NURS FPX 8022 Assessment 2 SAFER Guides and Evaluating

NURS FPX 8022 Assessment 2 SAFER Guides and Evaluating

• Student name
• Capella University
• NURS FPX 8022 Assessment 2
• Professor Name
• Submission Date

SAFER Guides and Evaluating Technology Usage

The assessment focuses on examining the readiness of MGH to integrate predictive tools for the identification of sepsis and fall risk into the EHR functions. The assessment helps identify organizational strengths and areas of possible gaps, as well as demonstrates how the SAFER framework can be applied to enhance informatics capabilities so as to maximize safety performance and guarantee strong, patient-focused care delivery.

SAFER Guides Findings Related to Risks of Technology

The SAFER framework identifies a number of areas at Massachusetts General Hospital where AI-powered informatics in the EHR are not optimally enabled. The AI-based CDS has multiple benefits for the hospital (Cabello et al., 2024). However, one domain classified as “Not Implemented” is the system interfaces and interoperability.

Experiences of Using SAFER Guides

Looking back on the application of the SAFER guides to assess technology at MGH, the process was discovered to be both systematic and enlightening.

Conclusion

Applying the SAFER framework to Massachusetts General Hospital’s adoption of AI-powered predictive analytics in EHR both reveals strong foundations and telling gaps. Although the hospital shows strength in governance, system configuration, and evidence-based clinical processes, interoperability, workflow integration, and cybersecurity remain areas of challenge.

The SAFER guides were useful in revealing risks and prioritization areas for improvement, with the assurance that technology-driven change would be both safe and sustainable. Through filling the gaps, MGH can optimize the effect of informatics innovation, improve patient safety, and solidify organization’s reputation as a quality, patient-centered care leader.

References

• Cabello, C. A. G., Borna, S., Pressman, S., Haider, S. A., Haider, C. R., & Forte, A. J. (2024). https://doi.org/10.3390/ejihpe14030045
• Elhaddad, M., & Hamam, S. (2024). https://doi.org/10.7759/cureus.57728
• Kushniruk, A., & Kaufman, D. (2024). https://doi.org/10.1055/s-0044-1800744
• Mennella, C., Maniscalco, U., Pietro, G. D., & Esposito, M. (2024). https://doi.org/10.1016/j.heliyon.2024.e26297
• MGH. (n.d.-a). https://www.massgeneral.org/quality-and-safety
• MGH. (n.d.-b). https://www.massgeneral.org/news/press-release/electronic-health-records-can-be-a-valuable-predictor-of-those-likeliest-to-die-from-covid19
• Sittig, D. F., Sengstack, P., & Singh, H. (2022). https://doi.org/10.1001/jama.2022.0085