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.

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).

Passive Impact (You Can Optimize For That)

by Dana Lewis

Some of the popular use cases of AI agents are to build custom software for other people, and sell it cheaper than big corporate software but still giving you a really good return on your time. A lot of people are quickly making good chunks of money doing that, and automating their workflows so they can make money while they sleep, so to speak. It’s often referred to as passive income, whether that’s investments or a product or business that can generate income for you without you having to do everything.

Some of us, though, are taking a different approach. Not a new one, though, although AI is helping us scale our efforts. This type of approach has been common through open source software for decades. The idea is that you can build something that will help other people…including yes, while you sleep. Instead of passive income, it’s passive impact. 

We can think about using our time, skills, and output of our work not for financial profit, but for people’s benefit, by reaching more people and being useful to people we don’t even know. The goal isn’t making money, it’s to help more people.

Some people do pursue financial income to make an impact. I’m not saying not to do that, if that’s your path of choice. You can maximize your income, then donate to causes you care about. It’s a great strategy for a lot of people who have lucrative careers, and there are a lot of causes (like Life For a Child, which we estimate is the most cost-effective way to help people with diabetes worldwide) that can scale their work with your help through financial donations.

Other people maybe don’t have the same income earning potential or have chosen less financially lucrative careers where their work makes a difference, or they volunteer their time and elbow grease outside of their work to make a difference.

Both of those are great. Yet, I’m saying “yes and” there’s a third option we should talk about more in general society: scaling impact asynchronously and building things for passive impact.

These are things that don’t just solve a one-time problem for one person. They can solve a one-time problem for a lot of people, or a recurring problem for other people.

They scale.

They persist.

What are some examples of passive impact? Think about things that can run without you.

They don’t require a calendar invite or a Zoom link. They don’t need a customer service rep. They just… work. For someone. Somewhere. Every day.

Note that these tools don’t have to work for EVERYONE. Most of my stuff is considered “niche”. I was asked countless times early on why I thought OpenAPS would work for everyone with diabetes. People were surprised when I said I didn’t think it would work for everyone, but it would probably work for everyone who wanted it. (Then we did a lot of research and proved that). Not everyone needed or wanted it, but that doesn’t mean that it wasn’t worthwhile to build for me, or me plus the first dozen, or me plus the now tens of thousands of people using the OpenAPS algorithm in their AID system. But that’s “just” a “small fraction” of “only people with type 1 diabetes” who are “a tiny part of the population of the world”. It doesn’t matter; those people still matter and they (and I!) benefitted from that work, so it was worth doing. The impact scaled.

(You don’t have to quantify it all, but some metrics are helpful)

As someone who builds a lot of things with passive impact, it’s helpful to have data or some kind of analytics to see use of what I built over time. It’s useful for identifying where there are areas to improve, such as if people are stuck when using a feature or finding a bug, but also reinforcing the scale of your impact.

I love waking up and seeing the volume of meal data that’s been processed through Carb Pilot or PERT Pilot (measured via API use metrics), to know that while I was sleeping on the west coast of the US, several people woke up in (likely Europe or elsewhere) and used one of my apps to estimate what they had for breakfast. It’s great reinforcement and you can also see whether you’re gaining exponential growth (in terms of overall usage or new users) over time, and perhaps consider whether you should do a little more sharing about what you’ve built so it can reach the right people before it takes off on its own. Again, not for profit, but for helping.

(But passive impact doesn’t mean passive effort)

Creating something helpful is not passive in the beginning. It takes work, and elbow grease, to understand the problem you are (or someone else is) facing. To be able to determine a useful solution. To build, or write, something that other people can use, without needing to explain it every time. To deploy or share or host it in a way that is accessible and usable long-term. Of course, there are increasing numbers of tools (like LLMs – here are some of my tips for getting started if you’re new to prompting) that can help you get started more quickly, or find a fix to a bug or project blocker, or try something new you didn’t know how to do before.

But once it’s live, the math changes. One hour of your time can help hundreds of people, without requiring hundreds of hours. A lot of times it may be more than one hour, but nowadays, it’s often a lot less than it would have been otherwise. And more likely, you may find yourself spending multiple hours building something and be frustrated (well, I often get frustrated) at “how long it’s taking”, then realize that if you don’t build it, no one will. And without this effort, it wouldn’t get built at all. So it’s worth the >one hour time it’s taking to build it, even if it’s longer than expected.

That’s how a lot of my projects started: I needed something, I built it, and then I realized others needed it too. So I built (or wrote) it and shared it.

That’s passive impact, and it adds up.

Passive impact: Creating and building to help people you don’t know in ways that persist without always requiring your time or presence

I’d love to see more of this in the world. And I’d love to see an understanding that this IS the goal, not financial outcomes, and that’s a valid and celebratable goal. (This comment is motivated by having someone ask me in recent weeks asking how much royalties I’m getting from the open source code we released 10 years ago, intentionally free, with the goal of companies using it!) But preferably not followed with “I could never do that”, because, of course, you could. You can. You…should? Maybe, maybe not. But hopefully you think about it in the future. It’s not “either or” with financial income, either. You can do both! Society spends a lot of time talking about how to earn money passively. But not nearly enough time thinking about how we can create value for others passively. Especially in health, technology, and research spaces (fields where gaps are common and timely help matters), this way of thinking can change not just how we build, but who we build for. We can bring more people into using or building or doing, whether it’s active or passive. And we all win as a result.

Try, Try Again with AI

by Dana Lewis

If you’ve scoffed at, dismissed, or tried using AI and felt disappointed in the past, you’re not alone. Maybe the result wasn’t quite right, or it missed the mark entirely. It’s easy to walk away thinking, “AI just doesn’t work.” But like learning any new tool, getting good results from AI takes a little persistence, a bit of creativity, and the willingness to try again. Plus an understanding that “AI” is not a single thing.

AI is not magic or a mind reader. AI is a tool. A powerful one, but it depends entirely on how you use it. I find it helpful to think of it as a coworker or intern that’s new to your field. It’s generally smart and able to do some things, but it needs clear requests and directions on what to do. When it misses the mark, it needs feedback, or for you to circle around and try again with fresh instructions.

If your first attempt doesn’t go perfectly, it doesn’t mean the technology is useless, just like your brand new coworker isn’t completely useless.

Imperfect Doesn’t Mean Impossible

One way to think of AI is that it is a new kitchen gadget. Imagine that you get a new mini blender or food processor. You’ve never made a smoothie before, but you want to. You toss in a bunch of ingredients and out comes…yuck.

Are you going to immediately throw away the blender? Probably not. You’re likely to try again, with some tweaks. You’ll try different ingredients, more or less liquid, and modify and try again.

I had that experience when I broke my ankle and needed to incorporate more protein in my diet. I got a protein powder and tried stirring it into chocolate milk. Gross. I figured out that putting it in a tupperware container and shaking it thoroughly, then leaving it overnight, turned out ok. Eventually when I got a blender, I found it did even better. But the perfect recipe for me ended up being chocolate milk, protein powder, and frozen bananas. Yum, it made it like a chocolate milkshake texture and I couldn’t tell there was powder in it. But I still had to tweak things: shoving in large pieces of frozen bananas didn’t work well with my mini blender. I figured out slices worked ok, and eventually Scott and I zeroed in that it was most efficient to slice the banana and put it into the freezer, that way I had ready-to-go frozen right-sized banana chunks to mix in.

I had some other flops, too. I had found a few other recipes I liked to do without protein powder. Frozen raspberry or frozen pineapple + a crystal light lemonade packet + water are two of my hot weather favorites. But one time it occurred to me to try the pineapple recipe with protein powder in it… ew. That protein powder did not go well with citrus. So I didn’t make that one again.

AI is like that blender. If the result isn’t what you wanted, you should try:

  • Rewriting your prompt. Try different words, try giving it more context (instructions).
  • Give it more detail or clearer instructions. “Make a smoothie” is a little vague; “blend chocolate milk, protein powder, and frozen banana” is a little more direction to tell it what you want.
  • Try a different tool. The models are different for LLMs, and the setup is different for every tool. How you might use ChatGPT to do something might end up being different for using Gemini or MidJourney.

Sometimes, small tweaks make a big difference.

If It Doesn’t Work Today, Try Again Tomorrow (or sometime in the future)

Some tasks are still on the edge of what AI can do in general, or a particular model at that time. That doesn’t mean they’ll always be unable to do that task. AI is improving constantly, and quickly. What didn’t work a few months ago might work today, either in the same model or a new model/tool.

A flowchart diagram titled “Try a task with AI” illustrates how to approach AI usage with persistence and iteration. At the top is a purple box labeled “Try a task with AI.” Two arrows extend downward. The left arrow leads to a peach-colored box labeled “Result is not quite right,” which then leads to another box with three bullet points: “Reword your prompt,” “Give it more instructions,” and “Try this prompt with a different model/tool.” Below that is a smaller orange box labeled “Still didn’t work?” which connects to a final box that says: “Park this project: ‘try again later’ list” and “Try a different task or project.” From this box, an arrow loops back to the top box, showing that users should try again. The right arrow from the top goes to a green box labeled “Result is pretty good,” which then leads to another green box that says “Keep going & use AI for other tasks and projects.” This green path also loops back to the top. The overall message of the diagram is that regardless of whether the result is good or not quite right, users should continue experimenting with AI and trying new tasks.I’ve started making a list of projects or tasks I want to work on where the AI isn’t quite there yet and/or I haven’t figured out a good setup, the right tool, etc. A good example of this was when I wanted to make an Android version of PERT Pilot. It took me *four tries* over the course of an entire year before I made progress to a workable prototype. Ugh. I knew it wasn’t impossible, so I kept coming back to the project periodically and starting fresh with a new chat and new instructions to try to get going. In the course of a year, the models changed several times, and the latest models were even better at coding. Plus, I was better through practice at both prompting and troubleshooting when the output of the LLM wasn’t quite what I wanted. All of that over time added up, and I finally have an Android version of PERT Pilot (and it’s out on the Play Store now, too!) to match the iOS version of PERT Pilot. (AI also helped me quickly take the AI meal estimation feature from PERT Pilot, which is an app for people with EPI, and turn it into a general purpose app for iOS called Carb Pilot. If you’re interested in getting macronutrient (fat, protein, carb, and/or calorie) counts for meals, you might be interested in Carb Pilot.)

Try different tasks and projects

You don’t have to start with complex projects. In fact, it’s better if you don’t. Start with tasks you already know how to do, but maybe want to see how the AI does. This could be summarizing text, writing or rewriting an email, changing formats of information (eg json to csv, or raw text into a table formatted so you can easily copy/paste it elsewhere).

Then branch out. Try something new you don’t know how to do, or tackle a challenge you’ve been avoiding.

There are two good categories of tasks you can try with AI:

  • Tasks you already do, but want to do more efficiently
  • Tasks you want to do, but aren’t sure how to begin

AI is a Skill, and Skills Take Practice

Using AI well is a skill. And like any skill, it improves with practice. It’s probably like managing an intern or a new coworker who’s new to your organization or field. The first time you managed someone, it probably wasn’t as good as after you had 5 years of practice managing people or helping interns get up to speed quickly. Over time, you figure out how to right-size tasks; repeat instructions or give them differently to meet people’s learning or communication styles; and circle back when needed when it’s clear your instructions may have been misunderstood or they’re heading off in a slightly unexpected direction.

Don’t let one bad experience with AI close the door. The people who are getting the most out of AI right now are the ones who keep trying. We experimented, failed, re-tried, and learned. That can be you, too.

If AI didn’t wow you the first time for the first task you tried, don’t quit. Rephrase your prompt. Try another model/tool. (Some people like ChatGPT; some people like Claude; some people like Gemini….etc.) You can also ask for help. (You can ask the LLM itself for help! Or ask a friendly human, I’m a friendly human you can try asking, for example, if you’re reading this post. DM or email me and tell me what you’re stuck on. If I can make suggestions, I will!)

Come back in a week. Try a new type of task. Try the same task again, with a fresh prompt.

But most importantly: keep trying. The more you do, the better it gets.

iOS and Android development experience for newbies

by Dana Lewis

Vibe coding apps is one things, but what about deploying and distributing them? That still requires some elbow grease, and I’ve described my experiences with both Apple and Google below for my first apps in each platform.

(I’m writing this from the perspective of someone familiar with coding primarily through bash scripts, JavaScript, Python, and various other languages, but with no prior IDE or mobile app development experience when I got started, as I typically work in vim through the terminal. I was brand new to IDEs and app development for both iOS and Android when I got started. For context, I have an iOS personal device.)

Being new to iOS app development

First, some notes on iOS development. If you only want to test your app on your own phone, it’s free. You can build the app in XCode and with a cord, deploy it directly on your phone. However, if you wish to distribute apps via TestFlight (digitally) to yourself or other users, Apple requires a paid developer account at $99 per year. (This cost can be annoying for people working on free apps who are doing this as not-a-business). Initially, figuring out the process to move an app from Xcode to TestFlight or the App Store is somewhat challenging. However, once you understand that archiving the app opens a popup to distribute it, the process becomes seamless. Sometimes there are errors if Apple has new development agreements for you to sign in the web interface, but the errors from the process just say your account is wrong. (So check the developer page in your account for things to sign, then go try again once you’ve done that.) TestFlight itself is intuitive even for newcomers, whether that is yourself or a friend or colleague you ask to test your app.

Submitting an app to the App Store through the web interface is relatively straightforward. Once you’ve got your app into TestFlight, you can go to app distribution, and create a version and listing for your app and add the build you put into TestFlight. Note that Apple is particular about promotional app screenshots and requires specific image sizes. Although there are free web-based tools to generate these images from your screenshots, if you use a tool without an account login, it becomes difficult to replicate the exact layout later. To simplify updates, I eventually switched to creating visuals manually using PowerPoint. This method made updating images easier when I had design changes to showcase, making me more likely to keep visuals current. Remember, you must generate screenshots for both iPhone and iPad, so don’t neglect testing your app on iPad, even if usage might seem minimal.

When submitting an app for the first time, the review process can take several days before beginning. My initial submission encountered bugs discovered by the reviewer and was rejected. After fixing the issues and resubmitting, the process was straightforward and quicker than expected. Subsequent submissions for new versions have been faster than the very first review (usually 1-3 days max, sometimes same-day), and evaluation by App Store reviewers seems more minimal for revisions versus new apps.

The main challenge I have faced with App Store reviews involved my second app, Carb Pilot. I had integrated an AI meal estimation feature into PERT Pilot and created Carb Pilot specifically for AI-based meal estimation and custom macronutrient tracking. Same feature, but plucked out to its own app. While this feature was approved swiftly in PERT Pilot as an app revision, Carb Pilot repeatedly faced rejections due to the reviewer testing it with non-food items. Same code as PERT Pilot, but obviously a different reviewer and this was the first version submitted. Eventually, I implemented enough additional error handling to ensure the user (or reviewer, in this case) entered valid meal information, including a meal name and a relevant description. If incorrect data was entered (identified by the API returning zero macronutrient counts), the app would alert users. After addressing these edge cases through several rounds of revisions, the app was finally approved. It might have been faster with a different reviewer, but it did ultimately make the app more resilient to unintended or unexpected user inputs.

Other than this instance, submitting to the App Store was straightforward, and it was always clear at what stage the process was, and the reviewer feedback was reasonable.

(Note that some features like HealthKit or audio have to be tested on physical devices, because these features aren’t available in the simulator, so depending on your app functionality, you’ll want to test both with the simulator and with physical iOS devices to test those. Otherwise, you don’t have to have access to test on a physical device.)

Being new to Android app development

In contrast, developing for Android was more challenging. I decided to create an Android version of PERT Pilot after receiving several requests. However, this effort took nearly two years and four separate attempts to even get a test version built. I flopped at the same stage three times in a row, even with LLM (AI) assistance in trying to debug the problem.

Despite assistance from language models (LLMs), I initially struggled to create a functional Android app from scratch. Android Studio uses multiple nested folder structures with Kotlin (.kt) files and separate XML files. The XML files handle layout design, while Kotlin files manage functionality and logic, unlike iOS development, which primarily consolidates both into fewer files or at least consistently uses a single language. Determining when and where to code specific features was confusing. (This is probably easier in 2025 with the advent of agent and IDE-integrated LLM tools! My attempts were with chat-based LLMs that could not access my code directly or see my IDE, circa 2023 and 2024.)

Additionally, Android development involves a project-wide “gradle” file that handles various settings. Changes made to this file require manually triggering a synchronization process. Experienced Android developers might find this trivial, but it is unintuitive for newcomers to locate both the synchronization warnings and the sync button. If synchronization isn’t performed, changes cannot be tested, causing blocks in development.

Dependency management also posed difficulties, and that plus the gradle confusion is what caused my issues on three different attempts. Initially, dependencies provided by the LLM were formatted incorrectly, breaking the build. Eventually (fourth time was the charm!), I discovered there are two separate gradle files, and pasting dependencies correctly and synchronizing appropriately resolved these issues. While partly user error (I kept thrashing around with the LLM trying to solve the dependency formatting, and finally on the fourth attempt realized it was giving me a language/formatting approach that was a different language than the default Android Studio gradle file, even though I had set up Android Studio’s project to match the LLM approach. It was like giving Android Studio Chinese characters to work with when it was expecting French), this issue significantly impacted my development experience, and it was not intuitive to resolve within Android Studio even with LLM help. But I finally got past that to a basic working prototype that could build in the simulator!

I know Android has different features than iOS, so I then had to do some research to figure out what gestures were different (since I’m not an Android user), as well as user research. We switched from swiping to long pressing on things to show menu options for repeat/edit/deleting meals, etc. That was pretty easy to swap out, as were most of the other cosmetic aspects of building PERT Pilot for Android.

Most of the heartache came down to the setup of the project and then the exporting and deploying to get it to the Play Store for testing and distribution.

Setting up a Google Play developer account was quick and straightforward, despite needing to upload identification documents for approval, which took a day to get verified. There’s a one-time cost ($25) for creating the development account, that’s a lot cheaper than the yearly fee for Apple ($99/year). But remember, above and below, that you’re paying with your time as opposed to money, in terms of a less intuitive IDE and web interface for moving forward with testing and deploying to production.

Also, you have to have hands-on access to a physical Android device. I have an old phone that I was able to use for this purpose. You only have to do this once during the account creation/approval process, so you may be able to use a friend’s device (involves scanning QR code and being logged in), but this is a little bit of a pain if you don’t have a modern physical Android device.

I found navigating the Play Store developer console more complicated than Apple’s, specifically when determining the correct processes for uploading test versions and managing testers. Google requires at least 12 users over a two-week testing period before allowing production access. Interestingly, it’s apparently pretty common to get denied production access even after you have 12 users, the minimum stated. It’s probably some secret requirement about app use frequency, although they didn’t say that. The reason for rejection was uninformative. Once denied, you then have a mandatory 14 day wait period before you can apply again. I did some research and found that it’s probably because they want a lot of active use in that time frame. Instead of chasing other testers (people who would test for the sake of testing but not be people with EPI), I waited the 14 days and applied again and made it clear that people wouldn’t be using the app every day, and otherwise left my answers the same…and this time lucked into approval. This meant I was allowed to submit for review for production access to the Play Store. I submitted….and was rejected, because there are rules that medical and medical education apps can only be distributed by developers tied to organizations that have a business number and have been approved. What?!

Apparently Google has a policy that medical “education” apps must be distributed by organizations with approved business credentials. The screenshots sent back to me seem to be flagging on the button I had on the home screen that described PERT and dosing PERT and information about the app. I am an individual (not an organization or a nonprofit or a company) and I’m making this app available for free to help people, so I didn’t want to have to go chase a nonprofit who might have android developer credentials to tie my app to.

What I tried next was removing the button with the ‘education’ info, changing the tags on my app to fall under health & fitness rather than ‘medical’, and resubmitting. No other changes.

This time…it was accepted!

Phew.

–

iOS or Android: which was easier? A newbie's perspective on iOS and Android development and app deployment, a blog by Dana M. Lewis from DIYPS.orgTL;DR: as more and more people are going to vibe code their way to having Android and/or iOS apps, it’s very feasible for people with less experience to do both and to distribute apps on both platforms (iOS App Store and Google Play Store for Android). However, there’s an up front higher cost to iOS ($99/year) but a slightly easier, more intuitive experience for deploying your apps and getting them reviewed and approved. Conversely, Android development, despite its lower entry cost ($25 once), involves navigating a more complicated development environment, less intuitive deployment processes, and opaque requirements for app approval. You pay with your time, but if you plan to eventually build multiple apps, once you figure it out you can repeat the process more easily. Both are viable paths for app distribution if you’re building iOS and Android apps in the LLM-era of assisted coding, but don’t be surprised if you hit bumps in the road for deploying for testing or production.

Which should you choose for your first app, iOS or Android? It depends on if you have a fondness for either iOS or Android ecosystem; if one is closer to development languages you already know; or if one is easier to integrate/work with your LLM of choice. (I now have both working with Cursor and both also can be pulled into the ChatGPT app). Cost may be an issue, if $99/year is out of reach as a recurring cost, but keep in mind you’ll pay with your time for Android development even though it’s a $25 single time user account setup fee for developers. You also may want to think about whether your first app is a one-off or if you think you might do more apps in the future, which may change the context for paying the Apple developer fee yearly. Given the requirements to test with a certain number of users for Play Store access, it’s easier to go from testing to production/store publication on Apple than it is for Google, which might factor into subsequent app and platform decisions, too.

iOS Android
Creating a developer account better (takes more time, ID verification), one time $25 fee, requires physical device access
Fees/costs $99/year Better: one time $25 fee for account creation
IDE better (more challenging with different languages/files and requires gradle syncing)
Physical device access required No (unless you need to test integrations like HealthKit or audio input or exporting files or sending emails) Yes, as part of the account setup but you could borrow someone’s phone to accomplish this
Getting your app to the web for testing Pretty clear once you realize you have to “archive” your app from XCode, pops up a window that then guides you through sending to TestFlight. (Whether or not you actually test in TestFlight, you can then add to submit for review).

Hiccups occasionally if Apple requires you to sign new agreements in the web interface (watch for email notifications and if you get errors about your account not being correct, if you haven’t changed which account you are logged into with XCode, check the Apple developer account page on the web. Accept agreements, try again to archive in XCode, and it should clear that error and proceed.

 A little more complicated with generating signed bundles, finding where that file was saved on your computer, then dragging and dropping or attaching it and submitting for testing.

Also more challenging to manage adding testers and facilitate access to test.
Submitting for approval/production access Better, easy to see what stage of review your app is in. Challenging to navigate where/how to do this in web interface the first time, and Google has obtuse, unstated requirements about app usage during testing.
Expect to be rejected the first time (or more) and have to wait 14 days to resubmit.
Distribution once live on the store Same Same

 

Piecing together your priorities when your pieces keep changing

by Dana Lewis

When dealing with chronic illnesses, it sometimes feels like you have less energy or time in the day to work with than someone without chronic diseases. The “spoon theory” is a helpful analogy to illustrate this. In spoon theory, each person has a certain number of “spoons” representing their daily energy available for tasks including activities of daily living, activity or recreation activity, work, etc. For example, an average person might have 10 spoons per day, using just one spoon for daily tasks. However, someone with chronic illness may start with only 8 spoons and require 2-3 spoons for the same daily tasks, leaving them with fewer spoons for other activities.

I’ve been thinking about this differently lately. My priorities on a daily basis are mixed between activities of daily living (which includes things like eating, managing diabetes stuff like changing pump site or CGM, etc); exercise or physical activity like walking or cross-country skiing (in winter) or hiking (at other times of the year); and “work”. (“Work” for me is a mix of funded projects and my ongoing history of unfunded projects of things that move the needle, such as developing the world’s first app for exocrine pancreatic insufficiency or developing a symptom score and validating it through research or OpenAPS, to name a few.)

A raccooon juggles three spoonsAs things change in my body (I have several autoimmune diseases and have gained more over the years), my ‘budget’ on any given day has changed, and so have my priorities. During times when I feel like I’m struggling to get everything done that I want to prioritize, it sometimes feels like I don’t have enough energy to do it all, compared to other times when I’ve had sufficient energy to do the same amount of daily activities, and with extra energy left over. (Sometimes I feel like a raccoon juggling three spoons of different weights.)

In my head, I can think about how the relative amount of energy or time (these are not always identical variables) are shaped differently or take up different amounts of space in a given day, which only has 24 hours. It’s a fixed budget.

I visualize activities of daily living as the smallest amount of time, but it’s not insignificant. It’s less than the amount of time I want to spend on work/projects, and my physical activity/recreation also takes up quite a bit of space. (Note: this isn’t going to be true for everyone, but remember for me I like ultrarunning for context!)

ADLs are green, work/projects are purple, and physical activity is blue:

Example of two blocks stacked on each other (green), four blocks in an l shape (purple), three blocks in a corner shape (blue)

They almost look like Tetris pieces, don’t they? Imagine all the ways they can fit together. But we have a fixed budget, remember – only 24 hours in the day – so to me they become Tangram puzzle pieces and it’s a question every day of how I’m going to construct my day to fit everything in as best as possible.

Preferably, I want to fit EVERYTHING in. I want to use up all available time and perfectly match my energy to it. Luckily, there are a number of ways these pieces fit together. For example, check out these different variations:

8 squares with different color combinations with a double block, an l shaped block, and a corner (three pieces) block. All squares are completely full, but in different combinations/layouts of the blocks

But sometimes even this feels impossible, and I’m left feeling like I can’t quite perfectly line everything up and things are getting dropped.

Example of a square where the blocks don't all fit inside the squareIt’s important to remember that even if the total amount of time is “a lot”, it doesn’t have to be done all at once. Historically, a lot of us might work 8 hour days (or longer days). For those of us with desk jobs, we sometimes have options to split this up. For example, working a few hours and then taking a lunch break, or going for a walk / hitting the gym, then returning to work. Instead of a static 9-5, it may look like 8-11:30, 1:30-4:30, 8-9:30.

The same is true for other blocks of time, too, such as activities of daily living: they’re usually not all in one block of time, but often at least two (waking up and going to bed) plus sprinkled throughout the day.

In other words, it’s helpful to recognize that these big “blocks” can be broken down into smaller subunits:

Tangram-puzzle-pieces-different-shapes-closeup-DanaMLewis

And from there… we have a lot more possibilities for how we might fit “everything” (or our biggest priorities) into a day:

Showing full blocks filled with individual blocks, sometimes linked but in different shapes than the L and corner shapes from before.

For me, these new blocks are more common. Sometimes I have my most typical day with a solid block of exercise and work just how I’d prefer them (top left). Other times, I have less exercise and several work blocks in a day (top right). Other days, I don’t have energy for physical activity, activities of daily living take more energy or I have more tasks to do and I also don’t have quite as much time for longer work sections (bottom left). There’s also non-work days too where I prioritize getting as much activity as I can in a day (bottom right!). But in general, the point of this is that instead of thinking about the way we USED to do things or thinking we SHOULD do things a certain way, we should think about what needs to be done; the minimum of how it needs to be done; and think creatively about how we CAN accomplish these tasks, goals, and priorities.

A useful trigger phrase to check is if you find yourself saying “I should ______”. Stop and ask yourself: should, according to what/who? Is it actually a requirement? Is the requirement about exactly how you do it, or is it about the end state?

“I should work 8 hours a day” doesn’t mean (in all cases) that you have to do it 8 straight hours in a row, other than a lunch break.

If you find yourself should-ing, try changing the wording of your sentence, from “I should do X” to “I want to do X because Y”. It helps you figure out what you’re trying to do and why (Y), which may help you realize that there are more ways (X or Z or A) to achieve it, so “X” isn’t the requirement you thought it was.

If you find yourself overwhelmed because it feels like you have a big block task that you need to do, this is also helpful then to break it down into steps. Start small, as small as opening a document and writing what you need to do.

My recent favorite trick that is working well for me is putting the item of “start writing prompt for (project X)” on my to-do list. I don’t have to run the prompt; I don’t have to read the output then; I don’t have to do the next steps after that…but only start writing the prompt. It turns out that writing the prompt for an LLM helps me organize my thoughts in a way that it then makes the subsequent next steps easier and clearer, and I often then bridge into completing several of those follow up tasks! (More tips about starting that one small step here.)

The TL;DR: perhaps is that while we might yearn to fit everything in perfectly and optimize it all, it’s not going to always turn out like that. Our priorities change, our energy availability changes (due to health or kids’ schedules or other life priorities), and if we strive to be more flexible we will find more options to try to fit it all in.

Sometimes we can’t, but sometimes breaking things down can help us get closer.

Showing how the blocks on the left have fixed shapes and have certain combinations, then an arrow to the right with example blocks using the individual unit blocks rather than the fixed shapes, so the blocks look very different but are all filled, also.

What bends and what breaks and the importance of knowing the difference as a patient

by Dana Lewis

As a patient, navigating healthcare often feels like decoding a complex rulebook. There are rules for everything: medication dosages, timing protocols, follow-up intervals. Some of these rules matter a lot, for either short term or longer term safety or health outcomes. But at other times… the rules seem senseless and are applied differently based on different healthcare providers within the same specialty, let alone across different specialities. As a patient, it’s easy to initially want to try to follow all rules perfectly, but feel unable to because the rules don’t make sense in a personal context. Over time, it can be hard to resist the conclusion that the rules don’t matter or don’t apply to you. The reality is somewhere in between. And it’s the in-between part that can be a challenging balance to figure out. Learning to navigate this balance requires understanding which rules are flexible and which aren’t.

I’ve learned there’s enormous value in digging into the “why” behind medical recommendations, when I can. Take acetaminophen (Tylenol), for example. There’s a clear, non-negotiable daily limit on the bottle because exceeding it is dangerous. The over-the-counter recommendation for Extra Strength acetaminophen (500 mg tablets) is no more than two tablets every six hours, not exceeding six tablets in 24 hours. Which actually means 3 doses per day, despite the 6 hour recommendation. This maximum daily limit (no more than six tablets) is set close to the safety threshold; exceeding that limit (eight tablets in 24 hours) increases the risk of severe liver damage.

Understanding this daily limit provides flexibility within safe boundaries (with the obvious caveat that I’m not a doctor and you should always talk to your own doctor). The “every 6 hours” recommendation ensures stable bioavailability of acetaminophen throughout the day, and making sure over the course of 24 hours that you are safely and completely below the max dosage line. Slight deviations to timing, such as taking a dose at 5 hours and 30 minutes instead of precisely 6 hours because you’re about to go to sleep, do not inherently cause harm, as long as the total intake remains within the safe daily limit. This is an example where a compliance-oriented guideline is designed primarily for optimal adherence at the population based level, rather than marking an absolute safety threshold at each individual dose.

There are a lot of things like this in healthcare, but it’s not always explained to patients and patients may not always think to stop and question the why – or have the time and resources to do so – and figure it out from first principles to decide whether a deviation on the timing or amount is risky, or not.

But many healthcare rules aren’t as clearly defined by safety, as is the case of the acetaminophen example. Other rules are shaped by convenience, compliance, and practical constraints of research protocols.

Timelines like “two weeks,” “one month,” or “six months” for follow-up visits or medication titration points often reflect research convenience more than physiological necessity or even the ideal best practice. These intervals might mark study endpoints, or convenience to the healthcare system, but they don’t necessarily pinpoint the best timeline overall or the right timeline for an individual patient. It can be hard as a patient to decide if your experience is deviating from the typical timeline in a beneficial or non-optimal way, and if and when to speak up and try to better adjust to the system or adjust the system to meet your needs (such as scheduling an earlier appointment rather than waiting for a mythical 4 month follow up when it’s clear by months 2-3 that there is no benefit to a treatment because any impact should have been observed by then, even if it wasn’t significant).

As a patient, understanding when rules reflect safety versus when they’re crafted primarily for convenience is crucial, but hard. Compliance-driven rules can sometimes be thoughtfully bent. They might be able to be adjusted to better fit individual circumstances without compromising safety. For instance, a medication schedule set strictly every eight hours might be modified slightly based on daily activities or sleep patterns, provided the change remains within safe therapeutic boundaries over the course of 24 hours. (And patients should be able to discuss this with their doctors! But time availability or access may influence the ability to have these conversations up front or over time as conflicts or issues arise.)

Yet, bending rules requires confidence, critical thinking, and often significant resources, whether those are educational, emotional, health itself, or financial. It means feeling secure enough to question a provider’s advice or advocate for adjustments tailored to individual needs. It’s not always even questioning the advice itself, but checking the understanding and interpretation of how you apply it to your own life. Most providers understand that, and have no problem confirming your understanding. Other times, though, it can accidentally or unintentionally cause conflict, if providers sometimes perceive questioning of their judgement.

I’ve tripped into that situation at least once accidentally before, when I had a follow up appointment with a non-MD clinical provider who wasn’t my main doctor at the practice, who I was seeing for an acute short-term issue. She was describing a recommendation for an rx, specifically because I have diabetes. In the past, I have received over-treatment from most providers because of having type 1 diabetes, because many recommendations for non-diabetes management that have guidance for people with diabetes are based on an assumption of non-optimal healing and non-optimal glucose management. Given that at the time I was already using OpenAPS, with ideal glucose outcomes for years, and no issues ever with reduced healing, I asked if the prescription recommendation would be given to the same type of patient without diabetes. I was trying to help myself make an informed decision about whether to accept the recommendation for the rx to determine if it was appropriate. If it was just because I had diabetes, it warranted additional discussion. It wasn’t about her clinical judgement per se, but about a shared decision making process to right-size the next steps to my individual situation, rather than assume that population-based outcomes for people with diabetes were automatically appropriate. Because of my experience, I know that sometimes they are and sometimes they are not, so I’ve learned to ask these questions. However, some combination of the lack of existing relationship with this provider; perhaps a poorly worded question; and other factors made the provider act defensive. I got the information I needed, decided the rx was appropriate for me and I would use it, and went about my business. But I got a follow up call later from another MD (again, not my MD) who was defensive and calling to check why I was questioning this non-MD provider and it came across as if I was questioning her because the provider was a non-MD…which was not the issue at all! It was about me and my care and making sure I understood the root of the recommendation: whether it was because of the health situation or because I had diabetes. (It was the former, about the health situation, although initially articulated as being simply because of the latter fact of simply having diabetes.)

This situation has colored all future encounters with healthcare providers for me. Seeing new providers who I don’t have a longstanding relationship with makes me nervous, from learned, lived experience about how some of these one-off encounters have gone in the past, like the ones above.

Unfortunately, patients who push back against compliance-driven rules or simply ask questions to facilitate their understanding risk being labeled “non-compliant” or “non-adherent”, and sometimes we get labels on our chart for asking questions and being misunderstood, despite our good intentions. Such labels can have lasting impacts, influencing how future providers perceive our reliability and credibility and can cause subsequent issues for receiving or even being granted access to healthcare.

This creates a profound dilemma for patients: follow all rules precisely, without question, but potentially sacrificing optimal care, or thoughtfully question to bend them and risk being misunderstood or penalized for trying to optimize your individual outcomes when the one-size-fits-all approach doesn’t actually fit.

Breaking compliance-oriented rules isn’t about defiance. At least, it’s never been that way for me. It’s about personalization and achieving the best possible outcomes. But not every patient has the luxury of confidently navigating these nuances, and even when they do, as described above, it can still sometimes turn out not so well. Many patients don’t have the time, energy, resources, or privilege required to safely challenge or reinterpret guidelines. Or they’ve been penalized for doing so. Consequently, they may remain strictly compliant, potentially missing opportunities for better individual outcomes and higher quality of life.

Healthcare needs to provide clarity around which rules are absolute safety boundaries and which are recommendations optimized primarily for convenience or broad adherence for the safe general public use. Patients deserve transparency and support in discerning between what’s bendable for individual benefit and what’s non-negotiable for safety.

What bends, what breaks and the importance of understanding the difference in healthcare. A blog post by Dana M. Lewis from DIYPS.orgAnd: patients should not be punished for asking questions in order to better understand or check their understanding. 

Knowing the difference on what bends and what breaks matters. But many patients remain caught in the delicate balance between bending and breaking, carefully evaluating risks and rewards, often alone.

Just Do Something (Permission Granted)

by Dana Lewis

Just do it. And by it, I mean anything. You don’t need permission, but if you want permission, you have it.

If you’ve ever found yourself feeling stuck, overwhelmed (by uncertainty or the status of the world), and not sure what to do, I’ll tell you what to do.

Do something, no matter how small. Just don’t wait, go do it now.

Let’s imagine you have a grand vision for a project, but it’s something you feel like you need funding for, or partners for, or other people to work on, or any number of things that leave you feeling frozen and unable to do anything to get started. Or it’s something you want the world to have but it’s something that requires expertise to build or do, and you don’t have that expertise.

The reality is…you don’t need those things to get started.

You can get started RIGHT NOW.

The trick is to start small. As small as opening up a document and writing one sentence. But first, tell yourself, “I am not going to write an entire plan for Z”. Nope, you’re not going to do that. But what you are going to do is open the document and write down what the document is for. “This document is where I will keep notes about Plan Z”. If you have some ideas so far, write them down. Don’t make them pretty! Typos are great. You can even use voice dictation and verbalize your notes. For example “develop overall strategy, prompt an LLM for an outline of steps, write an email to person A about their interest in project Z”.

Thanks to advances in technology, you now have a helper to get started or tackle the next step, no matter how big or small. You can come back later and say “I’m not going to do all of this, but I am going to write the prompt for my LLM to create an outline of steps to develop the strategy for project Z”. That’s all you have to do: write the prompt. But you may find yourself wanting to go ahead and paste the prompt and hit run on the LLM. You don’t have to read the output yet, but it’s there for next time. Then next time, you can copy and paste the output into your doc and review it. Maybe there will be some steps you feel like taking then, or maybe you generate follow up prompts. Maybe your next step is to ask the LLM to write an email to person A about the project Z, based on the outline it generated. (Some other tips for prompting and getting started with LLMs here, if you want them.)

The beauty of starting small is that once you have something, anything, then you are making forward progress! You need progress to make a snowball, not just a snowflake in the air. Everything you do adds to the snowball. And the more you do, the easier it will get because you will have practice breaking things down into the smallest possible next step. Every time you find yourself procrastinating or saying “I can’t do thing B”, get in the habit of catching yourself and saying: 1) what could I do next? And write that down, even if you don’t do it then, and 2) ask an LLM “is it possible” or “how might I do thing B?” and break it down further and further until there’s steps you think you could take, even if you don’t take them then.

I’ve seen posts suggesting that increasingly funders (such as VCs, but I imagine it applies to other types of funders too) are going to be less likely to take projects seriously that don’t have a working prototype or an MVP or something in the works. It’s now easier than ever to build things, thanks to LLMs, and that means it’s easier for YOU to build things, too.

Yes, you. Even if you’re “not technical”, even if you “don’t know how to code”, or even if you’re “not a computer person”. Your excuses are gone. If you don’t do it, it’s because you don’t WANT to do it. Not knowing how to do it is no longer valid. Sure, maybe you don’t have time or don’t want to prioritize it – fine. But if it’s important to you to get other people involved (with funding or applications for funding or recruiting developers), then you should invest some of your time first and do something, anything, to get it started and figure out how to get things going. It doesn’t have to be perfect, it just has to be started. The more progress you make, the easier it is to share and the more people can discover your vision and jump on board with helping you move faster.

Another trigger you can watch for is finding yourself thinking or saying “I wish someone would do Y” or “I wish someone would make Z”. Stop and ask yourself “what would it take to build Y or Z?” and consider prompting an LLM to lay out what it would take. You might decide not to do it, but information is power, and you can make a more informed decision about whether this is something that’s important enough for you to prioritize doing.

And maybe you don’t have an idea for a project yet, but if you’re stewing with uncertainty these days, you can still make an impact by taking action, no matter how small. Remember, small adds up. Doing something for someone else is better than anything you could do for yourself, and I can say from experience it feels really good to make even small actions, whether it’s at the global level or down to the neighborhood level.

You probably know more what your local community needs, but to start you brainstorming, things you can do include:

  • Go sign up for a session to volunteer at a local food bank
  • Take groceries to the local food bank
  • Ask the local food bank if they have specific needs related to allergies etc, such as whether they need donations of gluten-free food for people with celiac
  • Go take books and deposit them at the free little libraries around your neighborhood
  • Sign up for a shift or get involved at a community garden
  • Paint rocks and go put them out along your local walking trails for people to discover
  • Write a social media post about your favorite charity and why you support it, and post it online or email it to your friends
  • Do a cost-effective analysis for your favorite nonprofit and share it with them (you may need some data from them first) and also post it publicly

Just-do-something-you-have-permission-DanaMLewisI’ve learned from experience that waiting rarely creates better outcomes. It only delays impact.

Progress doesn’t require permission: it requires action.

What are you waiting for? Go do something.

The Cost-Effectiveness of Life for a Child – A Deep Dive into DALY Estimates and the 2025 Funding Gap

by Dana Lewis

Life for a Child is an international non-profit organization that supports children with diabetes by providing insulin, test strips, and essential diabetes care to over 60,000 children in low-income countries who would otherwise have little to no access to treatment.

Without access to supplies and skilled medical care, children with type 1 diabetes (T1D) often die quickly, and with only intermittent access may die within a few years of diagnosis. In some countries,  limited amounts and types of older insulins may be provided by the health systems. In these ‘luckier’ countries, test strips are still not usually provided. Without regular blood glucose testing, children may survive into early adulthood, yet still experience early mortality due to long-term complications such as blindness, kidney failure, or amputations.

Life for a Child (LFAC) offers a lifeline, extending life expectancy and improving the quality of life for children at a remarkably low cost. Life for a Child also does incredibly critical work in improving care delivery infrastructures in each of these countries that they support. They work directly with local healthcare providers to co-develop critical education materials for young people living with diabetes. Further, they provide a support network to local healthcare providers and some governments. This is all to help improve sustainability of access to services, medications, and support for people with diabetes in the long run.

Scott and I have been supporting Life for a Child as our charity of choice for many years. As we wrote in our analysis here in 2017:

“Life for a Child seems like a fairly effective charity, spending about $200-$300/yr for each person they serve (thanks in part to in-kind donations from pharmaceutical firms). If we assume that providing insulin and other diabetes supplies to one individual (and hopefully keeping them alive) for 40 years is approximately the equivalent of preventing a death from malaria, that would mean that Life for a Child might be about half as effective as AMF, which is quite good compared to the far lower effectiveness of most charities, especially those that work in first world countries.”

We used some of GiveWell’s analyses to assess effective giving, especially comparing options like GiveDirectly or more specific charity options like AMF:

“​For example, the Against Malaria Foundation, the recommended charity with the most transparent and straightforward impact on people’s lives, can buy and distribute an insecticide-treated bed net for about $5.  Distributing about 600-1000 such nets results in one child living who otherwise would have died, and prevents dozens of cases of malaria.  As such, donating 10% of a typical American household’s income to AMF will save the lives of 1-2 African kids *every year*.”

(Note: In addition to donations, I also have supported Life for a  Child with my time at both the US level, serving on the US-based Life for a Child US board, as well as the US representative on the international steering committee for Life for a Child.)

However, in 2025, Life for a Child faces an immediate and unexpected $300,000 funding shortfall, due to a previously committed donor no longer being able to provide this donation. This funding was for test strips, which will reduce the number of strips provided per child from three to two test strips per day.

Further, Life for a Child has additional funding needs to continue expanding to support more children who are otherwise unsupported and going without critical supplies. (The room for funding is several orders of magnitude above this year’s funding gap.)

In order to assess the need for how we (in a general sense, speaking of all of us) fill this funding gap and understanding if this is still a cost-effective way to support people with diabetes, we wanted to revisit our analysis for how cost-effective Life For a Child is.

For background, I asked Graham Ogle, head of LFAC, for some numbers. These include:

  • Life for A Child currently supports 60,000 children in 2025
  • The original expansion plan is a goal to support 100,000 children or more by 2030
  • Estimates for how much is spent per child is about $150 USD (slightly less than what Scott and I had estimated in 2017), or $160 USD if you incorporate indirect costs.

We used these numbers below to estimate the cost-effectiveness of Life for a Child’s interventions.

Estimating Life For A Child’s Cost per Disability-Adjusted Life Year (DALY)

The Disability-Adjusted Life Year (DALY) is the most commonly used metric in global health to capture both the years of life lost (YLL) due to premature death and the years lived with disability (YLD) due to a health condition, such as type 1 diabetes.

The goal of Life for a Child’s work is to reduce both of these by providing insulin and glucose monitoring as well as improved care necessary for improved health outcomes.

  1. Life for a Child support reduces Years of Life Lost (YLL) 

To estimate YLL reduction, we calculate the difference between the expected age at death for a child with T1D who receives no care versus a child receiving LFAC support:

  • Without Life for a Child :
    • In the worst-case scenario, children with T1D may die within 1-2 years due to lack of insulin, meaning an early death by age 10 instead of the typical life expectancy of 60 years in some of these countries. . This results in 50 YLLs (60 – 10 = 50).
    • In countries where insulin is available but costly and/or glucose monitoring is not affordable and readily available, children may survive into their late 20s or 30s, but still experience significant complications, reducing life expectancy. In this scenario (minimal access to insulin, glucose monitoring, etc), we make a rough assumption that children with diabetes may survive into their mid to late 30s, therefore 25 YLLs is a reasonable estimate (60 – 35 = 25).
  • With Life for a Child :
    • Life for a Child’s program significantly improves both short-term and long-term survival. We assume that children supported by Life for a Child have the potential to live to an average life expectancy of 50-60 years (instead of dying prematurely due to untreated T1D), even when considering that LFAC only supports children into early adulthood (e.g. 25-30 years of age).

If we assume the average life expectancy for children newly diagnosed with T1D increases from 15-35 years to 50-60 years with standard Life for a Child support, that gives a savings of 25-35 YLLs (DALYs) per child, accounting for most of the uncertainty in our lifespan estimates above.

  1. Years Lived with Disability (YLD) Reduction

T1D also causes significant disability when people with T1D don’t have access to insulin and/or sufficient glucose monitoring and monitoring for early signs of complications, especially due to complications like blindness, kidney failure, and amputations. Each of these conditions brings about substantial life impairment.

  • Without Life for a Child:
    • Children with poorly supported T1D face a high likelihood of severe complications as they age. We estimate the disability weight (DW) for this scenario at 0.20, reflecting significant disability as a result of some of those complications.
  • With Life for a Child:
    • Access to insulin and glucose monitoring and healthcare monitoring drastically reduces the risk of complications. We estimate a DW of 0.05, which represents a much lower level of disability, especially in terms of future complications.

With such DWs, the reduction in YLD before premature death (20%-5%=15% over 5-30 years = 1-4 DALYs), and the 5% reduction in the YLL benefit (5% * 25-35 = 1-2 DALYs) partially cancel out, and don’t change the end result much. The net gain of 1-2 DALYs due to YLD reduction is smaller than the uncertainty range on the YLL benefit.

So for purposes of cost-effectiveness calculations, we’ll ignore YLD in the rest of this post and continue using the 25-35 DALYs per child figure.

  1. Total DALYs and Cost per DALY

For this section, we’ll assume the total impact of Life for a Child’s intervention per child from the calculations above is 25-35 DALYs.

Life for a Child’s cost per child in 2025 is approximately $150 per year (or $160 including indirect costs), and if we estimate that most children receive treatment for about 15 years, meaning the total cost per child is roughly $1,500–$2,250 over that period (or $1,600-$2,400 total with indirect costs).

Thus, the cost per DALY for Life for a Child can be estimated as:

(Cost per child) / (DALYs saved per child)

Here are a variety of estimates for varying cost levels using the lower bound of 25 DALYs saved per child supported:

  • With $1,500 per lifetime per child ($150/year for 10 years) and 25 DALYs saved, that estimates $60 per DALY ($64 with indirect costs)
  • With $2,250 per lifetime per child ($150/year for 15 years) and 25 DALYs saved, that estimates $90 per DALY ($96 with indirect costs)
  • With slightly higher costs to assume the cost will rise over time of $175/year for 15 years, this is a higher estimated $2,625 per lifetime per child and 25 DALYs saved, estimating $105 per DALY.
  • With slightly higher costs to assume the cost will rise over time of $175/year for 20 years, this is a higher estimated $3,500 per lifetime per child and 25 DALYs saved, estimating $140 per DALY.

This places Life for a Child’s cost per DALY in the range of $60–$90, for conservative estimates a remarkably cost-effective intervention, and even the higher estimates of $105-$140 assuming an increase in costs and increase in years of support compares favorably to the most effective global health programs, including those recommended by GiveWell.

How did we come to this conclusion?

  • GiveWell estimates cash transfers through GiveDirectly result in $1000/DALY, based on welfare gains rather than direct health outcomes (so apples and oranges), but even apples to oranges we can estimate Life for a Child is more cost-effective by at least single digit (eg 1-9x) factors than cash giving elsewhere.
  • We know GiveWell’s top charities are around $50-$100/DALY. Given we were estimating $60-$140 with a wide swathe of estimates, we can see that Life for a Child aligns with some of GiveWell’s top charities in terms of cost per DALY and thus “compares favorably” in our analysis. 

Why You Should Donate to Life for a Child

The point of this post was for Scott and I to reassess our statement that we have been making since ~2017 or so, which is the fact that Life for a Child is a remarkably cost-effective charity overall, and likely one of the most cost-effective charities to support people living with diabetes around the world who otherwise won’t have access (or regular access) to insulin and blood glucose testing.

Life for a Child has a DALY cost in the range of $60-$140 (reflecting current versus future cost increases), depending on which input variables you use, which makes it one of the best uses of global health funding available today.

Because of this reassessment, we also hope if you’ve read this far that you, too, will consider making a life-saving and life-changing donation for people with diabetes by donating to Life for a Child.

If you’re feeling overwhelmed with world events and want to make a tangible difference in people’s lives in a measurable way, consider donating to Life for a Child.

If you want to support people with diabetes in the most cost-effective way, so that your donation dollars make the biggest impact? Donate to Life for a Child.

Your donation saves – and changes – lives.

Life for a Child is a cost-effective charity supporting people with diabetes that needs your help. A blog post from Dana M. Lewis at DIYPS.org(Thank you).

PS – feel free to reach out to me (Dana@OpenAPS.org) and/or Scott (Scott@OpenAPS.org) if you want to chat through any of the estimates or numbers in more detail and how we consider donations.

Scale yourself

by Dana Lewis

One of the things I wish people would consider more often when thinking about AI is how they can use it to scale themselves. What are some time-consuming things that they currently have to do themselves that AI could do for them to streamline their output and increase their productivity? Productivity for giving them more time to do the things only they can do, the things they want to do, or the things they love to do. (And to help stop procrastinating on things they have to do.)

I have a habit of trying to scale myself. These days, it’s often related to EPI (exocrine pancreatic insufficiency, which some areas of the world know by the acronym PEI). I developed a strong knowledge base first from personal experience, then by doing research – including a systematic review where I read hundreds, plural, of research papers on key topics related to design protocols and guidelines. As a result of both personal and research experience, I have a lot of knowledge. It gets tapped almost daily in the EPI support groups that I’m a part of.

Whenever I notice myself answering the same question repeatedly, I make a mental note of it. Eventually, if a topic comes up often enough, I turn my response into a blog post. This way, I can provide a well-structured, comprehensive answer with more time and context than a quick comment on social media allows – and with the ability to give the same, high quality answer to multiple people (and in some cases, hundreds or thousands of people rather than the few who might see the comment buried in a response thread).

A few examples of this include:

One of my favorite things with this approach is then seeing other people begin to share the links to my longer-form content to help answer common questions. By writing things down in a shareable way, it also enables and supports other people to scale your work by sharing it easily. This has started to happen more and more with the elastase blog post, in part because there are so few resources that cover this information all in one place.

For me, I lean toward writing, but for other people that could be videos, podcast/audio recording, or other formats that can capture things you know and make them shareable, thus scaling yourself.

For me, this approach of “scaling myself” and thinking about longer form content to post online instead of re-typing similar answers over and over again isn’t unique to EPI.

I have been doing this for over a decade. I developed this pattern early after we developed and shared OpenAPS (the first open source automated insulin delivery algorithm) with the world. Early on, I found myself answering the same technical questions repeatedly in online discussions with the same answers. Typing out explanations on my phone was inefficient, and if one person had a question, others likely had the same one. Instead of repeating myself, I took the time to document answers. I would often pause, write up the information in the documentation, and share that instead. This made it easier and quicker to go find and share a link instead of retyping responses, and it also took less time, so I was willing to do it more quickly than if I had to delay what I was doing in real life in order to type out a long yet already-answered question. Over time, I had to do less one-off typing on my phone (and could save that time and energy for true, one-off unique questions) and could share links with a lot more information more easily.

How do I use AI to scale this type of work?

A lot of the above tasks are related to writing. There are different ways you can use AI for writing, without having it write something completely. You can give it notes – whether you type or voice dictate them – and have it clean up your notes, so you can focus on thinking and not about typing or fixing typos that break your flow. You can have it convert the notes into full sentences. You can ask it to write a paragraph or an article based on the notes. You can ask it to suggest wording for a particular sentence you want to clarify for your audience.

If you think about the AI as an intern and/or a partner/collaborator who you would ask to review or edit for you, you’ll likely find even more ways to integrate AI into different parts of your writing process, even if it’s not doing the full writing for you.

I have also tried to task the AI with writing for me, with mixed results. This doesn’t mean I don’t use it, but I’ve been practicing and learning where it generates usable content and where it doesn’t.

A lot of it depends on the prompt and the topic (as much as it does the output in terms of style, length, intended audience etc).

If it’s a topic that’s “known”, it can write more content that I can take and edit and transform, as opposed to when I am trying to write about a concept that is far from the current knowledge base. (I mean far for both humans and of AI – a lot of my work is bleeding edge, pushing fields towards new developments and leading humans there.) Sometimes I ask it to write something and end up using none of the content, but by saying “ugh no” my brain has jumped to saying to myself “it should really say…” and I am able to more quickly springboard into manually writing the content I was previously slow on. In other words, it can be a brainstorming tool in the opposite sense, showing me what I do not want to say on a topic! And on some of my frontier/bleeding edge topics, it reflects what is commonly ‘known’ and when what is known is now wrong (example, as always, of how it’s commonly incorrectly reported that chronic pancreatitis is the most common cause of EPI), it helps me more clearly distinguish the new content from the old, wrong, or misinformed.

(Also, it’s worth reminding you what I have to remind myself, that AI is changing constantly and new tools override what is known about what tasks do and don’t do well! For example, in between writing this and posting it, OpenAI released GPT4.5, which is reportedly better at writing-related tasks than GPT-4o and other older models. I’ll have to test it and see if that’s true and for what kinds of writing tasks!)

This isn’t the only way you can scale yourself with AI, though. Scaling yourself doesn’t have to be limited to writing and documentation style tasks. AI and other tools can help with many tasks (more examples here and here), such as:

  • Cleaning and transforming data into different formats
  • Converting a CSV file into a more readable table
  • Writing code to automate tedious data processing
  • Drafting plain-language instructions for engineers or programmers
  • Checking whether instructions or explanations are clear and understandable, and identifying any gaps in logic that you missed on your first pass

By leveraging AI and other automation tools, you can free up time and energy for higher-value work: the things you are uniquely suited to do in the world, and the things that you want or love to do. And do them more easily!

Pro tip: if you find yourself procrastinating a task, this may be a good sign that you could use AI for some of it. 

I’m trying to use noticing procrastination as a trigger for considering AI for a task.

An example of this is an upcoming post with a bunch of math and meaty cost analysis that I originally did by hand. I needed (wanted) to re-do these estimates with different numbers, but procrastinated a bit because having to carefully re-do all the estimates and replace them throughout the blog post seemed tedious, so my brain wanted to procrastinate. So, I took the blog post and dumped it in with a prompt asking it to write Jupyter Notebook code to replicate the analyses explained via the plain language post, with the ability to adjust all input variables and see the results in a table so I could compare the original and updated numbers. It took less than 1 minute to generate this code and about 5 minutes for me to copy/paste, update the numbers, run it, and evaluate the output and decide what to update in the post. Manually, this would’ve taken 30-60 minutes due to needing to check my work manually and trace it throughout the post. Instead, this automated the tedious bit and will result in this new post coming out next week rather than weeks from now (read about it here – it’s an analysis on how cost-effect Life for a Child is, a charity supporting people living with diabetes in low- and middle-income countries that can use your help to save lives.)

Scale yourself: automate more, so you can handle what matters, a blog by Dana M. Lewis from DIYPS.orgI encourage you to think about scaling yourself and identifying a task or series of tasks where you can get in the habit of leveraging these tools to do so. Like most things, the first time or two might take a little more time. But once you figure out what tasks or projects are suited for this, the time savings escalate. Just like learning how to use any new software, tool, or approach. A little bit of invested time up front will likely save you a lot of time in the future.