Week 6 informatics replies

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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