classwork: June 2025

Friday, 13 June 2025

AI based patient centered and bedside critical appraisal of medical evidence in real time.

Disclaimer - only for educational purposes and not recommending to rely on this for clinical decision making. ( just an educational demo). used perplexity Ai

here is output of test for testing the capability of AI based critical appraisal below and after this you may find a patient centered Realtime critical appraisal of evidence for a patient centered question (yet to be experimented and added).

prompt - do a critical appraisal of Aspirin and heparin in acute unstable angina in PICO format

output - Aspirin and heparin are cornerstone therapies in acute unstable angina, with evidence supporting their efficacy in reducing ischemic events but highlighting important considerations for clinical use.

Population

Adults diagnosed with acute unstable angina, typically presenting with recent episodes of chest pain at rest or with minimal exertion, often accompanied by ECG changes or elevated cardiac biomarkers1 25.

Intervention

Aspirin: 325 mg twice daily1 to four times daily2, initiated immediately upon diagnosis.
Heparin: Intravenous unfractionated heparin (1000 units/hour)1 or low-molecular-weight heparin (e.g., enoxaparin)46.

Comparison

Placebo1 2
Aspirin vs. heparin1
Aspirin + heparin vs. aspirin alone4 6

Outcomes

Myocardial Infarction (MI):
- Aspirin reduced MI risk by 50–70%1 5, with a 3% incidence vs. 12% in placebo1.
- Heparin alone showed even greater MI reduction (0.8% vs. 12% in placebo)1.
- Combination therapy provided no additive MI reduction over heparin alone16.
Refractory Angina:
- Heparin significantly reduced refractory angina (P = 0.002)1.
- Reactivation of angina occurred in 13% of heparin-only patients within hours of discontinuation, mitigated by concurrent aspirin3.
Mortality:
- Aspirin reduced cardiac death by 71%2.
- Heparin showed no mortality benefit alone but prevented rebound ischemia when combined with aspirin3.
Safety:
- Major bleeding occurred in 1.7–3.3% with heparin16.
- No significant bleeding risk with aspirin alone25.

Critical Appraisal

Validity:

RCT Design: Trials like 1 and 2 were double-blind, randomized, and placebo-controlled, minimizing bias.
Sample Size: Adequate power in larger studies (n = 4791, n = 5552), though smaller trials (e.g., n = 4033) had limited power for mortality outcomes.
Consistency: Meta-analyses4 5 confirmed aspirin’s benefit (33–50% risk reduction) and short-term heparin efficacy.

Limitations:

Short-Term Focus: Heparin’s benefits were transient, with no long-term mortality reduction46.
Rebound Ischemia: Heparin discontinuation without aspirin led to angina reactivation in 9.5 hours3.
Bleeding Risk: Combination therapy increased bleeding (3.3% vs. 1.7% for heparin alone)1.

Applicability:

Aspirin remains first-line due to its simplicity, safety, and long-term benefits5.
Heparin is reserved for high-risk patients but requires overlap with aspirin to prevent rebound events36.
Low-molecular-weight heparin (e.g., enoxaparin) offers similar efficacy with easier administration46.

Conclusion:
Aspirin is essential for all patients with unstable angina, while heparin provides short-term ischemic protection in high-risk cases. Combining both requires careful monitoring for bleeding, and transitioning to oral antiplatelets post-heparin is critical to avoid rebound ischemia1 35.

more to be updated.....

Wednesday, 11 June 2025

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

my article converted in a research paper using copilot (Microsoft) and references using perplexity ai. link to original article - classwork: CDSS module for EHRs based on FMEA (failure mode and effects analysis) of clinical guidelines

Suggested reading - UDHC, CBBLE, PAJR (more details and links to be updated) full article below - CDSS Module for EHRs Based on FMEA of Clinical Guidelines (Failure Mode and Effects Analysis for Safer Electronic Health Records)

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. ---

Monday, 9 June 2025

CDSS module for EHRs based on FMEA (failure mode and effects analysis) of clinical guidelines

Disclaimer:- written with help of ChatGPT.

FMEA = Failure Mode and Effects Analysis

EHR (Electronic Health Record) system that integrates FMEA insights to ensure safer, guideline-adherent patient centered asthma care.

Here's how:

*1. Embed Clinical Decision Support (CDS):*

- Prompt doctors to *prescribe ICS* when asthma severity meets criteria.

- Alert if dose is too low/high for age/severity.

- Remind providers to *schedule follow-ups*.

*2. Workflow Checklists:*

- Use structured forms to *ensure inhaler technique is taught*.

- Include mandatory fields for *trigger assessment & avoidance advice*.

*3. Automation & Reminders:*

- Auto-generate *follow-up appointments*.

- Send *educational materials* to patients via portal.

*4. Real-Time Error Prevention:*

- Prevent prescription submission if *key steps* (e.g., patient education, dose confirmation) are skipped.

- Use color-coded *risk indicators*.

*5. Analytics Dashboard:*

- Track compliance with asthma guidelines.

- Monitor for *frequent deviations* or errors.

Such an EHR would directly address the failure modes identified in your FMEA and improve both safety and outcomes.

Prompt - FMEA analysis of a clinical guideline for asthma

ChatGPT -

Here's an FMEA (Failure Modes and Effects Analysis) of a clinical guideline for asthma management, particularly focusing on key components like diagnosis, treatment, patient education, and follow-up. This helps identify potential failure points that could harm patient outcomes or healthcare processes.

FMEA for Clinical Guideline: Asthma Management

Step	Potential Failure Mode	Potential Effects of Failure	Potential Causes	Current Controls	Severity (S)	Occurrence (O)	Detection (D)	RPN (S×O×D)	Recommended Action
1. Diagnosis of asthma	Misdiagnosis (e.g., COPD mistaken for asthma)	Inappropriate treatment, poor control, adverse effects	Inadequate history, no spirometry	Clinical training, spirometry guideline	9	5	4	180	Mandatory spirometry for diagnosis; provider training
2. Severity classification	Underestimating severity	Inadequate therapy, frequent exacerbations	Poor symptom documentation, rushed consult	GINA severity criteria	8	4	3	96	Use checklists; digital decision support tools
3. Prescribing controller meds	Omission or incorrect dosage of inhaled corticosteroids (ICS)	Poor control, ER visits, exacerbation	Lack of knowledge, misinterpretation of stepwise therapy	Pharmacy check, EHR alerts	9	3	3	81	Incorporate guideline-based alerts in EMRs
4. Patient education	Inadequate inhaler technique education	Poor drug delivery, ineffective treatment	Time constraints, staff not trained	Nurse education, pamphlets	7	6	5	210	Hands-on demo + teach-back method; audit technique regularly
5. Follow-up planning	Missed or delayed follow-up	Poor long-term control, risk of severe attacks	Poor tracking, patient noncompliance	Appointment system	7	4	5	140	SMS reminders, follow-up flag in EMR
6. Emergency care plan	No written action plan for acute exacerbations	Delayed response in emergencies, hospitalization	Clinician oversight, lack of time	Verbal advice given	8	5	4	160	Mandate written action plans at diagnosis
7. Allergen/environmental control	Failure to advise on trigger avoidance	Continued exposure, symptom worsening	Lack of awareness, not assessed	Patient interview	6	4	4	96	Standard environmental history checklist

Key

Severity (S): Impact of failure (1 = low, 10 = catastrophic)
Occurrence (O): Likelihood of failure (1 = rare, 10 = frequent)
Detection (D): Likelihood that failure is detected before harm (1 = very likely, 10 = not likely)
RPN (Risk Priority Number): S × O × D

Top Priority Areas Based on RPN

Inhaler technique education (RPN 210)
Diagnosis without spirometry (RPN 180)
Lack of emergency action plan (RPN 160)

Let me know if you'd like this tailored to a specific setting (e.g., rural India, pediatric care, emergency departments), or if you want it as a PowerPoint/table PDF.