July 12 2025

The Phrase That Silenced Me — “No Acute Distress”

By Neeley Jo Minor | Estimated read time: 4 mins

What doctors wrote in my records didn’t match what I lived—and it cost me time, treatment, and truth.

🔍 What This Post Covers

What “No Acute Distress” really means
Why it shows up even when you’re in pain
How to address it in an addendum

🧠 Quick Summary

“No Acute Distress” is a default line often used by doctors even when you’re crying or reporting pain. It reflects their observation, not your experience.

🧾 Real Example

Doctor’s Note: “Pleasant, no acute distress”

Reality: I was sobbing, in pain, and terrified of a possible rupture.

✅ What You Can Do Today

Request a copy of the visit notes
Highlight the “no acute distress” line
Use our template to submit a correction

📣 Advocate With Us

If you’ve ever been misdocumented or dismissed, your voice matters. Share your story with EnBloc AI™ and let’s build change.

June 4 2025

neeleyjo21@gmail.com Uncategorized 0

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June 4 2025

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https://stop-the-sound-of-madness.my.canva.site/exploring-immune-cell-imbalance-in-sinusitis-and-autoantibody-production by neeley jo studio

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June 4 2025

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EnBloc AI™ Research Brief

Title: Comparing Bias Reduction Strategies to Improve Healthcare AI Fairness

Author: Neeley Jo Minor | Founder, EnBloc Co™

🧠 Introduction

Healthcare AI has immense potential to advance diagnosis, treatment, and access. However, algorithmic bias can lead to harmful disparities—especially for women, racial minorities, and individuals with chronic illness. EnBloc AI™ is committed to identifying and integrating effective bias reduction strategies to ensure fair, accountable AI.

🗞️ The Neeley Daily: What Went Wrong—Day After Day

“I felt like I was dying. Week after week, I begged for help, but the doctors wouldn’t listen. When I finally found out what was wrong—it was buried in my records the whole time. And still… they said it wasn’t urgent.”

This is the lived reality of too many patients—especially women—who face dismissal, delayed diagnoses, and medical gaslighting. EnBloc AI™ is designed to bring these stories forward, uncover what’s hidden in the documentation, and transform ignored suffering into truth-telling power.

If you’ve ever been told “it’s all in your head,” you’re not alone.

Our tools help you decode your records, fix the notes, and reclaim your health story.

📝 Health Note

Keeping track of your medical records is not just about paperwork—it’s about protection, pattern recognition, and power.

Here’s what to review regularly:

SOAP Notes (Subjective, Objective, Assessment, Plan)
Specialist summaries
Lab result trends (especially inflammation and immune markers)
Missed or altered symptoms in visit notes
Referrals and follow-up timelines

EnBloc AI™ helps you:

Spot hidden patterns in autoimmune flares
Correct false or missing notes
Compare symptom clusters with known triggers
Track your health history visually

Remember: Your story matters. Your notes matter more.

🩺 Why Medical Notes Matter™

Your medical notes shape your future care. Every word, omission, and miscode can:

Delay diagnosis
Deny proper treatment
Undermine insurance or legal action
Affect how every new provider sees you

EnBloc AI™ exists to make sure your notes tell the truth about what you’ve lived through.

We believe:

📌 If it’s not documented, it didn’t happen.
🧠 Bias in records leads to bias in care.
📣 Fixing your notes can save your life—and someone else’s.

#MedicalNotesMatter is more than a hashtag. It’s a movement. Join us.

📉 Strategy Comparison Table

Strategy Type	Description & Methods	Strengths	Weaknesses	Impact on Fairness & Performance
Pre-processing	Modifies training data (e.g., resampling, reweighting)	Easy to apply before training; addresses data imbalance	May slightly lower accuracy	Improves fairness; useful when retraining model is difficult
In-processing	Adds fairness constraints or training methods (e.g., adversarial learning)	Directly optimizes fairness; can tailor per subgroup	Requires more resources and group data	Strongest fairness gains; small performance tradeoffs
Post-processing	Adjusts predictions or thresholds after model training	Works on deployed models; minimal disruption	Limited by pre-existing model bias	Helpful for tuning; less effective on deep-rooted bias
Human-in-the-loop	Human reviews and feedback at key AI development points	Adds nuance, context, and ethical oversight	Time and resource intensive	Enhances trust and captures subtle bias
Transparency	Model explainability and decision traceability	Builds trust; allows for bias identification	Doesn’t fix bias alone; explanations may be unreliable	Supports fairness through auditability and accountability
Stakeholder Input	Involves patients, advocates, and clinicians throughout design and deployment	Ensures real-world relevance and equitable goals	Slower development; resource heavy	Critical for ethical, inclusive AI

🔍 Key Research Takeaways

In-processing shows the greatest ability to equalize outcomes across subgroups.
Pre-processing is ideal when model access is limited, but risks performance tradeoffs.
Post-processing is fast but less effective alone.
Human and stakeholder engagement is critical for ethical AI, especially in healthcare.

✅ Practical Recommendations

Combine pre-, in-, and post-processing strategies for layered fairness.
Integrate clinicians, patients, and advocates from design to deployment.
Maintain auditable logs and explainability tools.
Regularly audit and retrain models to correct emerging bias.

📈 Real-World Examples

Strategy Type	Application Example	Outcome
Pre-processing	Reweighting EHR data by race	Improved fairness with slight accuracy loss
In-processing	Stratified cardiac imaging training	Equalized performance across groups
Post-processing	Calibrated thresholds on risk scores	Some fairness improvement, limited scope
Human oversight	Clinician review of flagged predictions	Detected edge cases, increased trust
Stakeholder input	Patient advocate involvement in model rollout	Higher patient alignment + trust

💜 Conclusion

Improving fairness in healthcare AI is not about choosing one perfect method—it’s about combining technical solutions with human-centered oversight. The EnBloc AI™ approach incorporates layered fairness strategies and community collaboration to bridge healthcare bias and empower better outcomes for all.

Visit: EnBloc.org Contact: support@enbloc.org #BridgeTheBias #FairAI #HealthcareEquity #MedicalNotesMatter

Medical Notes Matter

Medical Notes Matter: Using AI to Bridge Healthcare Bias

Discover how EnBloc AI™ combines AI, storytelling, and patient power to fight medical bias and transform health records into advocacy tools.

Start Fixing Your Notes Today

Published: May 15, 2023|Reading time: 6 min

Medical Notes MatterUsing AI to Bridge Healthcare Bias

When Sophia Neeley received her medical records after a spinal cord injury, she discovered something alarming: her symptoms were repeatedly downplayed, her pain dismissed as “anxiety,” and critical information about her ASIA impairment scale was missing entirely.

This wasn’t just poor documentation—it was a pattern that could affect her treatment, insurance coverage, and long-term care. Unfortunately, Sophia’s experience isn’t unique.

The Hidden Crisis in Medical Documentation

Medical notes serve as the official record of a patient’s health journey, but they’re often riddled with biases that disproportionately affect women, people of color, and those with disabilities. These biases can manifest as:

Minimizing symptoms and pain levels
Attributing physical symptoms to psychological causes
Omitting critical diagnostic information
Using stigmatizing language that affects future care

“Your medical record isn’t just documentation—it’s the story that follows you through every healthcare interaction for the rest of your life. Make sure it’s accurate.”

Introducing EnBloc AI™: Your Medical Advocate

EnBloc AI™ is a groundbreaking tool designed to help patients identify, understand, and address potential biases in their medical records. Using advanced natural language processing, it:

How EnBloc AI™ Works:

Analyzes your medical notes for language patterns associated with bias
Identifies missing information critical to your diagnosis and treatment
Suggests evidence-based corrections you can request from your provider
Generates a professional amendment letter you can submit

The Impact of Biased Documentation

Biased Documentation	Potential Consequences	EnBloc AI™ Solution
“Patient appears anxious about pain”	Pain undertreated, psychological focus	Flag psychologizing language, suggest objective pain documentation
Missing ASIA impairment scale	Incomplete treatment, insurance denial	Identify missing clinical assessments, generate amendment request
“Non-compliant with treatment”	Stigma affecting future care	Suggest neutral alternatives that capture context

Real Stories, Real Impact

Sophia’s Story

After discovering critical omissions in her spinal cord injury documentation, Sophia used EnBloc AI™ to generate an amendment request that included:

Proper documentation of her ASIA impairment scale
Correction of dismissed symptoms later confirmed by imaging
Removal of speculative psychological attributions

Result: Complete revision of her medical record, approval of previously denied treatments, and a formal apology from her healthcare provider.

Taking Control of Your Medical Narrative

Your medical record is your story—and you have both the right and responsibility to ensure it’s accurate. Here’s how to get started:

1. Request

Request your complete medical records from all providers

2. Review

Use EnBloc AI™ to analyze your records for bias and omissions

3. Respond

Submit amendment requests using our templates

Ready to Take Action?

Join thousands of patients who are transforming their healthcare experience by ensuring their medical records accurately reflect their reality.

Download the Free EnBloc AI™ Toolkit

Join The Neeley Daily Community

Get updates, tools, and real stories that drive change in healthcare.

#EnBlocAI#MedicalNotesMatter#PatientAdvocacy#ASIAAwareness

Sophia Neeley

Patient advocate, healthcare equity researcher, and founder of EnBloc AI™. Sophia’s work focuses on using technology to empower patients and address systemic bias in healthcare.

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June 4 2025

EnBloc AI™ Research Brief

neeleyjo21@gmail.com Uncategorized 0

Comparing Bias Reduction Strategies to Improve Healthcare AI Fairness

Author: Neeley Jo Minor | Founder, EnBloc Co™

🧠 Introduction

Text content

📉 Strategy Comparison Table

Strategy Type	Description & Methods	Strengths	Weaknesses	Impact on Fairness & Performance
Pre-processing	Modifies training data (e.g., resampling, reweighting)	Easy to apply before training; addresses data imbalance	May slightly lower accuracy	Improves fairness; useful when retraining model is difficult
In-processing	Adds fairness constraints or training methods (e.g., adversarial learning)	Directly optimizes fairness; can tailor per subgroup	Requires more resources and group data	Strongest fairness gains; small performance tradeoffs
Post-processing	Adjusts predictions or thresholds after model training	Works on deployed models; minimal disruption	Limited by pre-existing model bias	Helpful for tuning; less effective on deep-rooted bias
Human-in-the-loop	Human reviews and feedback at key AI development points	Adds nuance, context, and ethical oversight	Time and resource intensive	Enhances trust and captures subtle bias
Transparency	Model explainability and decision traceability	Builds trust; allows for bias identification	Doesn’t fix bias alone; explanations may be unreliable	Supports fairness through auditability and accountability
Stakeholder Input	Involves patients, advocates, and clinicians throughout design and deployment	Ensures real-world relevance and equitable goals	Slower development; resource heavy	Critical for ethical, inclusive AI

June 3 2025

📣 My Breast Implant Imaging Was Ignored — But the Pictures Tell the Truth

neeleyjo21@gmail.com Uncategorized #AIForPatients, #BIIAwareness, #BreastImplantIllness, #BreastImplantRupture, #ChronicIllnessWarrior, #CTScanAwareness, #DecodeYourSymptoms, #EnblocAI, #EnblocCo, #EnblocSurgery, #ExplantJourney, #ExplantSupport, #HealthLiteracy, #ImplantSafety, #ImplantTruth, #InvisibleIllness, #LowLiteracyAdvocacy, #MammogramAwareness, #MedicalGaslighting, #MedicalNotesMatter, #MedNeeley, #MRIResults, #PatientAdvocacy, #RadiologyRedFlags, #SiliconeImplants, #SimplyNeeley, #SubcapsularLineSign, #TrustYourBody, #WomenInMedicine 0

📣 My Breast Implant Imaging Was Ignored — But the Pictures Tell the Truth

By Neeley Jo Minor | #MedicalNotesMatter Series

When I first got breast implants, I never expected that one day I’d have to decode my own medical records just to be heard.

But that’s exactly where I found myself—alone, exhausted, and dismissed. Doctors told me my pain was “in my head,” even while I struggled with symptoms like fatigue, brain fog, chest pain, and burning nerve sensations.

So I started looking for answers the only place I knew they might still exist: the images.

🖼️ What My Imaging Showed

After years of symptoms, I reviewed two major scans:

1️⃣ CT/MRI Scan – April 2021

Finding: A clear “subcapsular line sign”—a well-known indicator of intracapsular silicone rupture.
What It Means: Silicone had leaked inside the scar capsule surrounding the implant. This is a documented rupture, even if it’s not “emergency level” yet.

2️⃣ Mammogram – May 2021

Finding: Distorted implant shape, shadowing, and unclear breast tissue density.
What It Means: The scan couldn’t read the breast volume or tissue properly—because the implant blocked visibility. The system literally couldn’t calculate it.

And yet… no one called me.
No follow-up.
No plan.

🚩 The Problem with Being “Too Complicated”

Because I had implants, my scans became “technically difficult.”
Because I had symptoms “that didn’t fit one diagnosis,” I was told I was anxious.
Because I questioned it, I was seen as noncompliant.

This is medical gaslighting. And it’s far too common for women with implants.

💬 Why This Matters for You

If you have breast implants and feel something is wrong, trust yourself. Your body knows.

Start asking:

❓ What did my last scan actually show?
❓ Was my breast density readable?
❓ Is there a report of “shell collapse,” “subcapsular line,” or “distortion”?

You may be able to spot red flags before your doctor ever mentions them.

🔎 The Real Cost of Being Ignored

A ruptured implant isn’t just cosmetic. It can:

Trigger chronic inflammation
Aggravate mast cell activation
Mimic autoimmune diseases
Cause neurological issues
Be hard to remove once the silicone leaks further

This isn’t just about looks. This is about life.

💡 Takeaway: You Deserve to Be Taken Seriously

Your symptoms are real. Your imaging matters.
And your voice—especially when backed by evidence—is powerful.

That’s why I started sharing my own scans. Not to scare anyone. Not to shame doctors.
But to help someone else realize:

“If no one else is listening—your images still are.”

🧠 Related Posts to Read Next:

🩻 What the Subcapsular Line Sign Means (And Why Doctors Miss It)
📸 Breast Density, Implants, and Missed Diagnoses
🧾 How to Write an Addendum Request for Your Medical Record

💌 Ready to Advocate?

Download my Medical Notes Matter Checklist or use my free templates to review your own records.
Follow @EnblocAI or [@SimplyNeeley] for tips, templates, and real truth from real women.

📣 Because we are not too complicated.
We’re just tired of being ignored.

Welcome This is our story

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