real-life environments (Mountain et al., 2015). The simulation center can also incorporate standardized patients or mannequins to simulate patient interactions, enhancing realism. This helps the nurses practice communication while documenting care accurately in the EHR, thus providing a holistic approach to patient safety and improving clinical judgment (Baxter & Andrews, 2018).
Moreover, faculty members play an essential role in the integration of EHR into simulation activities. Faculty "super users" with expertise in the EHR system should be involved to provide adequate training and guidance, which enhances the acceptance and effectiveness of academic EHR integration (Wilbanks et al., 2018). Faculty champions should support faculty development, ensuring that all instructors are comfortable using the system and able to instruct students effectively.
2. Evaluation of Nurses’ Performance: Tools and Methods
To evaluate nurses’ performance effectively during the simulation, a combination of direct observation, standardized assessment tools, and debriefing sessions can be employed. One such assessment tool is the Competency Assessment in Simulation of Electronic Health Records (CASE) tool, which has been designed to objectively evaluate EHR competencies within simulation settings. This tool uses a five-point Likert scale to assess ten competency domains, providing a structured way to score and comment on the documentation capabilities of nurses (McBride et al., 2020).
Another approach is to use simulation-based assessments with predefined clinical scenarios to evaluate both technical EHR skills and soft skills, such as communication and decision-making. Studies have shown that repeated EHR-based simulations improve the ability of healthcare professionals to recognize patient safety issues and enhance their EHR proficiency (Stephenson et al., 2014). After the simulation, debriefing sessions should be used to provide formative feedback, reinforcing both strengths and areas for improvement. This reflective process helps in embedding the learned skills into practice.
Assessment methods should also align with best practices for competency evaluation, including the Quality and Safety Education for Nurses (QSEN) framework, which emphasizes informatics competencies as integral to patient safety (Smith et al., 2007). By focusing on competencies like error mitigation, effective communication, and clinical decision-making, the evaluation process can ensure nurses develop the skills necessary to utilize EHR systems effectively.
Conclusion
By creating a realistic EHR production environment and employing robust evaluation tools such as the CASE tool, we can significantly enhance nurses' competencies in using EHR systems, thereby improving documentation accuracy and patient safety. Faculty support, realistic simulation scenarios, and structured assessment methods are critical to achieving these goals. These strategies collectively aim to close the gap between academic training and real-world EHR use, thus ensuring better patient outcomes.
References
Baxter, P. M., & Andrews, L. A. (2018). Successful integration of an academic electronic health record into the curriculum of an associate degree nursing program. Nursing Education Perspectives, 39(4), 250-252. https://doi.org/10.1097/01.NEP.0000000000000255
McBride, S., Tietze, M., Thomas, L., & Pierce, M. (2020). Competency Assessment in Simulation of Electronic Health Records (CASE) tool development. In Developing Competencies in Nursing for an Electronic Age of Healthcare.
Mountain, C., Redd, R., O’Leary-Kelly, C., & Giles, K. (2015). Electronic medical record in the simulation hospital: Does it improve accuracy in charting vital signs, intake, and output? Journal of Nursing Education, 54(3), 156-160. https://doi.org/10.3928/01484834-20150218-05
Smith, E., Cronenwett, L., & Sherwood, G. (2007). Current assessments of quality and safety education in nursing. Nursing Outlook, 55(3), 132-137. https://doi.org/10.1016/j.outlook.2007.02.005
Stephenson, E., Stephenson, M., & Sternberger, C. S. (2014). The use of electronic health record to improve patient safety: A literature review. Nursing Education Perspectives, 35(5), 294-298. https://doi.org/10.5480/13-1217.1
Wilbanks, B. A., & Aroke, E. N. (2020). Simulation-based EHR training: Improving EHR competencies and nursing care. Computers, Informatics, Nursing, 38(7), 320-327. https://doi.org/10.1097/CIN.0000000000000626