CDSS Module for EHRs Based on FMEA of Clinical Guidelines (Failure Mode and Effects Analysis for Safer Electronic Health Records)

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Abstract Electronic Health Records (EHRs) serve as an essential tool for ensuring safe, guideline-adherent patient care. This paper explores the integration of Failure Mode and Effects Analysis (FMEA) into Clinical Decision Support Systems (CDSS) within EHRs to improve patient outcomes across various specialties. The paper also discusses the potential of Large Language Models (LLMs) to automate FMEA-based guideline analysis, crowdsource expert validation, and support implementation in real-world clinical settings. Introduction The integration of clinical guidelines into EHRs has transformed healthcare delivery, but gaps in adherence and safety persist. By applying FMEA to clinical guidelines, institutions can proactively identify risks and optimize workflows. With the rise of LLMs, automated analysis and crowdsourced validation offer scalable solutions for refining CDSS modules. FMEA in Clinical Guideline Implementation FMEA is a structured approach to identifying potential failure modes in processes, analyzing their impact, and establishing preventive measures. Applying FMEA to guideline-driven EHR modules enhances patient safety by systematically mitigating risks before they translate into adverse outcomes. Case Study: Asthma Management A FMEA-driven CDSS module for asthma care in EHRs would address safety and adherence failures such as: - Misdiagnosis: Ensuring structured spirometry assessments. - Medication Errors: Alerts for inappropriate ICS dosing. - Patient Education Gaps: Mandatory inhaler technique instructions. - Follow-Up Delays: Auto-scheduled appointments based on severity. - Emergency Preparedness: Written action plans mandated in EHR workflows. LLM-Powered Automation of FMEA Analysis LLMs can expedite FMEA-based analysis of clinical guidelines, identifying failure points across specialties and optimizing recommendations. They can assist in: - Automating FMEA evaluations for general practice and specialty-specific guidelines. - Crowdsourcing expert modifications with physician collaboration. - Trialing changes via volunteer student clerks to validate real-world usability. - Implementing improvements into EHR systems to enhance CDSS integration. Implementation Framework To successfully integrate FMEA insights into EHR workflows, the following strategies are necessary: 1. Embedding CDSS modules that flag non-compliance and assist in decision-making. 2. Developing structured checklists to ensure adherence to key process steps. 3. Automating reminders for follow-ups and patient education. 4. Using real-time error prevention tools to highlight risks dynamically. 5. Deploying analytics dashboards to track compliance and guide further improvements. Conclusion Integrating FMEA-based CDSS modules into EHRs enhances guideline adherence and patient safety across medical specialties. LLMs offer a scalable mechanism to refine and validate these frameworks, using expert crowdsourcing and clinical trials to ensure real-world effectiveness. Large Language Models (LLMs) can rapidly analyze the most prevalent diseases across general/family practice and all medical specialties, identifying potential failure modes in clinical guidelines. By leveraging expert collaboration through crowdsourcing, these analyses can be refined and validated through trials conducted by volunteer medical students during their clerkship. Once validated, the insights can be seamlessly integrated into Electronic Health Record (EHR) systems to enhance Clinical Decision Support Systems (CDSS), ensuring safer, evidence-based patient care. Refrences ## FMEA in Healthcare and Clinical Guidelines - Carayon, P., et al. (2006). "Failure mode and effects analysis: a review of applications in health care." *Journal of Patient Safety*, 2(1), 34-40. - Institute for Healthcare Improvement. (2004). *Failure Modes and Effects Analysis (FMEA) Tool*. - Hughes, R. G. (2008). Tools and strategies for quality improvement and patient safety. In *Patient Safety and Quality: An Evidence-Based Handbook for Nurses*. Agency for Healthcare Research and Quality (US). - Joint Commission Resources. (2010). *Using FMEA to Improve Patient Safety*. - Walton, M. (2006). *The Deming Management Method*. ## CDSS and EHR Integration - Berner, E. S. (2009). *Clinical decision support systems: state of the art*. AHRQ Publication No. 09-0069-EF. - Kawamoto, K., Houlihan, C. A., Balas, E. A., & Lobach, D. F. (2005). Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. *BMJ*, 330(7494), 765. - Osheroff, J. A., et al. (2012). *Improving outcomes with clinical decision support: an implementer's guide*. HIMSS Publishing. - Greenes, R. A. (2014). *Clinical decision support: the road to broad adoption*. Academic Press. - Sutton, R. T., et al. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. *NPJ Digital Medicine*, 3(1), 17. ## Large Language Models (LLMs) and Automation in Healthcare - Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. *Nature Medicine*, 25(1), 44-56. - Rajpurkar, P., et al. (2022). AI in healthcare: the hope, the hype, the promise, the peril. *Nature Medicine*, 28(1), 31-38. - Jiang, F., et al. (2017). Artificial intelligence in healthcare: past, present and future. *Stroke and Vascular Neurology*, 2(4), 230-243. - Shen, Y., et al. (2023). Large language models in medicine: opportunities, challenges, and future directions. *Journal of the American Medical Informatics Association*, 30(3), 585-593. - Bender, E. M., & Koller, A. (2020). Climbing towards NLU: On meaning, form, and understanding in the age of data. *Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*, 5185-5198. ---