Ten Years Ago, They Told Us to Stop Training Radiologists

The 30-second version

• In 2016 a Nobel laureate told the world to stop training radiologists. There are more radiologists now than there were then, and the wait for an MRI is still measured in months.
• Everyone tells the ATM story half-finished. Teller jobs did rise, and are now projected to fall 13.2%. Cashiers are down roughly 700,000 since 2019 and still falling.
• But radiology is not retail, and the reason is demand, not prestige. Retail demand is roughly fixed, so faster checkout means fewer checkers. Imaging demand is nowhere close to met. Free capacity in a system with a backlog gets absorbed, not shed.
• In one BLS projection round, office administration sheds 752,100 jobs while health care adds 2.2 million, about 37% of all new jobs in the economy. The work is moving, not vanishing. The fear is aimed at the wrong target.

Ten years ago a Nobel laureate told the world to stop training radiologists. I am a radiologist. I am still here, and so is everyone I trained with, and the waiting list for an MRI in this country is still months long. That gap between the prediction and the reality is worth understanding, because we are about to make the same category of mistake again.

In 2016, Geoffrey Hinton stood at a seminar in Toronto and said that anyone working as a radiologist was "like the coyote that's already over the edge of the cliff" and had not yet looked down. He said people should stop training radiologists immediately, because deep learning would beat them within five years. He is one of the most important scientists of the century and he was not being cynical. He genuinely believed it.

Medical students believed it too. Some of them changed their plans.

Here is where things actually landed. The number of practicing radiologists in the United States rose 12% between 2010 and 2022, from 34,328 to 38,306, which works out to a move from 11.1 to 11.5 radiologists per 100,000 people. One large midwestern academic medical center has reportedly grown its radiology staff by 55% since 2016, to roughly 400 radiologists, though that figure comes from press reporting rather than a peer-reviewed source. Meanwhile, imaging volume kept climbing and turnaround times kept stretching. The prediction did not just miss. It missed in the opposite direction.

The interesting part is who is quoting the story now. Jensen Huang has spent the last year telling audiences at Davos and elsewhere that radiology is the proof that AI creates jobs rather than destroying them. I want to be careful here, because a lot of people have heard "Jensen Huang says AI is coming for radiology" and that is backwards; he is arguing the opposite. But his version has its own problem. He claims hospitals are hiring more radiologists because of AI. That causal story is not supported. Imaging volumes have been growing steadily for decades for reasons that have nothing to do with machine learning, and interpretation speed has barely moved. We are hiring because we are drowning, not because algorithms freed us up.

Both the doom version and the triumph version get radiology wrong in the same way. They treat the job as a task, and the task as the job.

What I think, and where this series is going

This is the first of three posts, and I want to state my position rather than meander toward it. I think this is the most opportune moment in my professional lifetime to change how care is actually delivered, and that almost nobody is arguing about the right thing. The public fight is over whether a model can out-read a human. What will actually decide whether patients are better off is whether we can finally match expertise to need.

Across the three posts, here is what I believe:

• The work redistributes rather than disappears. Health care cannot fill the jobs it already has. The fear of mass unemployment and the reality of a staffing crisis are unfolding in the same room, and the thing standing between them is retraining nobody has funded. That is this post.
• Triage becomes the organizing principle, and the pair outperforms either half, but only if the human is genuinely in the loop and somebody is monitoring the machine forever. That is the second post, on what the evidence actually shows.
• Reach matters more than accuracy, and patients level up the most. The knowledge asymmetry that defined the exam room for a century is closing from the patient's side, whether or not medicine approves. That is the third.

None of it is inevitable. It is a choice about what we build, not a forecast we are waiting on.

The ATM story, and the half of it nobody tells

Whenever this argument comes up, someone brings up bank tellers. It is the most-cited analogy in the automation debate and it is worth getting right.

The economist James Bessen went and collected the data. As ATMs spread across the United States, the number of tellers needed to run an average urban branch fell from about 20 to 13 between 1988 and 2004. But a cheaper branch is a branch worth opening, so banks opened 43% more of them in urban areas. Total teller employment held steady and even rose. The remaining work shifted toward relationships and problem-solving, the parts a cash dispenser could not touch.

That is the version everyone tells. Here is the part that gets left out: it did not last. The Bureau of Labor Statistics now projects teller employment to keep falling, because online and mobile banking eventually automated not just the cash-handling task but most of the reasons to walk into a branch at all.

Cashiers are the sharper case, and it is worth being concrete, because people wave at self-checkout without ever citing a figure. In May 2019 there were roughly 3.60 million cashiers in the United States, one of the largest occupations here. By 2025 there were 3.11 million. The BLS projects 2.91 million by 2035, a further loss of 200,600 jobs and a 6.5% decline, still the largest projected drop of any occupation in the country. Tellers are on that same table now, projected to fall 13.2%.

That is close to 700,000 cashier jobs gone or going in fifteen years. Not zero. Not a myth. The largest.

So the two stories are not the same story, and the difference is the whole point. Tellers rose while machines handled part of the job and are only now sliding, decades later, once phones removed the reason to enter a branch at all. Cashiers are further down that same road: the scanner and the payment terminal took the core of the work, the customer absorbed the rest, and one attendant now watches six lanes where four people used to stand.

The real lesson is not "automation never displaces anyone." It is a lesson about how much of a job gets automated, and it has a shape.

The question is not whether automation displaces people. It is where on the curve you are standing, and which way you are moving. The curve itself is a conceptual diagram drawn to organize the argument; nobody has measured this shape directly, and the horizontal axis has no units. The cashier figures below it are real: about 3.60 million in May 2019 (BLS Occupational Employment and Wage Statistics), 3.11 million in 2025 and a projected 2.91 million in 2035 (BLS National Employment Matrix, 2025–2035 round). Those come from two different BLS programs with slightly different methods, so read the trend rather than the exact differences. Teller figures from Bessen's IMF analysis.

So which part of the curve is radiology on? Before I answer that, there is a second half to the cashier story that the employment figures do not capture, and I notice it every single week.

I used to dread Costco. The checkout line was the thing you planned the trip around, the reason you talked yourself out of going. Now I walk through it. The scanners, the app, the reconfigured front end: the experience is dramatically better, for me and honestly for the person working there, who is solving problems instead of dragging four hundred items across a piece of glass. The company benefits, the customer benefits, and the work that remains is more interesting than the work that went.

Both things are true at once. The experience got much better and several hundred thousand of those jobs went away. I am not going to pretend otherwise to make my argument tidier.

But here is the structural difference, and it is the hinge of everything that follows. Retail demand is roughly fixed. There is a certain amount of shopping to be done in a week, so making checkout twice as fast means needing about half as many checkers. Imaging demand is not fixed, and it is nowhere close to met. There are scans sitting unread right now. There are people waiting four months for an MRI. There are patients whose scan will never be ordered at all because the queue makes ordering it pointless, and whose disease will therefore be found later than it should have been.

When you free capacity in a system with a fixed amount of work, you shed people. When you free capacity in a system drowning in unserved need, the capacity gets absorbed. Radiology is the second kind, and that is an argument about demand, not about how special radiologists are.

Which means the question that matters is not where we sit on that curve. It is how big the backlog is. So let me show you.

The constraint is not accuracy. It is capacity.

Almost every public argument about AI and radiology is an argument about accuracy: can the model see the nodule, can it beat the human. That is the wrong axis. Accuracy is not what is failing patients right now.

What is failing patients is that there is not enough of us, and there is more and more imaging.

A study of nearly 136 million imaging examinations across seven US health systems and all of Ontario found that between 2000 and 2016, CT use in older adults rose from 204 to 428 exams per 1,000 person-years, and MRI from 62 to 139. Both roughly doubled. Growth slowed in later years but never reversed. Over a broadly overlapping period, the number of radiologists per capita in this country moved by less than half a radiologist per 100,000 people.

Two curves that were never going to meet. The imaging figures come from Smith-Bindman's 2019 JAMA cohort (2000–2016); the workforce figure from a 2026 JACR analysis (2010–2022). The windows do not match, so read this as two separate trends placed side by side rather than a single like-for-like comparison. It is a picture of direction, not a calculation.

And the people absorbing that gap are not doing well. In a survey of a large coalition of physician-owned US radiology practices, 46% of radiologists met criteria for burnout and only 27% reported professional fulfillment. Taking call was the strongest associated factor. That survey had a 20.6% response rate, which means burned-out people may have been more motivated to answer, so treat the exact number loosely. The direction is not in dispute by anyone who works in a reading room.

In England, a 2026 review reported a 30% shortfall in clinical radiologists, projected to reach 40% by 2028.

This is the actual problem. Not "can a machine see the lesion." It is that scans are sitting unread, MRI slots are months out, and the people reading are running on empty. If you frame AI as a contest for who is the better reader, you have not even engaged with the thing that is hurting patients.

And it is not only radiologists

Step back from imaging and the picture gets genuinely strange, because the thing everyone is afraid of is not the thing that is happening.

The staffing crisis in American health care is not approaching. It is here, it has been here for a decade, and it keeps getting worse. In the ASRT's 2025 staffing survey, the CT technologist vacancy rate reached an all-time high of 19.4%. MRI was 17.4%. Cardiovascular interventional technology, 17.4%. Every imaging discipline surveyed sat above its 2020 level. Two years earlier the radiographer vacancy rate had hit 18.1%, up from 6.2% in 2021. The survey drew 475 department managers, so hold the decimal points loosely, but nobody who runs a department needs a survey to know this.

The jobs are open right now. Bars are zero-based and share one scale. Every discipline in this survey sits above its 2020 rate. From the ASRT 2025 Radiologic Sciences Staffing and Workplace Survey, which collected responses from 475 US radiology department managers. That is a small sample, so treat the decimal points as indicative rather than precise.

Now read that next to the volume figures from earlier in this post. CT use in older adults doubled. The CT technologist vacancy rate is nearly one in five. Those two facts together describe a queue, and the queue is made of people.

Then look at what the government actually projects, from the same 2025–2035 release I have been quoting on cashiers. Private health care and social assistance is projected to add more than 2.2 million jobs, the most of any sector, accounting for roughly 37% of all new jobs in the entire economy. Healthcare support and healthcare practitioners are the two fastest-growing of all 22 major occupational groups, and together they are expected to supply almost a third of every new job created through 2035. Nurse practitioner is the single fastest-growing detailed occupation in the country, at 41%.

In that same release, the group projected to shrink fastest is office and administrative support, shedding 752,100 jobs, the largest decline of any major occupational group. The BLS attributes it in plain language to automation, including AI.

Same document. Same decade. Opposite directions. Bars are drawn to a common scale from zero, so their lengths are directly comparable. The health care figure is reported by BLS as "more than 2.2 million," so the bar is a floor rather than an exact value. Source: BLS Employment Projections, 2025–2035, released 27 August 2026.

One government document, one projection round: office administration sheds three quarters of a million jobs while health care absorbs a third of all the new ones. That is not a story about work disappearing. It is a story about work moving.

I find the public conversation about this disorienting. I read that AI is about to leave people without jobs, and then I go to work in an industry that cannot fill the jobs it already has, where those unfilled positions are the direct reason somebody waits four months for a scan, and where the shortage has been deepening steadily since before large language models existed. The openings are posted. Where are the people?

So the question is not whether there is work. There is an enormous amount of work. The question is whether we are willing to move people toward it.

That requires being honest about which way things travel. Some roles genuinely are easier to automate, and cashiering is the clean case: what the customer needs is a fast, accurate, low-friction transaction, and a machine now delivers that well. Other roles are close to unautomatable on any near horizon, and they are disproportionately in health care. Turning a patient. Getting a difficult IV. Positioning someone who is in pain for a scan without hurting them more. Noticing that a person is frightened and doing something about it. Those are not knowledge tasks with a hands-on component. They are hands-on tasks with a knowledge component, and the order matters.

None of the moving happens by itself. Somebody has to fund the training pipelines, build bridges out of declining occupations into growing ones, and pay for the years in between. Enrollment in radiologic technology programs has been falling while the vacancies climb, which tells you the market signal is not reaching the people who could act on it. A labor market does not clear just because an economist can see that it ought to.

This is the part I would most like people to hear, because the fear is real and it is aimed at the wrong target. The jobs are not vanishing. They are relocating, into work that is harder to automate and, in most cases, more worth doing. What we owe people is the ladder to get there.

What this post does not tell you

Everything above is an argument about demand and labor. It is the argument I most want people to hear, because the fear is real and it is pointed at the wrong target. But it is also, deliberately, an argument that dodges the hardest question.

None of it tells you whether the technology actually works.

A staffing crisis is a reason to want a tool. It is not evidence that the tool is any good, and "we are desperate" is close to the worst frame available for a purchasing decision. There are real randomized trials now, some of them genuinely impressive. There is also a study in which very experienced radiologists went from scoring 82% of mammograms correctly to 46%. The difference was that the AI handed them the wrong answer.

That is the next post.

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How this piece was built

I structured this on Randy Olson's And, But, Therefore framework, which keeps an argument from collapsing into a list of statistics. Every figure here was checked against the primary source, and where a survey is small or two datasets are not strictly comparable, I have said so in the text rather than hiding it in a footnote.

AI disclosure. The argument, the experience, and the point of view are mine. I worked with Claude (Anthropic) as a research and drafting partner: it verified every number against BLS tables, ASRT survey releases, and peer-reviewed sources, produced the four figures, and helped organize the draft. It corrected two things I had wrong going in, which I have left visible in the text: the 2016 prediction was Geoffrey Hinton's rather than Jensen Huang's, and my cashier figures were from a superseded BLS projection round. I reviewed every claim and citation before publishing.

References

1. US Bureau of Labor Statistics. Employment projections: 2025–2035 summary. USDL-26-1422, August 27, 2026. bls.gov
2. US Bureau of Labor Statistics. Occupations with the largest job declines, 2025 and projected 2035. bls.gov
3. US Bureau of Labor Statistics. Occupational Employment and Wage Statistics, largest occupations, May 2019. bls.gov
4. Bessen J. Toil and technology. Finance & Development (IMF), March 2015. imf.org
5. American Society of Radiologic Technologists. 2025 Radiologic Sciences Staffing and Workplace Survey. asrt.org
6. Smith-Bindman R, et al. Trends in use of medical imaging in US health care systems and in Ontario, Canada, 2000-2016. JAMA. 2019;322(9):843-856. doi:10.1001/jama.2019.11456
7. Malhotra A, et al. The evolving US radiologist pipeline: trends in residency positions, resident workforce, and practicing radiologists per unit population. J Am Coll Radiol. 2026;23(8):1587-1592. doi:10.1016/j.jacr.2026.04.005
8. Parikh JR, et al. Prevalence of burnout of radiologists in private practice. J Am Coll Radiol. 2023;20(7):712-718. doi:10.1016/j.jacr.2023.01.007
9. Spalding A. A retrospective mixed-methods service evaluation of radiographer-led adult nephrostomy exchange service. Radiography. 2026;32(4S1):103316. doi:10.1016/j.radi.2025.103316

Peer-reviewed sources were located through PubMed and Consensus. Nothing in this post describes any individual patient or protected health information, and nothing here represents the position of my employer.

When the Hospital Came Home

My mother needed hospital care, and she got it. But the hospital also took her sleep, her movement, and every last bit of control over her own day. Then we qualified for a program that sent the hospital to her house instead, and almost everything except the medicine changed.

I am a radiologist. I have spent years inside hospitals, reading images for patients I rarely meet, trusting that the system on the other end of my report works the way it is supposed to. Then my mother was admitted, and I found out what the other side of that system feels like when you are the one sitting in the chair.

She does not speak English. That single fact reorganized our whole family. One of us had to be in the room with her at all times, so my siblings and I built a rotation and lived inside it. The room itself was lovely. Big windows, warm light, a recliner that folded back into something the brochure would probably call a bed.

It was not a bed. I know, because I spent nights in it.

The part nobody warns you about

Here is what I did not expect: the exhausting part was not the worry. It was the interruptions.

Vitals at midnight. A blood draw before dawn. An IV pump alarming at two in the morning. Someone coming in for weights, then someone else for the morning labs. My mother never got a full night. Neither did I. We were both awake at 4 a.m. in a room designed to make sure nothing was ever missed, which also meant nothing was ever quiet.

And then daylight brought the other problem: waiting. Several subspecialists were consulting on her case, and I never knew when any of them would appear. Rounds happened sometime. The nurse came sometime. The doctor will see you now, except no one could tell you when now was going to be.

So I did not leave. I skipped meals and held it and stayed put, because stepping out for ten minutes meant possibly missing the one conversation I had been waiting fourteen hours to have. I could not pick up my daughter. I could not be in two places. I sat in a beautiful room and felt completely trapped in it.

Meanwhile my mother sat too. Bed, chair, bed. Day after day, a woman who runs her own household barely moved twenty feet.

The care was excellent. The experience was not. Those turn out to be two different things, and the difference has a name in the literature.

The hospital itself is a stressor

In 2013, the Yale cardiologist Harlan Krumholz gave this a name in the New England Journal of Medicine: post-hospital syndrome. His argument is that the month after a hospital stay carries a broad, elevated risk of getting sick again, and much of that risk comes not from the original illness but from what the hospital did to the person while treating it. Sleep gets shredded. Nutrition suffers. People stop walking. Days lose their edges.

Once I read that, everything I had watched in that room stopped feeling like bad luck and started looking like a predictable pattern. The numbers back it up.

47 min

Less sleep per night in the hospital than the same patients got at home

683 older medical inpatients, four hospitals (Smichenko 2025)

57%

Of observed daytime hours, inpatients of all ages spent lying in bed. Nine percent standing or walking.

132 inpatients, behavioral mapping (Mudge 2016)

30%

Of hospitalized older adults go home less able to do a basic daily task than when they arrived

Meta-analysis, 7,375 patients (Loyd 2019)

That last one has a clinical name too: hospital-associated disability. Someone walks in able to bathe or dress themselves and walks out unable to, and the thing that took it away was the stay, not the illness. A large prospective study found that in-hospital mobility, continence care, and length of stay together explained 64% of the variation in who declined by discharge. Those are all things a system chooses.

Bed rest is often not even a medical decision. In a study of 498 hospitalized adults over 70, a third had bed rest ordered at some point, and among the least mobile patients, nearly 60% of those bed rest episodes had no documented medical reason at all. We immobilize people out of habit.

The sleep piece is just as fixable and just as stuck. When researchers asked patients, physicians, and nurses what wrecks sleep in the hospital, all three groups named the same top three: pain, vital signs, and tests. Everyone knows. It happens anyway.

And the language problem sitting underneath all of it

My family's rotation existed because my mother could not advocate for herself in English. I used to think of that as our private logistics problem. It is actually a documented safety issue.

In a study of 1,666 families across seven North American hospitals, children whose parents were not comfortable speaking English in medical settings had roughly twice the odds of experiencing a harm caused by their medical care (17.7% versus 9.6%). A companion study across 21 hospitals found families with limited English proficiency were dramatically less likely to speak up when something looked wrong, or to question a clinician's decision. Both studies looked at hospitalized children rather than adults, so I hold the specific numbers loosely. The direction is not in doubt, and it matched our experience exactly. Being physically present was our workaround for a system that could not hear her.

Then we qualified for hospital at home

Hospital at home is not new. Versions of it have run for decades in Australia and the United Kingdom. What is new in the United States is scale, and the reason is technology plus a Medicare waiver. The model works like this: a physician-led team runs your care from a command center, in-person visits come to your house, and everything that can be done remotely is. You are formally an inpatient. You are just an inpatient in your kitchen.

A program sets you up at home with a tablet, a blood pressure cuff, a scale, an oxygen monitor, an emergency alert you wear, a direct-dial phone to the command center, plus a wifi extender and a backup power supply because the whole thing depends on staying connected. Physicians, nurse practitioners, pharmacists, nurses, social workers, and paramedics all work off the same plan.

My mother qualified. Here is the day that followed.

Same illness, same medicine, two different days. A composite of our experience, not a chart of measured data. The hatched blocks on the left are the part that wore me down: care that was definitely coming, at a time nobody could tell me.

What actually changed

My mother started moving. Not because anyone prescribed it, but because she was in her own house and there was laundry to fold and dishes in the sink. She got up. She walked around. She did her own things. Within a day she was doing more than she had done in a week of lying in a beautiful room.

I slept. Fully, in my own bed, and nobody came in at 2 a.m. I picked my daughter up. I was in the same building as both the person I care for and the person I am raising, which had felt impossible for weeks.

And the schedule became ours. The nurse came at a time we knew. The blood draw had a window. I could actually schedule my mother's visit with the primary team, which meant I could plan a day around it instead of surrendering the day to it. Consultants still appeared without warning, but they appeared on a screen for a few minutes rather than being an eight-hour vigil.

There was one more thing I did not anticipate. In the hospital, I felt guilty calling the nurse. Towels, another gown, a cup of coffee: I knew how busy she was and I could see her running, so I sat on small needs and let them stack up. At home, a nurse was one button away, twenty-four hours a day, and I used it without hesitation, because now every call I made was actually about my mother's care. The small stuff was just ours to handle. That reallocation felt better for everyone.

It was not only that the care moved. It was that we got the environment back. The medicine stayed the same and the power over the day came home with her.

I thought this was my private observation until I found a study that had written it down. Researchers interviewed patients from a randomized home hospital trial and found that home patients described "a locus of control surrounding their sleep, activity, and environmental comfort" that hospitalized patients simply did not have. That is the whole thing, in the dry language of qualitative research. Not comfort. Control.

Does it actually work, or does it just feel better?

This is where I put my radiologist hat back on, because a good feeling is not an outcome. The honest answer is that hospital at home holds up on safety and wins clearly on experience and activity, while the cost and readmission findings are real but less consistent than the enthusiasm suggests.

Traditional hospital Hospital care at home

Two separate randomized trials. Compare the two bars within a panel, never across panels. The Boston trial (Levine 2020) randomized only 91 highly selected patients at two sites, with 63% of eligible patients declining to participate, so treat those two panels as promising rather than settled. The comfort figure comes from a larger 2025 trial of 1,150 patients (Maniaci 2025).

The strongest single piece of evidence is a randomized trial published in 2025. It randomized 1,150 acutely ill patients across three hospitals to hospital-at-home care or a traditional bed. The combined rate of death or unplanned readmission within 30 days was 17.3% at home and 19.8% in the hospital, which met the trial's bar for showing home care is not worse. No patient died while receiving their hospital care at home. And on comfort, the gap was wide: 84.4% versus 60.9%.

The broader picture, from a Cochrane review of 20 randomized trials covering 3,100 people, is consistent. Hospital at home probably makes little or no difference to death rates or readmissions, probably lowers costs, and probably makes people meaningfully less likely to end up living in a nursing home six months later. That last finding deserves more attention than it gets.

Two honest caveats. First, cost savings are not automatic: when Levine's group ran the same model in rural communities in 2025, the episode cost came out no different from a regular hospital stay, even though patients took roughly seven times more steps per day and rated the experience far higher. Second, almost every one of these trials enrolled carefully selected, relatively stable patients. That is exactly who the program is for, and it is exactly why you cannot generalize the results to everyone in a hospital bed.

Where it is imperfect, including for us

I do not want to write a brochure. We had real friction.

The tablet needed rebooting. We had connection problems. When technology is the spine of your care, the spine occasionally goes out. What made it workable was that the program planned for exactly this: there were two separate backup ways to reach the team while the tablet was being sorted out. Redundancy is not a nice-to-have in this model, it is the safety system. If you are evaluating a program, ask what happens when the internet drops, and do not accept a vague answer.

The bigger caveat is the one the research keeps flagging and the marketing keeps skipping: this model leans on the family. In a study of 125 caregivers assessed in the first 48 hours of a hospital at home admission, 61.6% already met the threshold for high caregiver strain. Interviews with caregivers in the United States and Denmark found the same pattern: they overwhelmingly preferred it to a hospital stay, and they also felt underprepared, unclear about what was their job versus the team's job, and sometimes overwhelmed.

I had three siblings, a flexible enough job, and clinical training. That is not most families. A model that quietly assumes a capable, available caregiver will work beautifully for people who have one and will not be offered to people who do not. That is an equity problem sitting right in the middle of a very good idea, and it should be designed for rather than discovered later.

If a program is offered to your family, ask these

• Exactly what am I responsible for, and what is the team responsible for? Get it in writing.
• What are the backup ways to reach you if the tablet or the internet fails?
• How fast can someone physically get to the house, and who is that person?
• Can we schedule the daily physician visit, or does it just happen?
• What triggers a transfer back to the hospital, and how does that work at 3 a.m.?
• Is interpretation built into every visit, or does the family have to arrange it?

Why I am excited about this beyond my own family

For twenty years, nearly every effort to improve the patient experience has aimed at making the in-person visit better. Nicer rooms. Better food. Softer lighting. My mother's room proved the ceiling on that strategy: it was a genuinely beautiful room, and it still took her sleep, her movement, and her control, because those losses are structural rather than decorative.

Remote monitoring plus a command center does something a renovation cannot. It keeps the medicine and drops the institution.

And it frees a bed. Australia's Victorian hospital-in-the-home program was described in one paper as "the 500-bed hospital that isn't there." Every stable patient treated at home is a bed available to someone who is critically ill and genuinely needs hands on them, in a country where capacity is the binding constraint on almost everything. My mother going home was not just better for my mother. It was better for whoever got that room.

The policy question is settled for now. Congress extended the Medicare waiver through 2030 as part of the Consolidated Appropriations Act, 2026. As of that extension, 366 programs across 139 health systems in 37 states were approved to deliver acute hospital care at home. Five years of stability is enough runway for health systems to actually build rather than pilot.

Framed against the Quintuple Aim, the goals most of us in health care now organize around, this model plausibly moves four of the five at once: outcomes hold, experience improves substantially, costs trend down in most settings, and freed capacity helps the sickest patients. The fifth, equity, is the one that will not take care of itself. It depends entirely on whether programs get built for families who do not already have a spare adult and a strong wifi signal.

What I keep coming back to

My mother received the same medicine either way. Same labs, same monitoring, same physicians. What changed was that she got to fold her own laundry, sleep through the night, and eat lunch at her own table, and I got to be a daughter and a mother on the same day instead of choosing.

For years I assumed the goal was a better hospital. I think the actual goal is needing the hospital for less.

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How this piece was built

I structured the story on Randy Olson's And, But, Therefore framework, which is a simple way to keep a narrative from collapsing into a list of facts. Three published frameworks shaped how I read my own experience: Krumholz's post-hospital syndrome for why the hospital itself is a stressor; the Age-Friendly Health Systems 4Ms (What Matters, Medication, Mentation, Mobility) for naming what changed at home; and the four core concepts of patient- and family-centered care (respect and dignity, information sharing, participation, collaboration), which is where the agency argument actually lives.

AI disclosure. I wrote this from my own experience and my own point of view. I worked with Claude (Anthropic) as a research and drafting partner: it searched PubMed and Consensus for the peer-reviewed evidence cited here, verified the trial numbers against the source abstracts, identified the narrative and conceptual frameworks above, produced the two figures, and helped me organize and tighten the draft. I directed the argument, supplied the experience, and reviewed every claim and citation before publishing.

References

1. Maniaci MJ, et al. Safety in a hybrid hospital-at-home program versus traditional inpatient care: a pragmatic randomized controlled trial. J Hosp Med. 2025;20(11):1174-1184. doi:10.1002/jhm.70076
2. Levine DM, et al. Hospital-level care at home for acutely ill adults: a randomized controlled trial. Ann Intern Med. 2020;172(2):77-85. doi:10.7326/M19-0600
3. Levine DM, et al. Hospital-level care at home for adults living in rural settings. JAMA Netw Open. 2025;8(12):e2545712. doi:10.1001/jamanetworkopen.2025.45712
4. Levine DM, et al. Hospital-level care at home for acutely ill adults: a qualitative evaluation of a randomized controlled trial. J Gen Intern Med. 2021;36(7):1965-1973. doi:10.1007/s11606-020-06416-7
5. Edgar K, et al. Admission avoidance hospital at home. Cochrane Database Syst Rev. 2024;3(3):CD007491. doi:10.1002/14651858.CD007491.pub3
6. Krumholz HM. Post-hospital syndrome: an acquired, transient condition of generalized risk. N Engl J Med. 2013;368(2):100-102. doi:10.1056/NEJMp1212324
7. Loyd C, et al. Prevalence of hospital-associated disability in older adults: a meta-analysis. J Am Med Dir Assoc. 2020;21(4):455-461.e5. doi:10.1016/j.jamda.2019.09.015
8. Brown CJ, et al. Prevalence and outcomes of low mobility in hospitalized older patients. J Am Geriatr Soc. 2004;52(8):1263-1270. doi:10.1111/j.1532-5415.2004.52354.x
9. Zisberg A, et al. Hospital-associated functional decline: the role of hospitalization processes beyond individual risk factors. J Am Geriatr Soc. 2015;63(1):55-62. doi:10.1111/jgs.13193
10. Mudge AM, et al. Poor mobility in hospitalized adults of all ages. J Hosp Med. 2016;11(4):289-291. doi:10.1002/jhm.2536
11. Grossman MN, et al. Awakenings? Patient and hospital staff perceptions of nighttime disruptions and their effect on patient sleep. J Clin Sleep Med. 2017;13(2):301-306. doi:10.5664/jcsm.6468
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13. Khan A, et al. Association between parent comfort with English and adverse events among hospitalized children. JAMA Pediatr. 2020;174(12):e203215. doi:10.1001/jamapediatrics.2020.3215
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Peer-reviewed sources were located through PubMed and Consensus. Nothing in this post describes anyone's diagnosis or protected health information.

What a Year of Building AI Workflows Actually Looks Like

Most conversations about AI in radiology ask whether it will read the images. That has never been the part of my job that consumes me. What consumes me is everything stacked around the images: the report I have to reconcile against six prior studies, the lecture that needs twelve good cases pulled from ten years of archives, the call schedule that has to satisfy a dozen rules, the license I have to renew on a website designed by someone who has never renewed one.

That is where AI has changed my work. It does not read for me. It clears the ground so I can read.

I want to describe what that actually looks like, because most of what I read about AI in medicine is either breathless or dismissive, and neither matches my experience. Mine has been slower and more mundane than the hype, and more useful than the skepticism.

One note before I start. Everything below runs inside enterprise, HIPAA-covered instances of these tools that my institution has approved, and the research is IRB-approved. None of this involves pasting patient information into a consumer chatbot, and I would not advise anyone to do that.

Reporting: the workflow I measured

Every one of my reports now goes through a custom GPT.

I built a set of them in ChatGPT Enterprise, one per modality, each with disease-specific templates. I dictate into the ChatGPT dictation button rather than into PowerScribe. The dictation is simply better, and that is the whole reason I switched. It is the one thing I use ChatGPT for exclusively, and everything else sits on top of it.

Three of these have become indispensable.

The "MISS" template. I paste in all the prior radiology reports along with my current draft, and it checks my current report against the priors to confirm I have addressed every finding someone described before. This one earns its keep on oncology studies, where a patient may have eight prior scans and a dozen tracked lesions. What goes wrong there is not that I misread something. It is that a finding described eighteen months ago quietly drops out.

The revised-report template. I review the prior findings, tell it what to change, and it writes a new draft.

Fat fraction. I paste in the in- and opposed-phase images with ROIs over liver and spleen, and it hands back a calculation formatted and ready to drop into the report. No calculator, no retyping.

I studied this rather than just claiming it worked. I published the results in Abdominal Radiology, framed through the Unified Theory of Acceptance and Use of Technology, comparing my own baseline period against my post-implementation period across 609 studies.

The findings split cleanly. For outpatient CT, the gains were large. With contrast, my median inter-study interval dropped from 23 minutes to 13. Without contrast, it dropped from 18.5 minutes to 7. Both were significant. For MRI, nothing clear. With contrast the numbers drifted slightly the wrong way, and without contrast the improvement looked big but did not survive correction for multiple comparisons.

I think that null result is the most honest and most useful thing in the paper. Standardized, high-volume CT is exactly the task a templated LLM workflow fits. Complex MRI is not. The cases vary more, the templates are harder to build, and you cannot reduce the reasoning to a form. How well the tool fits the task is doing the real work here, not the model.

Training took ten hours across five days. That is not nothing, and it is worth saying plainly, because people underestimate what you have to put in before you get anything back.

I have also looked at what LLMs do to report accuracy, in a separate study where six radiologists reviewed GPT-4's suggested revisions to 600 of their own finalized abdominopelvic CT reports. GPT-4 flagged something in 91% of reports, but the radiologists accepted only 23% of what it suggested, and most of what it caught was grammar. The clinically meaningful catches were real but uncommon.

I raise that because it tempers my own enthusiasm. The reporting workflow makes me faster and gives me a systematic second pass against priors. It does not make me infallible, and the data do not support anyone who says otherwise, including me.

Teaching: an assistant that does not go home

I am putting together a prostate case review talk for SABI 2026 in Savannah. Historically this takes weeks. Think of the teaching points, hunt through the archive for cases that actually demonstrate them, then chase down the pathology and the clinical follow-up.

Now I brainstorm the case list with Codex, then point it at our enterprise Illuminate instance, which holds the radiology, pathology, and clinical notes together. It pulls the cases while I look them up on PACS and grab images for the slides.

The moment it clicked for me was small. Codex found me a Müllerian duct cyst, but it measured 1.6 cm, too subtle to teach from. I asked for something bigger. It came back with a 15 cm cyst, now too dramatic to represent anything. I asked for something in between and got it.

That exchange took under a minute, and I would never have asked a human assistant to redo the same task three times in a row. It feels like working alongside a superintelligent assistant who does not get tired of my revisions.

Scheduling: the work nobody wants

I run the schedule for the Abdominal Radiology Case Conference, and scheduling has always been the most thankless part of it. Email everyone. Remind them to submit their availability. Reconcile what everyone gives you against the rules. Send calendar invites. Chase the people who never replied.

A student intern and an administrative assistant used to handle it, which worked until someone traveled or a new person came on, and then it broke and left gaps.

Claude now runs the whole cycle: the outreach, the reminders, the rule-based matching, the invites, the follow-ups. So far it is working well.

Our clinical schedule in QGenda got the same treatment. The QGenda rules are not hard, they are just tedious, which is exactly the kind of task worth handing off. I built a Codex project, gave it all the definitions and rules, told it what I wanted, and it took care of it. I have since found I prefer Claude for this particular job, because it gives me a visual dashboard and tracks my schedule as it changes rather than just answering once.

A smaller one in the same vein. I attend a weekly educational meeting where sessions sometimes get cancelled, the instructor changes, and each instructor sends a different Zoom link. All of it arrives by email. Now I hand Claude the email and tell it to update my calendar with the right link and the right instructions, so what is on my calendar always matches what is actually happening that week.

Research: the long tail

I have a project running now on discordant prostate MRI, meaning patients whose MRI was positive but whose biopsy came back negative, across roughly 500 patients. Codex is working through the follow-up MRIs, repeat biopsies, notes, and labs.

I layer the tools, and the layering is the point. I use enterprise Claude to write the instructions for Codex, then to troubleshoot when Codex gets stuck and to double-check what it produces. Having one model brief and audit another has caught things neither would have caught alone.

The small things, which turn out not to be small

I renewed my medical license recently. Anyone who has done it knows the actual work is trivial and navigating the site is miserable. You hunt for the right link, the right page, the right form, before you can start. Codex walked me through it and made it painless.

I mention this because it is typical. So much of my week goes to digging for the right place to begin rather than doing the work itself. Clearing that friction has changed how my days feel more than any single clinical application has.

The same holds for my student interns. I get a new one every year, and retraining them eats a lot of time. The workflows now absorb most of the repetitive tasks, so the time I spend with an intern goes toward actually teaching them something.

And announcing our division's recent publications on social media now runs automatically. Small thing. Off my plate.

Caregiving: the one I would least want to give back

I also care for an aging parent who is ill, which means a steady stream of appointments and a family calendar that has to stay in sync around them. I log into the patient portal, and Claude takes every appointment and puts it on our family Google Calendar, then keeps it updated as things shift.

None of that is hard. It is tedious, it arrives in fragments, and it never really stops. Handing it off lifted a specific kind of mental weight I had stopped noticing I was carrying. Of everything on this list, this is the one I would least want to give back.

What it gave back

Two effects I did not expect.

The reports got better, not just faster. Speed is what I set out to measure. Quality is what I noticed afterward. A systematic second pass against every prior raises the floor on a heavy day, which is exactly the day a finding from eighteen months ago slips through.

I stopped falling behind. The tasks I used to carry around as low-grade dread, the ones that were never hard but were always waiting, are now automated or scheduled. That dread took up more room in my head than the tasks ever took in my week.

What surprised me is what filled the space. I have wanted to write here for years and never had the bandwidth. This post exists because the scheduling, the calendar updates, and the reminders stopped eating the hours I would have spent on it.

It spilled into my personal life too. I have friends who fly first class and take their families on essentially free vacations using credit card points, and they have been telling me about it for years. I always found it interesting in theory, and I was never going to sit down and learn it.

So I built a Claude project instead. I gave it the collective knowledge from the physician points community I follow, the transfer rules between programs and their partners, my credit cards, all of my points accounts, and my travel goals for 2027, and I let it work.

It went through my cards and told me I could drop my Sapphire Reserve and save roughly $800 a year, but that I should downgrade it to a Freedom card rather than cancel it. Downgrading keeps my Ultimate Rewards points alive and avoids putting a closed account on my credit history. I had no idea downgrading was even an option. It also showed me that my other premium cards already carry most of the benefits I was paying the Reserve for, so I had been paying twice for the same thing.

That is one example out of many. The pattern is the same as it is at work. The barrier was never that any of this was difficult. The barrier was that I was never going to make the time.

What I have actually learned

Each tool is good at something different, and it is worth finding out what. ChatGPT dictates best, so all my reports go through it. Codex is where I work across systems. Claude gives me better visual, structured output, which is why my schedule lives there. I did not decide this in advance. I found it by using all three badly for a while.

You have to invest a lot before you get anything back. Building these workflows took hours I did not obviously have, and I still spend many hours a day working with AI. But once a workflow is built, it runs close to seamlessly, and it pays off a little more every week.

Fit matters more than raw capability. My own data showed a large benefit for CT and none for MRI, with the same model and the same radiologist. What differed was the task, not the technology. I would rather build three workflows that fit than ten that impress.

Measure it. I have written before that you cannot improve what you cannot measure, and I meant it about teaching. It applies here too. It would have been easy to feel faster and never check. Some of what I believed held up. Some of it did not.

None of this replaced my judgment. It cleared away the things standing between me and the point where judgment is required. I still read every image and I still own every report. I just spend a much larger share of my day on the part that actually needs me.

If you want to start

A few things I would tell someone at the beginning.

Tell it to double-check its work. Every time. This is the highest-yield instruction I give, and it costs one sentence.

Use two. I keep both Claude and ChatGPT, personal and enterprise. Ask them the same question and you get different answers, and the difference is the useful part. It is complementary rather than duplicative, like having two very smart consultants who think differently. I often use one to audit the other.

Watch a short video. I got started with Codex from one 28-minute YouTube video: Learn 95% of Codex in 30 minutes by Riley Brown. That was the whole onboarding.

You do not need to code. I am not a coder. Nothing I described in this post required me to be one.

Start small. You are not going to build Rome overnight. Every workflow here began as one annoying task I decided to hand off, and they accumulated from there.

One thought I keep returning to. I am starting to want a different kind of student intern: someone who supervises and runs AI workflows rather than doing every task by hand. But that only works if the person still understands the workflow deeply. You cannot supervise a process you do not understand, and you cannot tell when the output is wrong if you have never done the work yourself. That is the part that does not get automated.

How I used AI to make this post. Claude and I co-wrote this post. I talked through my workflows in one long unedited brain dump, and Claude turned that into the structure, the section order, and the prose you just read. Claude searched PubMed for my own papers, pulled the exact figures out of them, and added a fourth reference I had forgotten I was an author on. It built all three data figures here from the published numbers. It also fact-checked me: it cut a claim I made that my reports contain no mistakes, because my own GPT-4 paper does not support that, and it caught a broken link to our case conference channel before this went live. I gave it my writing preferences and it applied them. I read every line, changed what I wanted changed, and approved the final version. The experience and the opinions are mine.

Opinions are my own.

References

• Tan N. Large language model-assisted radiology reporting in a single-radiologist implementation: a retrospective cohort study interpreted through a UTAUT lens. Abdominal Radiology (NY). 2026. doi:10.1007/s00261-026-05524-y
• Mayes CJ, Reyes C, Truman ME, et al. Improving radiology reporting accuracy: use of GPT-4 to reduce errors in reports. Abdominal Radiology (NY). 2025;51(1):513-520. doi:10.1007/s00261-025-05079-4
• Reyes C, Nguyen E, Alexander LF, et al. Beyond Human Limits: The Promise and Pitfalls of Large Language Models in Radiology Research. Journal of Computer Assisted Tomography. 2025;49(4):545-553. doi:10.1097/RCT.0000000000001709
• Gerbaud AF, Berguido de la Guardia M, Booth SC, et al. Process Improvement Before Artificial Intelligence and Automation: Building Trust With the Understand-Transform-Sustain Framework. Mayo Clinic Proceedings: Innovations, Quality & Outcomes. 2026;10(4):100725. doi:10.1016/j.mayocpiqo.2026.100725

Replacing Self with Others

I attended my first meditation week long retreat and had a transformative experience (New Kadampa Tradition Mountain Retreat, Williams AZ, founder Kelsang Gyatso). Like many others, I was grappling with balancing work, family, and personal life. Despite being in a much better position than countless individuals worldwide, the elusive concept of work-life balance remained a challenge for me.


The week long meditation retreat transformed the way I thought, specifically it introduced the idea of how to reframing my approach to the world. One of the primary teachings was the idea of replacing self for others. This means to put my attention on the needs of others instead of putting that attention on myself. This shift in focus has begun to transform my approach to daily challenges.




Object of Attention: Replacing Self for Others

The concept of "Replacing Self for Others" marks a major shift in the way I/we think.  The more attention/concentration we put on an object, the more our mind and bodies channel our energy around that object. So much of the way I thought prior to the Meditation Retreat was about what I needed to do to achieve my personal goals. Take, for instance, my aspiration to advance from Associate to Full Professor. This goal required me to align research, education, and other activities to satisfy the criteria for promotion—a pursuit that was inherently self-centric and a significant source of stress, given the pressure to publish papers and secure grants. However, the retreat inspired me to view my goals through a new lens, focusing on how I could serve others, particularly my students, trainees, and colleagues. Instead of seeing promotion as an end goal, I began to consider how I could contribute to the advancement of those around me. This shift in perspective transformed my approach: aiding my students and trainees in their career paths, with publication efforts emerging as a natural outcome rather than the sole aim. This reorientation not only redefined my objectives but also imbued me with a renewed sense of purpose and energy.




Both goals (#1 full professor vs #2 helping others) achieved similar outcomes, but tapped into very different sources of energy. The energy source that came from Helping Others (#2) was positive, self-energizing force rather than stress/burden from the former (#1). When we frame our efforts and goals towards helping others, the source of energy transforms into something much more powerful and limitless. A mind shifting statement that I read during that one week meditation retreat is the idea that when we are in the service of others, we will never be lonely. This concept suggests that when our thoughts and energies are invested in helping others, feelings of isolation become untenable. Loneliness, along with other feelings such as sadness, depression, and burnout, stems from a self-focused perspective. Redirecting our attention outward effectively dispels these sentiments, anchoring us in a mindset geared towards communal support and connection. It's a very simple idea but execution is much harder. Yet with small practices that I've been able to do in reframing my efforts, the benefits that I've reaped have been tremendous. I'm no longer worried about all the things that I have to do and get done as part of my obligations / commitments. Now I think about what can I do to help those that I can help and how can I do that. Through the latter lens, I become liberated and my ideas are more free flowing and my efforts become more natural.


It's crucial to understand that prioritizing the well-being of others doesn't mean neglecting our own. A fundamental aspect of Buddhist philosophy is the harmonious balance between Compassion and Wisdom. Compassion motivates us to serve others, a hallmark of a fulfilling life. Yet, this must be tempered with Wisdom, recognizing that not all acts of service hold the same weight in terms of importance or impact. Distinguishing between what is essential and what is not allows us to channel our efforts effectively. Taking care of our bodies, health, and overall well-being is imperative. It is only by ensuring our own health and happiness—mentally, physically, and spiritually—that we can genuinely support others. The idea isn't to forsake self-care in favor of altruism; rather, it's to understand that the most effective way to assist others is by maintaining our well-being. This approach not only maximizes our capacity to contribute positively to the lives of others but also enriches our own experience.

If you want to read more, a good starting point is Kelsang Gyatso How To Transform Your Life. You can get a free pdf version online. I got the Kindle version, and used the "Text to Voice" function on my phone to have my phone read the book to me (audiobook version).




Acknowledgement: ChatGPT helped provided edits to the contents.