The Last Mile to Clinic: Why Democratized Software Means Something Different in Healthcare

July 13, 2026July 13, 2026 ~ cmonsen ~ Leave a comment

Last Friday, as I left the house for clinic, I dictated two prompts into my phone as an experiment.

The first:

In a single shot, provide me a dashboard of the most recent Massachusetts Health Policy Commission total cost of care data.

The second, verbatim, ums included:

In a single shot, create a Street Fighter game that I can play on my phone that has Elon Musk versus, uh, Mark Zuckerberg and maybe a few other Titans of Industry as selectable characters. Should be single player, and it will randomly select a opponent, um, kind of, uh, yeah. I I don’t care too much about the details, uh, but maybe mainly emphasizing that it… I… yeah. Don’t ask for additional design decisions. Just make them.

(Yes, years of experience in clinical dictation, on full display.)

Both were done before I parked. The dashboard quietly corrected my agency mix-up — the Health Policy Commission sets the benchmark; CHIA publishes the data — and tracked down CHIA’s March 2026 Annual Report, the freshest official read on Massachusetts health spending, assembling the CY2024 story into a single self-contained interactive page: total health care expenditures of $83.3 billion, $11,663 per resident, growth of 5.7% against a 3.6% benchmark, the fourth consecutive year above target. The payer detail was in there too — commercial spending up 10.8%, MassHealth per-member costs up 20.4% as eligibility redeterminations shrank membership and left a sicker risk pool.

The game agent imposed its own guardrails, declining to build a game where real people beat each other up. It invented four parody tycoons — a rocket mogul with a flaming dash, a hoodie-clad algorithm lord who throws spinning “like” projectiles — wired up touch controls, health meters, and a round timer, and handed me an arcade fighter that runs in my phone’s browser.

On paper, these are deliverables from two different professions. The dashboard is business intelligence — in a health system, the work of an analytics team: source data, KPIs, benchmark tables, a citation trail. The game is software development — a game loop, collision detection, input handling, character state machines, the work of engineers. Two realms of expertise, two backlogs, two degree paths — and they collapsed into the same act: a paragraph of dictated intent, a single HTML file, less time than it takes to drive ten miles. That collapse has got me thinking.

The prompt is not a spec

That second prompt is no one’s idea of a requirements document. It’s a transcript of a person thinking out loud on a commute, false starts and all, and its only unambiguous sentence — “Don’t ask for additional design decisions. Just make them” — hands over the specification itself.

In early 2025, Andrej Karpathy gave this workflow a name — vibe coding: give in to the vibes, “forget that the code even exists.” It’s easy to hear that as flippancy, but it is really about abstraction. Code hasn’t gone away — both of my artifacts are hundreds of lines of JavaScript that I will probably never read — but it now sits where assembly language has sat since the compiler arrived: a layer below where the author works. I supplied intent; the rest was translation.

For two decades, business intelligence promised something it called self-service: semantic layers, drag-and-drop chart builders, governed data marts. What it mostly democratized was consumption — anyone could view the dashboard someone else built. Creation still lived behind a queue: file the ticket, wait for the analyst, review, revise, wait again. The unit of BI delivery has always been the request — and it’s the queue, more than the craft, that this new way of working removes. Applications have their own version of the gate: the cost of design, development, and distribution has meant software gets written only when enough people share a problem to justify the investment. Both gates crumbled in the same ten-mile drive.

Yes — the demo is the easy half

Anyone who has run an analytics shop will object here, and the objection is fair. A single-shot dashboard has no refresh schedule, no lineage, no access controls, no agreed definitions behind its numbers. Mine froze the moment it was built and is already aging toward obsolescence. Healthcare data punishes casual handling more than most domains, and a wrong number in a benchmark hearing matters.

But the objection, taken seriously, concedes the interesting point: everything still on the worry list is about trust and maintenance rather than construction. Building the thing has dropped off the list of scarce resources.

The most instructive part of the dashboard, in fact, was its caveat. Unprompted, it flagged that one historical figure — the 2018 growth rate — came from prior-year reporting rather than the current chart, and was worth verifying against the source databook before citing. Building the artifact took minutes; deciding whether to trust it is the work that remains. Whether MassHealth per-member growth of 20.4% is meaningfully comparable across a redetermination year is not a question about pixels. It’s a question about definitions, denominators, and context — and settling questions like that, not rendering charts, is where BI budgets actually go.

The video game, meanwhile, made a judgment of its own: it declined to stage fights between real, named people and invented parody characters instead. Software embodies opinions — I wrote that about the EMR a decade ago — and now the thing that writes the software arrives with opinions too. Both artifacts came back with values baked in — one about data provenance, one about real persons — neither of which I asked for. Are they the right values? That’s a far harder question than whether the code runs. I think, perhaps, my new game lost some of its edge (at least until its new character — Rex Orbital (haha, Elon Musk, get it?) — becomes a household name).

What this means for healthcare IT

So if the ticket backlog were to disappear — and the cost gate on applications with it — what gets unlocked? I am sure there are industries where the answer is simple: abundance. Healthcare’s answer, though, runs through what telecom engineers call the last mile: the final connection to the home was always the expensive one, long after the long-haul lines got cheap. Our long-haul to the clinic just got cheap. In 2009, Mandl and Kohane argued in the New England Journal of Medicine that health IT needed substitutable applications — an iPhone-style app economy where a clinician could download a tool, reject it, and try another. Seventeen years on, that vision arrived only partially through EHR app stores and APIs. The gap remains wide enough that even today, the 2021 call to action I made alongside smart collaborators to enable integrating web services and applications into EHRs is still an opportunity. We are getting closer.

It seems that the rest of the vision will arrive through prompts and agents. Robin Sloan once wrote that an app can be a home-cooked meal — software made for a household, with no ROI and no roadmap. Maggie Appleton extended the idea to barefoot developers: domain experts building local software for communities that the industry will never serve. Healthcare is full of these unserved communities. The nurse manager’s staffing grid. The pharmacist’s prior-auth tracker. The quality team’s registry view that exists today as a heroic spreadsheet (v27_FINAL_final.xlsx). These are the small data tools — operationally vital, too small to justify taking a project number from the queue, which is precisely why they never got built. I am already living the home-cooked version of this: when I couldn’t buy the household assistant I wanted, I built it. These two took a commute. How many commutes are there in healthcare? How many to a clinic still thirsting for easier access to technology & data solutions?

The implication is uncomfortable for those of us who steward health systems’ information environments. Our de facto governance model for software creation has been scarcity: if building required developers, and developers required budget, then “who may build” was controlled by default, and the backlog could pass for a safeguard.

When anyone can build, “who may build” stops working as a control point. The real questions surface: What do we trust? How do we validate it? Where does it run, and what data can it see? What happens when the pharmacist’s tracker quietly becomes the source of truth for others as well? These are harder questions than triaging a ticket queue — but they are better ones, because they interrogate the actual risk instead of a proxy for it. A health system that answers them well gets something the request queue never delivered: the people who understand the problems helping build the solutions. Governance targeting is aimed more at meaning and trust rather than at construction.

On one drive to clinic, the dashboard and the video game cost me the same — a paragraph of dictated intent apiece — and both were waiting before I’d seen my first patient. But the ten miles I covered were never the hard part. The distance that matters in healthcare is the last mile, from a working artifact to the point of care, and it’s paved with exactly the things a prompt doesn’t generate: agreed definitions, validated numbers, workflows that carry a tool into the visit, and trust. Building was just the expensive part, and now it mostly isn’t. We can go farther faster, and — I hope — cast more of our attention to that last mile that still must be solved in carbon.

Home Hub: Building the Household Assistant I Couldn’t Buy

June 1, 2026 ~ cmonsen ~ Leave a comment

Running a home is not for the faint of heart. I have heard, seen, and in some cases tried to implement all manner of home productivity systems. Lately, our family has settled on a Skylight: the kitchen touchscreen where chores, the calendar, and the grocery list all live. It works, but it’s one of maybe six places household information actually lives. The school sends emails. Bills and shipping notices arrive in inboxes split between my wife and me. Appointments land on a Google Calendar. The mental work of pulling all of it together — what’s happening today, who’s doing which chore, which email actually needed a response — fell on a person. Mostly the person was my wife and that wasn’t what we wanted.

Synthesis is still hard. Figuring out next steps can be hard. I have been using LLM intelligence more and more in my life and was convinced that, with a little more synthesis, and a little more intelligence, I can reduce the load. I would have definitely bought this service if it existed. As far as I could tell, it did not. Claude Code had recently unlocked my ability to feasibly get back into coding, so I thought I would try to build it. My own Home Hub.

What it does

Home Hub is a privacy-preserving household assistant. It reads from the tools my family already uses — the Skylight (chores and the family calendar), a dedicated email inbox, Google Calendar — pulls everything into one place, and surfaces it three ways.

A daily email. Every morning at 5:30, before anyone’s awake, Home Hub assembles a briefing: today’s events, the open chores, the action items it found buried in overnight emails, and a look at what’s coming this week. It’s the synthesis that needed to come together in our heads, now done before breakfast.

A web dashboard. Includes prioritized views for today, this week, this month. The same information, browsable, when the email isn’t enough. It removes high-noise signals like repeating events so that we can, for example, focus on the bigger picture when a month’s view of Google Calendar otherwise just looks overwhelming.

An assistant you can talk to. This part has been the biggest unlock. Home Hub runs a conversational assistant over Telegram — I can text it like a member of the household. “What’s on for Saturday?” “Add a reminder to renew the registration.” It also works in the background: when there’s a task that needs research — compare three options, find a vendor, draft an email — it can take a first pass on its own and bring me something to review, rather than waiting to be asked.

The constraint that shaped everything

Here is the design decision I care most about: Home Hub does not run as me.

I was not interested in “connect all your accounts” tools that work by becoming you — full access, your login, your identity, acting on your behalf with the same powers you have. That’s convenient and, to my mind, the wrong model for something that touches my family’s email, calendar, and documents.

Home Hub works the way you’d want a trusted house-sitter to work. It has its own keys, scoped to what it needs, and you can take them back at any time without changing your own locks. Most processing happens in a sandbox, on a machine in my house. The genuinely sensitive material is airgapped elsewhere and separated out. Only sanitized extracts, stripped of the personal details, are ever handed to a cloud AI for the parts that need more horsepower.

But it’s the difference between an assistant I’d actually let near my family’s information and one I wouldn’t.

How it fits together

At the center is a small daily pipeline that does the unglamorous work: pull from each source, classify what’s sensitive, redact what needs redacting, and synthesize. Around it sit the three interfaces — the morning email, the dashboard, the conversational assistant — each reading from the same sanitized core.

(See the architecture diagram below.)

Why I could build this at all

It’s been a long time since I was building software regularly. Even then, small sections of this would have taken me a couple of weeks to implement. Five years ago, a project like this would have meant hiring someone, writing a requirements document, hoping the result resembled what I actually needed, and lots of iteration. The translation from what I know my household needs to working software was expensive enough that I simply would not have done it.

That translation cost has collapsed. With AI-assisted development, the person who understands the problem can increasingly build the solution directly — no intermediary, no requirements telephone game, no loss in translation. I know exactly what my family’s coordination problem looks like, because I live inside it. Now I can express that knowledge as a working system instead of as a feature request someone else might get wrong.

I think this is a small, domestic instance of a much larger shift we will see in our professional lives as well. For a long time, software was built by people who understood software, for people who understood the problem, with a lossy translation layer in between. As that layer thins, a shift will take place to people who understand the problem most deeply and are willing to build. In medicine, that’s the clinician who builds the tool instead of submitting a ticket. At home, that is me, usually while the kids are sleeping.

Home Hub isn’t really a product or package that I intend to share. I will probably get help from AI to consider that at some point. But for now it’s a household assistant that fits exactly one household — mine — because the person who built it is the person who needed it. That used to be a luxury reserved for good coders. I have proved, if only to myself, that it isn’t anymore.

When You Needed to Pay Attention

April 13, 2026 ~ cmonsen ~ Leave a comment

Since ChatGPT launched in November 2022, there have been hundreds of AI releases. If you tried to follow all of it, you drowned. If you ignored all of it, you missed something important.

I wanted to find the middle ground — to identify the moments where the capability ceiling seemed to jump, at least for me personally. These were genuine step changes that required me to update my mental model of what was possible and led to a lot of experimentation.

By my count, roughly eight qualify. ChatGPT made AI conversational. GPT-4V gave it eyes. GPT-4o made the interaction real-time and multimodal. o1 showed it could reason through multi-step problems. Deep Research meant it could investigate a question autonomously for hours. Claude Code meant it could write and maintain real software — not snippets, but systems. Gemini 3 commoditized reasoning. And Cowork made AI a genuine collaborator in professional-like workflows (I’m looking forward to when it’s available at work).

Reasonable people could draw the line differently. But when I laid them out on a timeline, the pattern that emerged is instructive: the gaps between step changes are compressing. Eleven months between the first two. Then seven. Then four. The most recent — two months. Part of what’s driving the acceleration is that there are more serious entrants now — OpenAI, Anthropic, Google, and others are effectively passing the ball to each other, each leap prompting the next within weeks. My own usage reflects this — I started with ChatGPT, moved to Gemini for a stretch, and now spend most of my time with Claude. I expect that will keep shifting as the capability lead changes hands.

In healthcare — where carbon is harder than silicon and the opinionated systems we built over the last decade are deeply entrenched — the compression is especially disorienting. We spent fifteen years deploying EMRs. AI is forcing us to reimagine what we do with them on a timeline measured in months, even if at the same time, things may move a little more slowly.

I don’t know how long the cadence keeps compressing or at what point it will feel that things have plateaued, but I hope the visualization helps to separate signal from noise.

Carbon Is Harder Than Silicon: Why the Future of AI Depends More on Human Systems Than Hardware

October 19, 2025 ~ cmonsen ~ Leave a comment

The one about the socio-technical model applied to AI.

Much of today’s AI conversation centers on tangible constraints — chip shortages, compute capacity, and the growing energy demands of model training. Those are real limits, and they’ll shape what’s technically possible for years.

But anyone who has lived through an EHR rollout or a “clinical decision support” pilot knows that the harder part often begins after the technology is installed. Once the silicon is humming, we still have to integrate it into the living fabric of care — how clinicians think, how teams coordinate, how patients experience the system.

That’s where the carbon-based agents come in. Not as obstacles, but as the essential medium through which any digital innovation actually becomes care.

The Sittig and Singh (2010) socio-technical model still provides one of the clearest guides here. It lays out eight interdependent dimensions — from technical infrastructure and clinical content to workflow, organizational culture, and external environment. It’s a reminder that safety, quality, and adoption emerge not from the technology itself, but from how these layers interact in practice.

The same logic applies to AI. A model can perform flawlessly in validation but fail to add value at the bedside if it doesn’t align with clinical priorities, decision rhythms, or accountability structures. These systems don’t just plug into existing teams; they subtly reshape how roles are defined, how authority flows, and how judgment is shared.

So yes, the field will need more chips, more power, and more scalable infrastructure. But the real breakthroughs will come from designing AI that supports the people who deliver care and ultimately the carbon-based agents central to their mission: the patient.

Switches, Boxes, and Steam — Three Questions for AI in Healthcare

September 13, 2025September 14, 2025 ~ cmonsen ~ Leave a comment

The one where 3 books answer 3 questions about AI in healthcare

Every few years a new wave hits healthcare IT. Some reshape the shoreline; some barely ripple. Lately I’m leaning toward a simple view: Gen AI is a real step forward—but the changes that endure will come from infrastructure and incentives. In as regulated and high-attention a system as health care, the roads matter more than the horsepower.

1) Who will benefit?

History says advantage concentrates around control points—the places where compute, data, and distribution meet. In today’s terms, that means model providers with scale (e.g., OpenAI, Anthropic, Google/DeepMind), the clouds that host them (eg AWS, Azure, Google Cloud), platforms already embedded in clinical workflows (eg Epic, Oracle Health/Cerner), and companies that own the last mile to clinicians and patients (eg large health systems). As though they control the “master switch,” these players have significant influence in supporting winners and losers.

But the circle does widen. A second group tends to capture durable value: the people and teams who complement those control points—clinical data stewards, evaluation and safety engineers, product integrators who turn models into reliable steps inside prior auth, triage, charting, imaging, and revenue cycle. As individuals, entrepreneurs who see where the network is going (and get there early) tend to do well: they make the glue, the adapters, the “boring” parts that let many models work safely across many contexts and control points.

As general models compete and compute capital fuels greater availability, the answer to “who benefits” may not depend as much on who has the smartest model as we thought. In healthcare, the answer is probably closer to: “where do you sit relative to distribution and standards?”

2) How will they benefit?

Consider what happened when shipping settled on a standard metal box—a 20- or 40-foot container with identical corner fittings. That one decision let cranes, ships, trains, and trucks handle cargo without repacking. Risk fell. Costs fell 90%. And the work moved: less muscle on the pier; more planning and throughput management at distribution centers and rail yards. Jobs followed the flow.

AI may trace a similar trajectory. As models ship in consistent packages—stable interfaces and licenses, companion evidence (safety & efficacy, provenance, evaluation coverage, known hazards)—risk drops across the chain. This is a chain, though, of bits and not atoms. Workflow behavior—increasingly digital—and model swaps achieve more predictable outcomes. Capital becomes willing to fund further scale because components are modular, productized, and auditable.

As for the work, I expect this to move from first-pass busywork to the “inland” roles that plan, do, study, and act towards the learning healthcare system. Technical roles for platform/orchestration, evaluation & red-team, data lineage & governance, enablement & change will blossom. Existing roles will morph. For example, on the admin side, copy-paste, phone calls, and status-chasing give way to flow coordination, exception desks, audit/QA, and patient navigation (think schedulers → access-ops coordinators; coders → utilization & compliance analysts). On the clinical side, keystrokes give way to judgment—ambient draft review, rare-case adjudication and roster reviews, care-plan design, patient counseling, and system-of-care safety & stewardship. And on the patient/caregiver side, the role shifts from passive data source to co-steward of context; from recipient of transactions to more of a navigator. There will be more need for controlling consent and sharing, supplying high-signal inputs (PROMs [patient-reported outcomes], home-device streams, life context, and values), correcting records and attaching verifiable documents, flagging errors or preference mismatches, and (for caregivers) supporting therapy reconciliation, adherence, and follow-up.

3) What is our responsibility?

I see two obligations emerging from this and running in parallel.

First: transparency as a design choice. The industrial age didn’t compound due to the steam engine alone; it compounded because we paid for disclosure. Blueprints were made public, and then builders turned them into businesses. In AI, the equivalent is releasing portable, trustworthy manifests with every meaningful update—lineage, test coverage, failure modes, guardrails—so others can evaluate, integrate, insure, and, when appropriate, improve. Procurement and reimbursement will prefer systems that come with real evidence as much as this has become the case for conventional therapeutics (ie medications) in trial and pharmacovigilance.

Second: we have to uplift the people, not just the pipes. Standards don’t only move information; they move jobs. Containerization made ports safer and faster, but it also displaced longshoremen and pushed opportunity inland. Healthcare will feel a similar migration as routine drafting and triage shrink. We will move only as fast as we develop the workforce a glide path. This looks like making the new roles visible; creating portable credentials for evaluation, operations, governance, and enablement; and retraining with intentionality. We won’t succeed if we think the players are frozen. We need to help the team skate to where the puck is going.

Bringing it together

If advantage tends to form at the control switches, and if standards are what turn demos into networks, then the next phase winners are 1. the builders who make AI reliable, swappable, and evidenced and 2. the organizations that invest in the people who run that learning system well. Gen AI “thoughtpower”—like the horsepower that came before—opens the door. The plumbing and the social compact (transparency + worker mobility) portends our trip through it.

Want to go deeper?

These ideas are an amalgam of a few books that have been highly influential in my own thinking. Please let me know if you have encountered others for this collection!

Tim Wu, The Master Switch — Why open eras often consolidate around control points, and what that means for innovation and competition.

Marc Levinson, The Box — How a universal container standard reshaped costs, jobs, and geography—useful for thinking about AI packaging and “inland” roles.

William Rosen, The Most Powerful Idea in the World — The case that incentives for disclosure (the patent bargain) made progress compound—and why entrepreneurs matter for carrying blueprints into the world.

A scientist, a magician, a poet, and an alien walk into a bar…

August 22, 2025August 23, 2025 ~ cmonsen ~ Leave a comment

The one about the Johari window

A scientist, a magician, a poet, and an alien walk into a bar. The bartender looks up and says, “Is this some kind of strained analogy?”

“Yes,” I say. Oh—I didn’t mention I’m at the bar, too? So are you.

It feels like we’ve been thinking a lot about uncertainty and navigating it these last five years in living rooms, workplaces, social media, and bars. (At least I think bars are still a thing.) That line of thought keeps bringing me back to the Johari Window. It’s a simple frame that helps me think about what we know, what we don’t, and what sits in between, so I started giving each box a voice.

The Johari Window sorts things into four panes: known knowns (what we understand and can explain), known unknowns (mysteries we can see but haven’t cracked), unknown knowns (truths we carry without fully realizing), and unknown unknowns (what we don’t even know we don’t know). Walking into the bar, we have the scientist (known knowns), the magician (known unknowns), the poet (unknown knowns), and the alien (unknown unknowns).

What would they be talking about tonight? Maybe they start with vaccines—Is everything we’ve known wrong?—or the state of the economy and national debt. Why is it so hard to buy a house or sell one? What’s wrong with the (grand)kids, and how do we raise them? Where is our social fabric? Our sense of national pride? Is that something we should care about? And what’s the deal with airplane food?

Maybe the poet will begin by speaking the scientist’s language: “why are we rejecting vaccines now, aren’t we all scientists?” The magician seems a little more open to mystery and talks, maybe, about how they’ve never seen measles and somehow ionized water has helped with their IBS. Awe and attention are their tools. “What do you mean you can’t explain it?” or “we’ve studied that!” we might overhear the scientist say, incensed that this is even a topic of conversation. The poet seems a little more open now thinking about how to carry the undercurrent before others can name it. Maybe they create a new phrase that can remind us to take action faster than a spreadsheet or weave a story about a medicoindustrial complex. The alien — definitely confused at first — might chime in with a nonsequitur or perhaps, “what even is a vaccine, anyway?”

Or maybe they start by talking about the economy? “What should we be doing now?” Do we have a government of magicians using levers that we don’t yet understand scientifically? Are they scientists running experiments? Poets telling stories of “American dynamism”. The scientist draws us to their numbers: this is unprecedented, “our experiments tell us tariffs are going to bite us.” The alien asks whether any of this even matters if superintelligence is right around the corner.

Standing in the doorway of the bar, it’s not clear where their conversation will go. It will probably look like it is getting messier and more argumentative before anything feels settled or harmonious. Still, I’ve enjoyed imagining the different parts and people in the conversation. I’m pretty sure I’d agree mostly with the scientist, but I can hear the poet sometimes. Magic seems like it’d be useful. And I like the enigmatic alien, so frequently misunderstood, even pitiable. Maybe, somehow, they’re the wisest of the group.

As we stand in the doorway, scanning the room for a cozy spot to settle in and talk, the bartender wipes a glass.

“Well, in that case, sit anywhere you all agree on.”

First! (in five years)

August 17, 2025August 17, 2025 ~ cmonsen ~ Leave a comment

The one where Craig remembers he has a blog.

The last time I wrote here was 2019. I didn’t know it then, but life was about to change in every possible way. By the spring of 2020, COVID had arrived, work felt all-consuming, and my wife and I were expecting our first child. It was terrifying but also grounding. In a world where almost nothing felt certain, the fact that a baby was coming felt more real than anything else.

That moment marked the beginning of a new identity, one that has taken me pretty much five years to develop a comfort. Especially as our second child joined us, I grappled with what this all would mean for remaining the partner I wanted to be with my wife and the professional I wanted to be at work.

My experience of travel describes the shift conveniently. Before, I traveled often: for work, for exploration. With COVID my travel fell off a cliff. Initially, that was the same reason as everyone else but later became more about balancing the demands of family/home life. Recently, for the first time in years, my wife and I traveled on our own, just the two of us, to the Azores. (Thanks mom, one of two people reading this, for watching the kids). Being away together reminded me not just of how travel shapes me — but how I, in turn, use my experiences to shape the things that matter most to me: helping me and those around me learn and grow, deepening relationships, and applying my skills and background where they can be most useful.

I think it is fair to say I am settling into a clumsy side-hug of being a parent alongside a professional life where I can support others to meaningfully thrive and grow at work. Indeed, certain things have felt even truer to me now when they arise in one moment while managing a complex 5-year program and in the next moment while trying to convince a 5-year-old it is time for bed.

Over these years, we have been doing a lot in my fields of health tech, AI, and value-based care: thinking, learning, and growing. Though I have been commenting on this evolution aloud in my living room — as cruel and unusual for my wife and family as it sounds — I think it will be differently fun and instructive for me and others to process the thoughts more visibly.

And, it feels like a great time to pick up the pen. I’ve dictated most of this, “vibe written” other parts, and I’m looking forward to rescanning the past in my notes, curating with the help of AI, and sharing more ideas. I’d like to prioritize presence over polish. I beg your forgiveness in advance if this starts to resemble too much AI-enabled slop as I rediscover my voice.

So — first, after five years, this post. More to come.

Medication cost transparency: does it make a difference?

April 15, 2019April 27, 2019 ~ cmonsen ~ Leave a comment

Medication costs make up over 10% of health care dollars and are the fastest growing source of medical expense. Many state and federal officials have called for action that would add cost transparency to the complex, opaque medication supply chain believing that this will lead to lower prices and higher value care. Though this may appeal to our intuition, evidence that medication cost transparency leads to higher value care is scant. In order to add to the evidence base, four other investigators and I embarked on a 9-month investigation designing and evaluating the effect of medication cost transparency decision support built into the electronic health record. Bottom line: it makes a difference. The results were recently published and are available on the JAMIA website: https://academic.oup.com/jamia/advance-article-abstract/doi/10.1093/jamia/ocz025/5445905.

Continue reading →

Digital Health: “What’s taking so long?” Part 2 of N

September 5, 2018September 5, 2018 ~ cmonsen ~ Leave a comment

For all the ways that technology has visibly transformed our lives as consumers over the last decade, it has seemed like just a matter of time before the excitement of big data, social, local, mobile, process automation, artificial intelligence, and blockchain (nb. use of buzzwords intentional) will make their way into helping us meet the aims of precision medicine and population health. Though I am quite convinced that health care as an industry can be one of the most rapidly changing, I think it is fair to say that the health care consumer (ie patient) experience has remained fundamentally unchanged during this period. It feels, if anything, that the gap is only getting wider. What’s taking so long?