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 biomarkers125.

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

• Placebo12

• Aspirin vs. heparin1

• Aspirin + heparin vs. aspirin alone46

Outcomes

1. Myocardial Infarction (MI):

   • Aspirin reduced MI risk by 50–70%15, 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.

2. 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.

3. Mortality:

   • Aspirin reduced cardiac death by 71%2.

   • Heparin showed no mortality benefit alone but prevented rebound ischemia when combined with aspirin3.

4. 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-analyses45 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 ischemia135.

1. https://pubmed.ncbi.nlm.nih.gov/3050522/
2. https://pubmed.ncbi.nlm.nih.gov/3903504/
3. https://pubmed.ncbi.nlm.nih.gov/1608405/
4. https://www.ccjm.org/content/ccjom/67/1/59.full.pdf
5. https://pmc.ncbi.nlm.nih.gov/articles/PMC3236445/
6. https://pmc.ncbi.nlm.nih.gov/articles/PMC1071027/
7. https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/648496
8. https://www.msdmanuals.com/professional/cardiovascular-disorders/coronary-artery-disease/unstable-angina
9. https://emedicine.medscape.com/article/159383-treatment
10. https://www.ahajournals.org/doi/10.1161/CIR.0000000000001309
11. https://www.nice.org.uk/guidance/cg95/update/CG95/documents/chest-paindiscomfort-of-recent-onset-appendix-c-guideline-question2
12. https://www.ncbi.nlm.nih.gov/books/NBK565355/
13. https://www.nice.org.uk/guidance/ng185/documents/evidence-review-3
14. https://www.nhjournal.info/pdf/vol16no2/26319-Article%20Text-79160-1-10-20191117.pdf
15. https://usurj.journals.usask.ca/article/download/151/pdf/
16. https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/622195
17. https://emergencymedicinecases.com/journal-jam-heparin-acs-stemi/

more to be updated.....

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. ---

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

1. Inhaler technique education (RPN 210)

2. Diagnosis without spirometry (RPN 180)

3. 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.

Doctor's Moat Over Time, While Saving Lives

My town.

1960 - Local Vaidhya

- Moat: The only available healer within a 20 km radius.  

- Insight: Exclusivity based on access and trust in traditional knowledge.

1980 - First College-Educated Doctor (BAMS)  

- Moat: Higher education credentials in Ayurveda, standing out as the first formally trained doctor in town.  

- Insight: Early adopters of formal education create a new level of trust.

1980s - Public Health Center (PHC)

- Moat: Government-provided healthcare with free services.  

- Insight: Accessibility to formal care is democratized, but only for basic treatments.

1990 - Occasional Visiting MBBS Doctor

- Moat: Limited competition, as visiting MBBS doctors are rare in small towns.  

- Insight: Scarcity of modern medicine professionals retains exclusivity.

2000 - Local Mix of Doctors (BAMS, BHMS, MBBS)

- Moat: Relationships and trust-building become critical as the number of local doctors grows.  

- Insight: Personal connections with patients ensure loyalty amid growing competition.

2010 - Introduction of Specialists

- Moat: Specialized care draws patients to cities with an increasing density of clinics offering niche treatments.  

- Insight: Specialization and centralization begin to take hold, shifting focus away from generalists.

2020 - Patient Shift to City Centers

- Moat: Marketing, celebrity status, and referral commissions differentiate successful doctors in competitive urban environments.  

- Insight: Patients opt for expertise over convenience, even battling to get appointments.

2025 - Arrival of Large Corporate Hospital Franchises 

- Moat: Comprehensive care under one roof with standardized quality and reputation.  

- Insight: Corporate setups cater to the patient’s need for assurance, convenience, and variety in one place.

2030 - Corporate Competition Intensifies

- Moat: Modular, process-based setups emphasize efficiency, insurance funding, marketing, and referral networks.  

- Insight: Patient interaction with the system rather than specific doctors; process consistency becomes key.

2035 - AI and Automation in Healthcare  

- Moat: Cost-cutting and speed via automation ensure higher patient flow without compromising care quality. Patients who desire more human interaction pay a premium for personal expertise.  

- Insight: The system becomes highly efficient, but personalized care turns into a luxury, available only at a higher cost.

2040 - The Future of Healthcare

Let see how it develops in coming years

Just another LLM analytics but for our highly complex case of a rare unidentified illness.

 Keywords

- Clinical complexity

- Undiagnosed illness

- Slow medicine

- Narrative History

Access detailed case record here - https://classworkdecjan.blogspot.com/2019/05/42-f-with-severe-regular-edema-with_17.html

Summary of analysis

Giving my opinion as scoring for performance of ChatGPT along with consideration of bias (limitation) that it have to reply in just a few short paragraphs.

Thematic analysis - 3/5

Critical analysis - 3/5

Research questions - 2/5

Subjective ratings, cutting marks for 

- losing themes of emotional pain

- critical analysis no evidence/ insights about any interventions

- no new Research questions other than what's directly noticeable in text.

Giving marks for

- good summary and prioritization of themes.

- covering wide range of important aspects in critical analsys.

- able to formulate research questions that are prioritized in text.

Usability -

- For usefulness to get clinical understanding/outline quickly - 4/5.

- For usefulness to get actionable (preferably evidence backed) insight - 1/5

Weakness - brute force strategy of summarizing literature, but no individualized exploration.

For speed - 100/5 🤣

Cbble/pajr/udhc model targets

Avinash - What will be this called in management/MBA terms?

1) Listing all key stakeholders in an industry

2) then listing top 5-10 problems they face

3) then Plotting the interconnections with common gaps

Eg. Money, man, machine

4) finally Plotting an interconnection of resource sharing?

Eg. Unskilled MBBS degree holders joining electives to become high skilled care provider.

Avinash - Like amazon does resource optimization to enable end user to get best, fastest and cheapest while also enabling sellers.

Although two side effects are

- small sellers who don't upgrade to better business goes towards eradication 

- those on top gets too much power and control

(Both are like survival of fittest on game of life /evolution)

Avinash- Lack of availability of this training is a gap

Lack of demand may also be a gap

Connecting pain points may give directions to fill the gaps.

Avinash - A diagram like this may be very interesting

Covn.org

(this covenant project was one of a few that i tried during covid)

Avinash- Many startups are coming in healthcare that do these connection of resources.

Avinash- Found answer from chat gpt, its

*Stakeholder Analysis and Resource Mapping.*

Avinash- Wow..chatgpt did a very good effort to give direction.. answer copied below 

Certainly! Here’s a high-level Stakeholder Analysis and Resource Mapping for healthcare in India:

### 1. Stakeholder Identification

**Key Stakeholders in Indian Healthcare:**

- **Patients**: General public, rural and urban populations, specific patient groups (e.g., chronic disease sufferers).

- **Healthcare Providers**: Doctors, nurses, paramedics, and other healthcare professionals.

- **Healthcare Institutions**: Hospitals (public and private), clinics, primary health centers.

- **Government Agencies**: Ministry of Health and Family Welfare, state health departments, regulatory bodies (e.g., Medical Council of India).

- **Pharmaceutical Companies**: Manufacturers, distributors, and retailers of medicines and medical supplies.

- **Medical Device Manufacturers**: Producers of medical equipment and technology.

- **Insurance Companies**: Public and private health insurers.

- **Non-Governmental Organizations (NGOs)**: Organizations working in healthcare awareness, patient support, and public health initiatives.

- **Educational Institutions**: Medical colleges, nursing schools, and training institutes.

- **Technology Companies**: Providers of healthcare IT solutions, telemedicine services, and health data analytics.

### 2. Stakeholder Needs and Issues Assessment

**Top Problems Faced by Stakeholders:**

- **Patients**: Accessibility to quality healthcare, affordability, awareness of health issues, and waiting times.

- **Healthcare Providers**: Workload and burnout, inadequate infrastructure, lack of continuous professional development, and compensation issues.

- **Healthcare Institutions**: Funding, staffing shortages, outdated technology, and administrative inefficiencies.

- **Government Agencies**: Policy implementation challenges, corruption, resource allocation, and monitoring healthcare quality.

- **Pharmaceutical Companies**: Regulatory hurdles, counterfeit drugs, supply chain issues, and R&D costs.

- **Medical Device Manufacturers**: Market penetration, regulatory compliance, and technological advancement.

- **Insurance Companies**: Fraudulent claims, customer education, and regulatory changes.

- **NGOs**: Funding, scalability of initiatives, and collaboration with government and private sector.

- **Educational Institutions**: Quality of education, updating curriculum, and bridging theory-practice gap.

- **Technology Companies**: Adoption of technology, integration with existing systems, and data privacy concerns.

### 3. Gap Analysis and Interconnection Mapping

**Common Gaps and Interconnections:**

- **Funding**: Shortage of funds for hospitals, research, and public health initiatives. Potential for public-private partnerships.

- **Manpower**: Shortage of trained medical professionals. Collaboration between educational institutions and healthcare providers for better training.

- **Infrastructure**: Outdated or insufficient healthcare infrastructure. Technology companies can provide innovative solutions.

- **Accessibility**: Rural populations face significant barriers. NGOs and government programs can work together to improve reach.

- **Regulation**: Complex regulatory environment. Streamlining processes could help pharmaceutical and medical device companies.

- **Awareness**: Lack of health awareness among the population. Educational initiatives by NGOs, government, and private sector can bridge this gap.

### 4. Resource Sharing and Collaboration Mapping

**Examples of Resource Sharing:**

- **Medical Training**: Partnerships between educational institutions and healthcare providers to enhance the skills of medical graduates. For example, unskilled MBBS degree holders can join electives and residencies to become high-skilled care providers.

- **Technology Integration**: Collaborations between technology companies and hospitals to implement electronic health records (EHRs), telemedicine, and health data analytics.

- **Public-Private Partnerships**: Joint ventures to build and upgrade healthcare infrastructure, especially in rural areas.

- **Pharmaceutical Collaboration**: Joint R&D initiatives between pharmaceutical companies and academic institutions to develop new treatments.

- **NGO and Government Collaboration**: NGOs can assist in implementing government health programs, increasing reach and effectiveness.

### Visualization

Creating detailed maps and diagrams to visualize the stakeholder interconnections and resource sharing can help in understanding the complex relationships and identifying areas for improvement. Tools such as stakeholder maps, gap analysis charts, and resource flow diagrams can be useful for this purpose.

By performing this comprehensive analysis, stakeholders can better understand their roles, the challenges they face, and the opportunities for collaboration and resource optimization in the Indian healthcare sector.

Dr. Rb - 👏👏

Archive this entire thing beginning from your question at 11:03 till the LLM response at 11:27

and share the link so that we can cite it in our own write up

29m quantified self pajr - 3

 1) [28/04, 7:43 pm] Dr.Dinesh Datta: Check out concept of SDA(Specific Dynamic Action)

[28/04, 7:50 pm] Dr. Avinash Kumar Gupta: Specific dynamic action (SDA), also known as the thermic effect of food (TEF), refers to the energy expenditure associated with the digestion, absorption, and metabolism of food. Essentially, it's the energy your body uses to process the food you eat. Different macronutrients have different SDA rates. For example, protein has a higher SDA compared to fats or carbohydrates. This means that your body burns more calories digesting and metabolizing protein compared to other nutrients.

[28/04, 7:50 pm] Dr. Avinash Kumar Gupta: How clinician can use this info for this case?

[28/04, 8:02 pm] Dr.Dinesh Datta: Basically..

BMR is the energy your body needs to function at rest.SDA(aka thermic effect/dietary induced thermogenesis) is extra energy your body burns to process food.It covers digestion,absorption and storage of nutrients from what you eat.Proteins have high SDA,requiring more energy to process compared to carbs and fats.

Sda is another layer over BMR in understanding total energy expenditure in body.

Total energy expenditure is BMR+SDA+Activity level

Basically we say,bmr is engine size of your metabolism and sda is fuel efficiency.

By understanding TEE,we can tailor/better understand weight gain/loss

[28/04, 8:03 pm] Dr. Avinash Kumar Gupta: Useful

2) To calculate your Basal Metabolic Rate (BMR), we can use the Harris-Benedict equation. 

For males:

BMR = 88.362 + (13.397 × weight in kg) + (4.799 × height in cm) - (5.677 × age in years)

First, we convert your height to centimeters:

5 feet 5 inches = 165.1 cm (1 foot = 30.48 cm, 1 inch = 2.54 cm)

Now, plug in the values:

BMR = 88.362 + (13.397 × 81.5) + (4.799 × 165.1) - (5.677 × 30)

BMR ≈ 88.362 + 1093.955 + 791.334 - 170.731

BMR ≈ 1803.92 calories per day

So, your Basal Metabolic Rate (BMR) is approximately 1803.92 calories per day. This is the number of calories your body needs to maintain basic physiological functions at rest.

3) [19/04, 7:06 am] Dr. Avinash Kumar Gupta: Cgpt

The estimation of calories burned during activities like walking involves several scientific methods and studies. The calorie expenditure for an activity like walking is typically determined through experiments that measure energy output in controlled settings. Here’s a brief outline of how scientists might arrive at such data:

1. **Metabolic Measurements**: One common method to determine calories burned is through direct and indirect calorimetry. Direct calorimetry measures the amount of heat the body emits, but it’s complex and not widely used. Indirect calorimetry is more common and measures oxygen consumption and carbon dioxide production. Since the body needs oxygen to burn nutrients for energy, and produces carbon dioxide as a byproduct, measuring these gases can give a reliable estimate of energy expenditure.

2. **Using Metabolic Equivalents (METs)**: The concept of METs (Metabolic Equivalents) is often used. One MET is defined as the energy it takes to sit quietly, equivalent to a calorie expenditure of 1 kcal/kg/hour. Physical activities are assigned MET values based on how much harder your body works relative to sitting quietly. For example, walking at a mild pace (about 3-4 mph) typically has a MET value of around 3.0 to 4.5 METs.

3. **Calculations Based on METs**:

   - First, the MET value is multiplied by the person’s body weight in kilograms.

   - Then, this number is multiplied by the duration of the activity in hours to find the total caloric expenditure.

   For instance, if walking at a mild pace has a MET value of 3.5, an 80 kg person walking for 1 km (which they might complete in about 10-15 minutes, or about 0.2 hours), the calculation would be:

   \[

   \text{Calories burned} = 3.5 \text{ METs} \times 80 \text{ kg} \times 0.2 \text{ hours} = 56 \text{ kcal}

   \]

   (Note: This is just an example; actual values might vary based on the specific speed and individual metabolism).

4. **Field Studies and Adjustments**: Scientists also conduct field studies where they monitor real-life activities rather than controlled laboratory settings. They might use portable devices that track physiological responses like heart rate, breathing rates, and even sweat rates to estimate energy expenditure more accurately in day-to-day activities.

5. **Calibration and Validation**: Any method used to calculate or estimate caloric expenditure is usually calibrated against more established methods (like those using laboratory equipment) and validated in different populations to ensure accuracy across age, body weight, and fitness levels.

When it comes to the provided data — an 80 kg person burning 80 calories walking 1 km — this might be a slightly simplified or rounded figure used for easy calculation. In actuality, the calories burned can be influenced by factors like walking speed, terrain, efficiency of movement, and individual metabolic differences. The figure could be derived from an average or estimated value that broadly fits the profile and activity described.

[19/04, 7:28 am] Dr. Avinash Kumar Gupta: "Definition

A MET is defined as the resting metabolic rate, that

is, the amount of oxygen consumed at rest, sitting quiet-

ly in a chair, approximately 3.5 ml 02/kg/min (1.2

kcallmin for a 70-kg person).* As such, work at 2 METS

requires twice the resting metabolism or 7.0 ml O,/kg/min

and three METS requires three times the resting

metabolism (10.5 ml 02/kg/min), and so on"