Electronic Health Records, Chronic Pain, and the Fight for Meaningful Data
Chronic pain has always lived in a difficult space.
It is real. It is widespread. It is costly.
And yet, for decades, it has remained largely invisible in the very systems designed to treat it.
For most of that time, if you were living with chronic pain, your experience depended on whether someone believed you.
That is beginning to change, not because we suddenly understand pain better, but because we are finally learning how to use the data we already have.
And that data lives in something most patients never think about, the electronic health record, or EHR.
What EHRs Actually Represent
An electronic health record is not just a chart. It is a timeline of a person’s interaction with the healthcare system.
It includes:
- Diagnosis codes
- Medication histories
- Lab values and vital signs
- Clinical notes written by providers
For chronic pain, this matters more than almost any other condition.
Why?
Because pain is rarely captured cleanly in a single field.
It lives in fragments:
- A note about persistent discomfort
- A refill request for medication
- A missed follow-up appointment
- A description buried in a clinician’s narrative
Each piece alone may not say much.
But together, across millions of patients, they begin to tell a story.
The Promise: Finally Seeing Chronic Pain at Scale
For years, chronic pain research has been limited.
Clinical trials are expensive, slow, and often exclude the very patients we are trying to understand. People with multiple conditions, long histories, and complex lives are often left out.
EHR-based research changes that.
Large data networks like PCORnet and All of Us Research Program are now allowing researchers to study millions of patients over time.
That opens the door to questions we could never answer before:
- How does acute pain become chronic
- What treatment paths do people actually follow, not just in trials, but in real life
- What patterns exist across different populations
We are beginning to see chronic pain not as a single condition, but as a set of different pathways, different experiences, and different outcomes.
That matters.
Because if we do not understand the differences, we cannot treat them.
The Problem: EHRs Were Never Built for This
Here is the reality we have to face.
EHRs were not designed for research.
They were designed for billing, documentation, and workflow.
And that shows up everywhere in the data.
Lack of Standardization
There is no consistent way to document pain.
One clinician writes, severe pain, limits function.
Another writes, patient reports discomfort.
Another uses a 0 to 10 pain scale.
Another does not document pain at all.
Even diagnosis coding is inconsistent:
- Chronic pain may not be coded
- It may appear only in free text
- It may be overshadowed by other diagnoses
This means we undercount pain, misclassify patients, and introduce bias without even realizing it.
The Limits of Pain Scores
We all know the question.
Rate your pain from 0 to 10.
It is simple. It is fast. And it is deeply flawed.
A 7 for one person is not a 7 for another.
It depends on context, history, expectations, and what that person has learned to tolerate.
Yet this number becomes data.
And researchers are expected to build meaning from it.
Missing Populations
EHR data only includes people who interact with the healthcare system.
That means we miss:
- People without insurance
- People who avoid care
- People who have lost trust in the system
And in chronic pain, those are often the very people most affected.
So the data we rely on is already incomplete, and not evenly incomplete.
Bias Enters Early and Often
Bias does not start at the research phase. It starts at documentation.
It shows up in:
- How pain is described
- Whether it is taken seriously
- What gets coded and what does not
- Who has access to care in the first place
If we are not careful, we do not just study pain. We study inequity and call it evidence.
What Happens Between Data and Discovery
This is the part that is often invisible.
Between EHR documentation and research findings, there is a critical step:
Data extraction, cleaning, and preparation.
This includes:
- Standardizing inconsistent records
- Harmonizing data across systems
- Defining patient cohorts
- Interpreting free text using natural language processing
This step is where meaning is either clarified or distorted.
If it is done without input from people with lived experience, important context can be lost.
What We Are Learning Anyway
Despite these limitations, EHR research is producing meaningful insights.
We are beginning to understand:
- How opioid use develops over time
- Risk factors for pain becoming chronic
- The long delays in diagnosis for conditions like complex regional pain syndrome and endometriosis
- Patterns of care that were previously invisible
When we can show years of delayed diagnosis, repeated failed treatments, and patterns of undertreatment, we move from anecdote to evidence.
And evidence is what systems respond to.
The Ethical Question We Cannot Ignore
Most patients do not realize their data is being used this way.
EHR research often happens under broad consent waivers.
The argument is that the research is low risk and it would be impractical to obtain consent at scale.
But from a patient perspective, there are real concerns:
- Who is using the data
- For what purpose
- Who benefits
- Who is left out
For communities that have historically been excluded or exploited, this is not theoretical.
It is personal.
What This Means for Patient Engagement
This is where we, as People With Lived Experience, come in.
Because this is not just a data problem.
It is a meaning problem.
If we are not involved:
- Pain will continue to be misclassified
- Outcomes will be defined without us
- Measures will reflect convenience, not reality
We need to be part of:
- Defining what pain data should include
- Interpreting what that data actually means
- Identifying what is missing
- Ensuring findings reflect real lived experience
Without that, we risk building better systems that still do not understand us.
What You Can Do
If you are living with chronic pain, this may feel far removed from your day to day reality.
It is not.
Your experience is already in the system, just not always in a way that reflects what you are actually going through.
Here are a few practical steps you can take:
Check your record
Ask for access to your electronic health record and read the notes.
Speak to what matters
Do not just answer the pain scale. Talk about function, what you cannot do, what has changed, what you are struggling to maintain.
Ask for accuracy
If something is incorrect or incomplete, ask for it to be corrected.
Participate where you can
Programs like All of Us Research Program are actively seeking participants, and patient advisor roles are expanding.
Think beyond your own care
Every accurate record helps build a clearer picture for the next person.
The Path Forward
EHR-based research is not the answer.
But it is a powerful tool.
To make it work, we need:
- Better standardization of pain documentation
- Integration of functional outcomes, not just scores
- Inclusion of patient-reported data
- Transparency in how data is used
- Meaningful patient engagement at every stage
Because the goal is not just to collect more data.
The goal is to finally make chronic pain visible in a system that has too often ignored it.
Final Thought
For years, people living with chronic pain have said:
You cannot see it, but it is there.
Now, for the first time, we are starting to see it, in data.
Not perfectly.
Not completely.
But enough to begin.
And in a field where being unseen has been the norm, being counted, even imperfectly, is a step forward.
References (Extended URLs)
- PCORnet: https://pcornet.org/
- All of Us Research Program https: //allofus.nih.gov/
- National Academy of Medicine, Relieving Pain in America
https://nap.nationalacademies.org/catalog/13172/relieving-pain-in-america-a-blueprint-for-transforming-prevention-care - CDC MMWR, Chronic Pain Prevalence (Dahlhamer et al.)
https://www.cdc.gov/mmwr/volumes/67/wr/mm6736a2.htm - Office of the National Coordinator for Health IT, EHR Adoption Data
https://www.healthit.gov/data/quickstats/electronic-health-record-adoption - NIH HEAL Initiative https://heal.nih.gov/
Disclaimer
Disclaimer: The views, positions, and recommendations expressed in this article are based on my personal experiences and independent research. They are solely my own and do not necessarily reflect the views, policies, or positions of the American Chronic Pain Association (ACPA).
Acknowledgment
This article was developed with the assistance of ChatGPT to support organization, clarity, and synthesis of complex research and lived experience perspectives.