Month: May 2025

AI is often an accessibility tool, even if you don’t use it that way

by Dana Lewis

Talking about AI (artificial intelligence) often veers conversations toward lofty, futuristic scenarios. But there’s a quieter, more fundamental way AI is making a big difference today: serving as an accessibility tool that helps many of us to accomplish tasks more efficiently and comfortably than otherwise would be possible. And often, enabling us to complete tasks we might otherwise avoid or be unable to do at all.

One way to think about AI is as the ultimate translator. But I don’t just mean between languages: I mean between ways of interacting with the world.

Imagine you’re someone dealing with a repetitive stress injury like carpal tunnel syndrome, making prolonged typing painful or even impossible. Traditionally, you might use dictation software to turn spoken words into text, alleviating physical strain. No issues with that, right? But somehow, suggesting people use AI tools to do the same thing (dictation and cleaning up of the dictated text) causes skepticism about “cheating” the “correct” way of doing things. If you imagine the carpal tunnel scenario, that’s less likely to be a reaction, but imagine many other situations where you see outrage and disgust (as a knee jerk reaction) to the idea of people using AI.

In reality, there are three ways of doing things to accomplish a note-taking task:

  • A human types notes
  • A human speaks notes to a voice dictation tool
  • A human speaks notes to an AI-based dictation tool, that also when prompted could clean up and transform the notes into different formats.

All three introduce the possibility of errors. The difference is how we perceive and tolerate those errors: the perception often reflects bias rather than logic.

For example, the focus disproportionately in the third example is about errors, where errors might not even come up in the other two. OMG, the AI might do something wrong! It might hallucinate an error! Well, yes, it might. But so too does the dictation software. There was similar outrage years ago when voice dictation software became common for doctors to use to dictate their chart notes. And yes, there were and are errors there, too. And guess what? Humans typing notes? ALSO RESULTS IN ERRORS. The important thing here is all three cases: human alone, human plus basic tech, human plus AI, all result in the possibility of errors.

(I actually see this frequently, where I see three different providers who either use voice dictation to write my chart notes, introducing errors; AI-assisted notetaking, occasionally introducing errors; and one manually types all of their notes…still occasionally introducing errors. They’re typically different types of errors, but the result is the same: error!)

This is more about cultural change than it is about the errors in and of themselves. If people actually cared about the errors, we would be creating pathways to fix errors by humans and other approaches, such as enabling wiki-style editing requests of medical charts so that patients and providers can collaboratively update and keep medical records and chart notes free of errors so they don’t propagate over time. This almost never happens: chart notes can only be corrected by providers, and patients often have to use scarce visit time if they care enough to request a correction. Instead, most discussions focus more on where theoretical errors came from rather than practical approaches to fix real-world errors.

Back to AI specifically:

Note taking is a simplistic example of what can be useful with AI, but there’s more examples of transformation, such as transforming data into different formats. Converting data from JSON to CSV or vice versa – this is a task that can be tedious or impossible for some people. Sure, this could be done manually, or it can be done with hand-written scripts for transforming the data, or it can be done by having an AI write the scripts to transform that data, or it can be done with the AI writing and executing the scripts to “transform the data itself”. AI can often do all of these steps quickly and efficiently, triggered by a plain-language request (either typed or dictated by voice).

Here are other examples where AI can be an accessibility tool:

  • A visually impaired user has AI describe images and generate ALT text and/or convert unreadable PDFs into something their screen reader can use. They might also have the AI summarize the text, first, to see if they want to bother spending the time screen reading all that text.
  • Individuals with mobility limitations control their home environment or work environment, by using AI to pair together tools that allow them to do things that weren’t possible before, and can brainstorm solutions to problems that previously they didn’t know how to solve or didn’t have the tools to solve or build.
  • People in a country where they don’t speak the language and are needing to access the healthcare system can benefit from real-time AI translation when there’s no medical interpreter services, if they bring their own AI translator. US healthcare providers are generally prohibited from using such tools and are forced to forego translation entirely when human translators are not available.
  • People with disabilities (whether those are mental or physical) using AI to help understand important healthcare or insurance forms or paperwork they need to understand or interpret and take action on.

Personally, I keep finding endless ways where AI is an accessibility tool for me, in large and small ways. And the small ways often add up to a lot of time saved.

One frequent example where I keep using it is for finding and customizing hikes. Last year, I had to change my exercise strategy, which included hiking more instead of running. Increasingly since then, though, I also have had to modify which hikes I’m able to do, including factoring in the terrain. (Super rocky or loose rock terrain are challenging whereas they used to not be a limitation). I used to spend a lot of time researching hikes based on location, then round trip distance, then elevation gain, then read trail descriptions and trail reports from recent weeks and months to ensure that a hike would be a good candidate for me. This actually took quite a bit of time to do manually (for context, we did 61 hikes last year!).

But with AI, I can give an LLM the parameters of geography (eg hikes along the I-90 corridor or less than two hours from Seattle), round trip mileage and elevation limits, *and* ask it to search and exclude any hikes with long sections of loose, rocky or technical terrain. I can also say things like “find hikes similar to the terrain of Rattlesnake Ledge”, which is a smooth terrain hike. This cuts down and creates a short list that meets my criteria so I can spend my time picking between hikes that already meet all my criteria, and confirming the AI’s assessment with my own quick read of the trail description and trail reviews.

It’s a great use of AI to more quickly do burdensome tasks, and it’s actually found several great hikes that I wouldn’t have found by manual searching, which is expanding my ‘horizons’ even when it feels like I’m being limited by the increasing number of restrictions/criteria that I need to plan around. Which is awesome. As hiking itself gets harder, the effort it takes to find doable hikes with my new criteria is actually much less, which means the cost-effort ratio of finding and doing things continues to evolve so that hiking continues to be something I do rather than giving it up completely (and drastically reducing my physical activity levels).

Whenever I see knee jerk reactions along the lines of “AI is bad!” and “you shouldn’t use it that way!” it often comes from a place of projecting the way people “should” do things (in a perfect world). But the reality is, a lot of times people can’t do things the same way, because of a disability or otherwise.

AI is an accessibility tool, even if you do not use it that way). A blog by Dana M. Lewis from DIYPS.orgAI often gives us new capabilities to do these things, even if it’s different from the way someone might do it manually or without the disability. And for us, it’s often not a choice of “do it manually or do it differently” but a choice of “do, with AI, or don’t do at all because it’s not possible”. Accessibility can be about creating equitable opportunities, and it can also be about preserving energy, reducing pain, enhancing dignity, and improving quality of life in the face of living with a disability (or multiple disabilities). AI can amplify our existing capabilities and super powers, but it can also level the playing field and allow us to do more than we could before, more easily, with fewer barriers.

Remember, AI helps us do more – and it also helps more of us do things at all.

IUD insertion or IUD replacement is more manageable with a paracervical block

by Dana Lewis

If you’re someone who is considering an IUD (intrauterine device) or has an IUD and is considering a replacement or the removal process, this post is for you. You should know about this! Feel free to share it with a friend.

I recently decided to replace my IUD. I was dreading it, because I found the insertion process the first time I got one to be the most painful thing I had ever experienced. For context, years later I massively broke my ankle in 3 places. I now am able to articulate that the pain level of an IUD insertion, for me, is like broken bone level pain inside. It “only” lasts for a few minutes at that level, but nevertheless, it is excruciating.

When I was due for my first ever replacement (swap), I asked my doctor’s office if there were any better pain management options than what I experienced for the insertion process. They told me no, the only thing they could offer was an oral medication I could try to soften the cervix. I took it in advance as directed, and also took full doses of ibuprofen and Tylenol, went for the swap process and…it was just as bad as the first insertion, even though I had previously had one. Ugh.

The good news related to IUDs is that they keep getting extended, in terms of how many years they are approved for birth control efficacy. Mine went from 5 years approval to 8 years approval, so I was looking forward to having more years in between the terrible experiences. However, my experience was that this time around when I reached a little over 5 years (fully expecting to go to 8 years with it), my period bleeding picked back up to a degree that I decided I would go ahead and swap to a new one. (Birth control-wise, they’re approved for 8 years, but the approval indication for heavy bleeding is still at 5 years, so it makes sense that some people who see a reduction in period bleeding on IUDs may see a return after that 5 year timeframe. Not everyone, but some will, and I did.)

So that’s why I was going in to get a replacement, at about 5.5 years from my last one. This time, I had a new provider’s office, but since my last office couldn’t offer me any reasonable pain mitigation, I didn’t bother asking in advance and just went in with an active dose of Tylenol in my system, mentally prepared for the pain.

But then, at my appointment when going over the risks and discussing any questions I had before the procedure, my new provider said “what pain mitigation would you like?”

I said: “What? You’re offering me something?!”

And yes, there are options and she did offer them! She talked about ibuprofen/Tylenol (I already had taken Tylenol), a hot pack for the stomach, or something called a paracervical block. It’s an injection, so she asked how I felt about needles. I laughed and told her I had type 1 diabetes (the implication being, I deal a lot with needles and regardless of what I feel about them, they keep me alive so I am used to dealing with them).

The side effects of this paracervical block include potentially experiencing ringing in the ears and a metal taste in your mouth, plus obviously the potential pain from the injection itself.

I quickly evaluated my thoughts – I didn’t think it would help (because softening the cervix previously didn’t help), and I didn’t love the idea of a block. That’s because my previous ‘nerve block’ type injections, such as when at the dentist, result in a LOT of pain for me for the injection. But, then again, the IUD replacement process is even more painful, so I thought for the small chance that it would help cut down on that pain, it was worth trying at least once. So I said yes.

While she was getting set up, I asked her if this was new (because I was surprised I hadn’t heard of it) and she said no, it’s been around but early research showed it wasn’t much more effective than placebo so it didn’t really pick up in clinical practice, but that later studies DID show efficacy of it. (I later looked that up and she was right – there’s a 2012 study showing similar efficacy on pain reduction to placebo, aka around 30%); whereas international studies and a later 2018 study with an increased dose DID show pain reduction for more people.) And we all know it takes time for things to translate to clinical practice (see this visual and imagine it as a game of telephone), so knowing this now helps me better understand why in 2015 (my first insertion) and 2020 (my first replacement), this was not an option offered to me by my old clinic. I don’t like it, but I understand the context better.

What the experience of a paracervical block was like

The first step was a numbing spray. Then came the injection. Maybe because of the numbing spray, it didn’t feel like an injection the way I normally experience injections for nerve blocks. I felt a minor pinch and a little bit of pressure from the fluid going in. I was surprised that it took a few (it was injected into several areas) and I was borderline slightly uncomfortable, not in the sense that I was going to ask her to stop, but I was ready for that part to be done. (And probably anticipatory pain for the actual removal/swap process). But it was done and then I realized, this was nothing like the other injections for nerve blocks and it was indeed very tolerable.

(Side effect wise, I did not experience ‘ringing’ in my ears, but I did feel like I could hear more easily (e.g. sounds in the room suddenly got louder). Afterward when I got up, I did feel a little odd for about 60 seconds, but that could have also been because I was laying down and then hopped back up (see below) pretty soon after. I didn’t have any taste in my mouth, and the ‘louder sounds’ didn’t persist beyond a few minutes. None of the side effects phased me nor would influence my decision to get another one.)

Then it was time for the IUD removal. She asked me to cough and I did while it came out. It felt uncomfortable like a pinch with friction, but it wasn’t stabbing excruciating pain. It wasn’t “sharp” feeling like pain. I breathed a bit while she got ready to do the insertion of the new IUD and she asked me to take a few deep breaths. I did, and the IUD was inserted. Like the removal, it felt slightly uncomfortable, but again more like friction, and it was less than the removal.

No excruciating, stabbing pain!!!

She was done, and I immediately sat up and told her the paracervical block helped, I was so glad I had done it, and now I wasn’t going to dread my next swap.

Previously, for my first insertion and my subsequent first swap, it took me a minute or two of laying there, breathing deeply, to recover from the intense, excruciating pain. I would be able to get up and get dressed and leave on my own, but it definitely was an intense full body experience that required a few minutes and then I would feel like I had to recover from it (psychologically) the rest of the day. And obviously carry that experience 5 years forward.

In contrast, I immediately sat up and was ready to get dressed and go. I didn’t need to recover. I left and drove home in great spirits, then started texting everyone I know who had IUDs that it was a jaw-dropping, wildly different experience and they should look up paracervical blocks and when it’s time for swap/replacement IUDs, ask in advance if their doctor/clinic offers it and shop around for somewhere that will offer it if not. It is THAT wildly different of an experience. I hate making phone calls, but I will 100% make as many phone calls as it takes in the future to make sure I always have this option. It took the painful experience from broken bone level pain (e.g. 9-10/10 excruciating pain) to a tolerable discomfort with only a little bit of pain (e.g. 2-3/10 experience). I say that as someone who was told by an ER doc while he was setting my ankle, broken in 3 places, that I have a high pain tolerance.

The other benefit of the paracervical block is she said it helps reduce cramping for up to an hour. And it did! I made it home before I started to feel cramping (like strong period cramps), almost exactly an hour after the injection. I continued to alternate between Tylenol and ibuprofen the rest of the day, but this was like managing a period, and I didn’t have any pain hangover from the injection or the IUD replacement process. (Again, previously it felt like it took me hours to recover from the experience, when I had it without the paracervical block).

IUD insertion (or IUD replacement) is more manageable with a paracervical block, a blog post from Dana M. Lewis on DIYPS.orgNot everyone finds IUD insertions or replacement to be excruciating. If you don’t, I’m so glad for you. But my experience was that it’s the most painful thing I’ve ever experienced. Over half the people I talk to with personal experience also say it is incredibly painful. So if you are one of the people, like me, who find IUD insertions or IUD replacements to be a terrible, painful experience…ask about a paracervical block. It makes an incredible difference and I’m now not dreading the replacement or future removal.

(And if you have any other questions about the experience that I can answer, happy to do so – leave a comment below.)

The data we leave behind in clinical trials and why it matters for clinical care and healthcare research in the future with AI

by Dana Lewis

Every time I hear that all health conditions will be cured and fixed in 5 years with AI, I cringe. I know too much to believe in this possibility. But this is not an uninformed opinion or a disbelief in the trajectory of AI takeoff: this is grounded in the very real reality of the nature of clinical trials reporting and publication of data and the limitations we have in current datasets today.

The sad reality is, we leave so much important data behind in clinical trials today. (And every clinical trial done before today). An example of this is how we report “positive” results for a lot of tests or conditions, using binary cutoffs and summary reporting without reporting average titres (levels) within subgroups. This affects both our ability to understand and characterize conditions, compare overlapping conditions with similar results, and also to be able to use this information clinically alongside symptoms and presentations of a condition. It’s not just a problem for research, it’s a problem for delivering healthcare. I have some ideas of things you (yes, you!) can do starting today to help fix this problem. It’s a great opportunity to do something now in order to fix the future (and today’s healthcare delivery gaps), not just complain that it’s someone else’s problem. If you contribute to clinical trials, you can help solve this!

What’s an example of this? Imagine an autoantibody test result, where values >20 are considered positive. That means a value of 21, 58, or 82 are all considered positive. But…that’s a wide range, and a much wider spread than is possible with “negative” values, where negative values could be 19, 8, or 3.

When this test is reported by labs, they give suggested cutoffs to interpret “weak”, “moderate”, or “strong” positives. In this example, a value of 20-40 is a “weak” positive, a value between 40-80 is a “moderate” positive, and a value above 80 is a strong positive. In our example list, all positives actually fall between barely a weak positive (21), a solidly moderate positive in the middle of that range (58), and a strong positive just above that cutoff (82). The weak positive could be interpreted as a negative, given variance in the test of 10% or so. But the problem lies in the moderate positive range. Clinicians are prone to say it’s not a strong positive therefore it should be considered as possibly negative, treating it more like the 21 value than the 82 value. And because there are no studies with actual titres, it’s unclear if the average or median “positive” reported is actually all above the “strong” (>80) cutoff or actually falls in the moderate positive category.

Also imagine the scenario where some other conditions occasionally have positive levels of this antibody level but again the titres aren’t actually published.

Today’s experience and how clinicians in the real world are interpreting this data:

  • 21: positive, but 10% within cutoff doesn’t mean true positivity
  • 53: moderate positive but it’s not strong and we don’t have median data of positives, so clinicians lean toward treating it as negative and/or an artifact of a co-condition given 10% prevalence in the other condition
  • 82: strong positive, above cutoff, easy to treat as positive

Now imagine these values with studies that have reported that the median titre in the “positive” >20 group is actually a value of 58 for the people with the true condition.

  • 21: would still be interpreted as likely negative even though it’s technically above the positive cutoff >20, again because of 10% error and how far it is below the median
  • 53: moderate positive but within 10% of the median positive value. Even though it’s not above the “strong” cutoff, more likely to be perceived as a true positive
  • 92: still strong positive, above cutoff, no change in perception

And what if the titres in the co-condition have a median value of 28? This makes it even more likely that if we know the co-condition value is 28 and the true condition value is 58, then a test result of 53 will be more correctly interpreted as the true condition rather than providing a false negative interpretation because it’s not above the >80 strong cutoff.

Why does this matter in the real world? Imagine a patient with a constellation of confusing symptoms and their positive antibody test (which would indicate a diagnosis for a disease) is interpreted as negative. This may result in a missed diagnosis, even if this is the correct diagnosis, given the absence of other definitive testing for the condition. This may mean lack of effective treatment, ineligibility to enroll in clinical trials, impacted quality of life, and possibly negatively impacting their survival and lifespan.

If you think I’m cherry picking a single example, you’re wrong. This has played out again and again in my last few years of researching conditions and autoantibody data. Another real-world scenario is where I had a slight positive (e.g. above a cutoff of 20) value, for a test that the lab reported is correlated with condition X. My doctor was puzzled because I have no signs of this condition X. I looked up the sensitivity and specificity data for this test and it only has 30% sensitivity and 80% specificity, whereas 20% of people with condition Y (which I do have) also have this antibody. There is no data on the median value of positivity in either condition X or condition Y. In the context of these two pieces of information we do have, it’s easier to interpret and guess that this value is not meaningful as a diagnostic for condition X given the lack of matching symptoms, yet the lab reports the association with condition X only even though it’s only slightly more probably for condition X to have this autoantibody compared to condition Y and several other conditions. I went looking for research data on raw levels of this autoantibody, to see where the median value is for positives with condition X and Y and again, like the above example, there is no raw data so it can’t be used for interpretation. Instead, it’s summary of summary data of summarizing with a simple binary cutoff >20, which then means clinical interpretation is really hard to do and impossible to research and meta-analyze the data to support individual interpretation.

And this is a key problem or limitation I see with the future of AI in healthcare that we need to focus on fixing. For diseases that are really well defined and characterized and we have in vitro or mouse models etc to use for testing diagnostics and therapies – sure, I can foresee huge breakthroughs in the next 5 years. However, for so many autoimmune conditions, they are not well characterized or defined, and the existing data we DO have is based on summaries of cutoff data like the examples above, so we can’t use them as endpoints to compare diagnostics or therapeutic targets. We need to re-do a lot of these studies and record and store the actual data so AI *can* do all of the amazing things we hear about the potential for.

But right now, for a lot of things, we can’t.

So what can we do? Right now, we actually CAN make a difference on this problem. If you’re gnashing your teeth about the change in the research funding landscape? You can take action right now by re-evaluating your current and retrospective datasets and your current studies and figure out:

  • Where you’re summarizing data and where raw data needs to be cleaned and tagged and stored so we can use AI with it in the future to do all these amazing things
  • What data could I tag and archive now that would be impossible or expensive to regenerate later?
  • Am I cleaning and storing values in formats that AI models could work with in the future (e.g. structured tables, CSVs, or JSON files)?
  • Most simply: how am I naming and storing the files with data so I can easily find them in the future? “Results.csv” or “results.xlsx” is maybe not ideal for helping you or your tools in the future find this data. How about “autoantibody_test-X_results_May-2025.csv” or similar.
  • Where are you reporting data? Can you report more data, as an associated supplementary file or a repository you can cite in your paper?

You should also ask yourself whether you’re even measuring the right things at the right time, and whether your inclusion and exclusion criteria are too strict and excluding the bulk of the population for which you should be studying.

An example of this is in exocrine pancreatic insufficiency, where studies often don’t look at all of the symptoms that correlate with EPI; they include or allow only for co-conditions that are only a tiny fraction of the likely EPI population; and they study the treatment (pancreatic enzyme replacement therapy) without context of food intake, which is as useful as studying whether insulin works in type 1 diabetes without context of how many carbohydrates someone is consuming.

You can be part of the solution, starting right now. Don’t just think about how you report data for a published paper (although there are opportunities there, too): think about the long term use of this data by humans (researchers and clinicians like yourself) AND by AI (capabilities and insights we can’t do yet but technology will be able to do in 3-5+ years).

A simple litmus test for you can be: if an interested researcher or patient reached out to me as the author of my study, and asked for the data to understand what the mean or median values were of a reported cohort with “positive” values…could I provide this data to them as an array of values?

For example, if you report that 65% of people with condition Y have positive autoantibody levels, you should also be able to say:

  • The mean value of the positive cohort (>20) is 58.
  • The mean value of the negative cohort (<20) is 13.
  • The full distribution (e.g. [21, 26, 53, 58, 60, 82, 92…]) is available in a supplemental file or data repository.

That makes a magnitude of difference in characterizing many of these conditions, for developing future models, testing treatments or comparative diagnostic approaches, or even getting people correctly diagnosed after previous missed diagnoses due to lack of available data to correctly interpret lab results.

Maybe you’re already doing this. If so, thanks. But I also challenge you to do more:

  • Ask for this type of data via peer review, either to be reported in the manuscript and/or included in supplementary material.
  • Push for more supplemental data publication with papers, in terms of code and datasets where possible.
  • Talk with your team, colleague and institution about long-term storage, accessibility, and formatting of datasets
  • Better yet, publish your anonymized dataset either with the supplementary appendix or in a repository online.
  • Take a step back and consider whether you’re studying the right things in the right population at the right time

The data we leave behind in clinical trials (white matters for clinical care, healthcare research, and the future with AI), a blog post by Dana M. Lewis from DIYPS.orgThese are actionable, doable, practical things we can all be doing, today, and not just gnashing our teeth. The sooner we course correct with improved data availability, the better off we’ll all be in the future, whether that’s tomorrow with better clinical care or in years with AI-facilitated diagnoses, treatments, and cures.

We should be thinking about:

  • What if we design data gathering & data generation in clinical trials not only for the current status quo (humans juggling data and only collecting minimal data), but how should we design trials for a potential future of machines as the primary viewers of the data?
  • What data would be worth accepting, collecting, and seeking as part of trials?
  • What burdens would that add (and how might we reduce those) now while preparing for that future?

The best time to collect the data we need was yesterday. The second best time is today (and tomorrow).

Exhausting and familiar, the experience of living with autoimmune diseases

by Dana Lewis

A new autoimmune disease is exhausting and foreign, until it becomes exhausting and familiar.

Exhausting is such a good word for it. Sometimes, it’s the disease process itself that is exhausting and causes fatigue physically. Other times, it’s the coping and figuring out how to wrangle your life into a pretzel around it that is exhausting. It’s exhausting continually finding new things that are changing, out of your control, that you have to adapt to both physically and mentally. Sometimes, it’s exhausting trying to explain to others how it affects you and what you need support-wise, especially when it doesn’t come with a clear name, a neat bow, and an easily explainable narrative about what the trajectory will look like. Because you don’t know. You don’t have answers, and it’s exhausting to deal with the unknown and uncertainty.

On the flip side, it’s also exhausting when you don’t talk about it. It’s exhausting to be dealing with it, struggling to adjust, and not talking about it. Because of stigma, because of concern about how other people will treat you differently (even if well-intentioned), because you don’t have answers or a name and can’t fully articulate what is going on in a way other people will understand, because you worry about how it impacts the people you love and whether you’re an anchor holding them back.

Sometimes that also means it’s exhausting to articulate to your healthcare providers. I am lucky that my healthcare providers listened to me and believed me, even when I was coming from a high state of health and physical fitness (e.g. cross country skiing for 6 hours, run/walking ultramarathons, exercising every day, etc) and respect that when I said “I can’t run and press off through my ankle and now it feels weird in my thigh and to lift my hip, and my hands are now weak”, that it was indeed a problem, even when my bloodwork and other biomarkers and clinical exams were mostly normal. (Except for my lungs, the canary in the coalmine for me, and a few sporadic blood biomarkers showing immune shenanigans, most of my data makes me look like the healthiest horse standing outside of the glue factory. I look sick compared to healthy horses, but I look too healthy compared to the horses going into the glue factory. So to speak.)

It’s exhausting to not gaslight myself, coming out of doctor’s appointment after doctor’s appointment where they repeat “you remain a mystery” while also doing everything they can to help to try to diagnose the exhausting, now-familiar thing that evades naming, evades mechanistic understanding, and evades effective curative treatment. They’re doing everything they can despite the fact that they can’t provide answers. Nor can I. I have to hold on to my data (experiential, lived, wearable, and the few lab results and pulmonary function testing that clearly show the level of the problem) tightly and push back against gaslighting myself.

But while it’s all exhausting, it has slowly shifted from foreign to familiar.

I am grateful for the familiarity in a way, because with familiarity comes a newly developed language to put words to the indescribable; a reinforced skill for adaptation and new ‘hacks’ and discovered instruments of freedom; and a commitment to find the glimmers of joy buried under all the exhaustion.

My newly developed language is evolving, because I constantly test this new language on my family and friends in the know. I have to differentiate how this impacts my muscles, especially because I come from a land of ultrarunners who specifically train in discomfort for the purpose of being able to tolerate discomfort in endurance activities. When I say something bothers me, it means it’s intolerable and not what a healthy body would experience in terms of discomfort. I know what tired, sore muscles feel like (hello, 82 miles of run/walking or 6 hours of cross country skiing 50 kilometers/31 miles) and what acute muscle damage feels like from physical activity. This is not that. It’s struggling to initiate a muscle movement, but still being able to move, even though it progressively feels like moving through jello. It’s not something that anyone I know, or I when healthy, experienced, and so I have to figure out and evolve the ways to describe it. Mostly, I found success in describing the consequences of what is happening, when I can’t run and I find it more challenging to walk and hike, even though I can still do those things. Those are things that people can understand, and understand that it’s important to me that I can’t do them and/or that these activities are immensely harder to accomplish, even if they don’t understand the sensation causing that outcome. I can generally describe having an autoimmune disease that affects my muscles, and that’s enough (people understand autoimmune diseases) to be understood.

Like Icarus flying too close to the sun (analogy at top of mind from recently reading to my nephew a Percy Jackson book…), it’s like my muscles are melting, but not to the point of my falling into the ocean. And that’s where healthcare providers most usually see patients, when they’re about to or have hit the ocean when their wings (muscles) fully stop working. I am still flying, but less high, a little melty, a little wonky. I know something is clearly wrong and I see the ocean and where things will go without a solution. But even by flying lower (aka, doing less, stopping running, etc), I’m still melting – it’s still happening, and limiting my physical activity or activities of daily living doesn’t change the trajectory.

Thus, the reinforced skills for adaptations. I learned a lot from my decades of type 1 diabetes and having to “DIY” things myself outside of the healthcare solution. I’m more quick to go from “ugh this is a problem” to wondering about possible solutions. This is everything from changing how I sit (different chairs, with cushions) or lay down to work (with my laptop on a stand to reduce neck muscle strain and a separate bluetooth keyboard and trackpad so I can iterate positions as needed) to bracing early and often (ankle braces, a back brace, a foam neck brace) to a variety of things to lower the challenge to my hands. This includes using little slide tins for meds instead of flip top containers, because even the easy to open containers sometimes bother my hands. I also found a ski carrying strap so I can carry my cross country skis in the winter over my shoulder with the strap, rather than holding them in my hands. Sometimes I ask Scott to make my dinner or prep things (like cutting fruit in advance) so I don’t make my food choices based on not wanting to use up my hand energy to prep the food rather than for eating it. (Ongoing shout out to Scott, who epitomizes the ultimate supportive partner/husband and if he gets annoyed at anything, it’s my occasional hesitation or resistance of wanting him to ask him to do more, because I worry about asking him to do much. He recently spontaneously reminded me that I am a sail…and that always helps.)

It’s important for me to also remember that every bit helps and it doesn’t have to cure but that doesn’t mean it won’t help. Because the help adds up. But it requires pushing back the mental knee-jerk response of saying “that won’t help” because it’s not a cure for the root of the problem. Nope, no cures here. But that doesn’t mean a little bit of help for a little corner of the problem isn’t worth trying. Usually, 2 out of 3 times that little bit of help is a huge relief and reduces the physical burden, even if it’s ‘small’ like something for my hands. Sometimes it’s only a little bit of a help, and so we keep looking for better solutions for that particular challenge. Sometimes I can’t adapt a solution and I adapt my behavior. But my success rate has gone up, and that is great knowing that I can adapt solutions to fit my needs, even though sometimes it gets overwhelming to think about the volume of adaptations, especially when comparing it to a few years ago of how I lived and locomoted and worked.

Because the adaptations mean I can continue to find the glimmers of joy in life. No, I can’t run and I hike and walk more slowly, but I can still get out into the trees and under the blue skies and sunshine and feel the breeze on my face as I move through space. On days I choose to rest, I can sit out on the porch, sometimes braced, sometimes reclined in a chair with pillows, and watch the kittens sun themselves or jump up and stretch out on my legs. I can still spend time with family and friends, enjoying what I can still do with my ten niblings (nieces and nephews) and honorary niblings in my life. I can remind myself that while it feels like I’m falling out of the sky and I am dipping down, I have (hopefully) miles to go before I hit the ocean and stop flying at all. The delta in altitude sucks, especially in comparison to what I could do before, but with less comparison I can find more glimmers of joy both now and on the horizon. There is still a lot I can (and do) do, even as the list of things I can’t do or must adapt grows.

Exhausting and familiar, the experience of living with autoimmune diseases, a blog by Dana M. Lewis on DIYPS.orgIf you find yourself in the exhausting-but-foreign space of a new or suspected autoimmune disease, it sucks. I’m sorry. I wish I could help. (And if I can help you, let me know). But I hope it helps you to know that you are not alone. That yes, it does suck, but there is some solace when it turns from completely foreign to somewhat familiar, even if that doesn’t mean that it got easier or got better. But maybe it’ll be more known, maybe you’ll find more adaptations, and maybe you’ll still be able to find some glimmers of joy.

I did (I have), and I hope you do, too.