From Symptoms to Codes

Subject: Your Health Information (Blog Post 2 of 4)

From Your Words to a Diagnosis Code: What Happens After You Describe Your Pain

Imagine you’re Maria. You’re 42, and for three years you’ve had pain in your shoulders, hips, and back—pain that moves, that’s hard to explain, that’s worse when you haven’t slept. You’re exhausted in a way that a good night’s rest doesn’t fix. At work, you lose words, lose your train of thought, lose yourself in a brain fog that frustrates and embarrasses you.

You describe all of this to your doctor over 20 minutes. She listens carefully. She asks about your mood, checks your joints, orders some bloodwork to rule out thyroid problems and inflammatory arthritis. Then you leave.

What happens next?

Your doctor spends another 15 minutes at her computer—documenting the visit, selecting a billing level, reconciling your medication list, and entering a diagnosis code: M79.7. Fibromyalgia.

That code is four characters. It will follow you through the healthcare system for years.

What is a diagnosis code, and why does it exist?

ICD-10 codes—the International Classification of Diseases, now in its 10th revision—are a standardized language used across medicine to name conditions. Every diagnosis has a code. M79.7 is fibromyalgia’s.

These codes serve several purposes: they enable billing (insurers need to know what they’re paying for), they feed into research and public health data, and they trigger certain administrative processes—prior authorizations, referral approvals, quality reporting.¹⁴

For a condition like fibromyalgia, the code is an act of translation. Your 25 minutes of vivid description—the specific locations of pain, the fatigue that sits behind your eyes, the way “fibro fog” makes you feel like you’re thinking through cotton—becomes a 4-character string. Something real and complex is compressed into something legible to a system. That compression is necessary. It’s also lossy.

How does a doctor arrive at a fibromyalgia diagnosis?

The American College of Rheumatology first established formal diagnostic criteria in 1990, requiring widespread pain and tenderness at 11 or more of 18 specific points on the body.¹ Updated criteria in 2010, revised again in 2016, moved away from the tender point exam entirely—recognizing that fibromyalgia is really a condition of centralized pain, where the nervous system amplifies signals, turning up the volume on sensations that others might not notice.² ³

Today, diagnosis is based on two main tools: the Widespread Pain Index (which maps pain locations across the body) and a Symptom Severity scale that captures fatigue, sleep problems, and cognitive symptoms.² ³ No blood test required. No imaging. Just a careful, systematic conversation about what you’re experiencing.

Your doctor isn’t guessing. There are real criteria. But those criteria are built entirely on patient-reported experience—which means the quality of the diagnosis depends heavily on the quality of the conversation, and the quality of the documentation depends on the tools available for capturing it.

Basic labs—blood counts, thyroid hormone, inflammatory markers—may be ordered not to diagnose fibromyalgia, but to rule out other conditions like rheumatoid arthritis, lupus, or hypothyroidism.⁶ ⁷ Much of the “objective data” in the chart, in other words, reflects what fibromyalgia is not.

What actually goes into your chart

Your EHR contains several layers. The problem list is a structured list of active diagnoses—fibromyalgia might appear there as M79.7. Your visit note is a narrative description of what happened. Your medication list records prescriptions. There may be a pain score—a single number from 0 to 10—somewhere in a flowsheet.⁸ ⁹

What’s often missing in structured, trackable form: where your pain actually is. How it’s changed since your last visit. Whether you’re sleeping. Whether you can do the things that matter—go to work, exercise, cook dinner, concentrate. These things may be mentioned in the note, but they’re typically in free text, not in fields that can be graphed over time or easily reviewed.¹¹

Validated questionnaires—like the Fibromyalgia Impact Questionnaire or PROMIS Pain Interference scales—exist specifically to capture this information systematically.⁴ ⁵ But their use is inconsistent. Many clinics don’t use them at all.

The gap between your experience and the record

There’s one more documentation issue worth knowing about: copy-forward notes. In many EHRs, a doctor can carry forward last visit’s note into this visit, updating only what changed. When done carelessly, this creates “note bloat”—lengthy documents that technically include your history but may obscure what’s actually different today.¹³ Problem lists can similarly become stale: conditions added but never removed or updated.¹²

Here’s the honest picture: fibromyalgia data is rich in narrative and thin in structure. The nuance of your experience tends to live in paragraphs that no tracking system is reading. The structured fields that systems do read were largely designed for conditions with lab values and imaging results. Understanding this mismatch is the first step toward pushing for something better.

Next up: Post 3 — Who’s Really Shaping Your Medical Record?

Disclaimer: Educational purposes only. Not medical advice.

References for Post 2

1. Wolfe F, et al. The American College of Rheumatology 1990 Criteria for the Classification of Fibromyalgia. Arthritis Rheum. 1990.
2. Wolfe F, et al. The American College of Rheumatology Preliminary Diagnostic Criteria for Fibromyalgia (2010). Arthritis Care Res. 2010.
3. Wolfe F, et al. 2016 Revisions to the 2010/2011 Fibromyalgia Diagnostic Criteria. Semin Arthritis Rheum. 2016.
4. Bennett R, et al. The Revised Fibromyalgia Impact Questionnaire (FIQR). Arthritis Res Ther. 2009.
5. Cella D, et al. PROMIS Adult Health Profiles. Qual Life Res. 2019.
6. Goldenberg DL, et al. Management of Fibromyalgia Syndrome. JAMA. 2004.
7. Häuser W, et al. EULAR Revised Recommendations for the Management of Fibromyalgia. Ann Rheum Dis. 2017.
8. Blumenthal D. Launching HITECH. N Engl J Med. 2010.
9. Office of the National Coordinator for Health IT (ONC). Office-based Physician EHR Adoption Data Brief. 2019.
10. Holmgren AJ, et al. Assessing EHR Data Quality. Health Aff. 2017.
11. Wright A, et al. Accuracy of the Problem List in Electronic Health Records. J Am Med Inform Assoc. 2012.
12. Hirschtick RE. A Piece of My Mind: Copy-and-Paste. JAMA. 2006.
13. O’Malley KJ, et al. Measuring Diagnoses: ICD Code Accuracy. Health Serv Res. 2005.