Can ChatGPT build a residency schedule?

Chiefly starts from your rules, not a prompt, so there is nothing to write, paste or re-explain. A program sends last year’s schedule and its requirements, and Chiefly’s team loads the program’s rules and ACGME requirements, along with the hundreds of nuances we’ve built up over many GME clients.

• Generated, then reviewed. Chiefly generates the block schedule to those rules, with the daily, clinic and call schedules built from it as one integrated system, all kept ACGME-compliant. Chiefs review drafts and make the calls.
• Every swap checked. Residents request swaps in the Swap Portal, and each is checked against your rules before a chief approves it (how swaps work).
• Changes re-run, versions saved. Your chiefs re-run the schedule themselves around parental leave, off-cycle residents and late starts, and get the result in minutes, with your rules intact and every version saved for comparison. Chiefs can change a rule and re-run, or edit by hand, and every edit is checked against the rules, just like a swap.
• Next year starts from this one. Rules, residents and settings roll over, bringing in your incoming matched residents.
• Ask Chiefly, if you want it. Programs can turn on Ask Chiefly, an AI assistant for learning Chiefly and making changes in bulk in plain language, with every edit it proposes waiting for your approval (more below).

Chiefly is used by residency programs across many specialties. Boston Children’s, with 188 residents across two hospitals, saved more than 3,600 chief and admin hours in its first eight months (case study); UVA Pediatrics built its academic year in a week and a half (case study). You can always download the schedule to Excel.

ChatGPT predicts a schedule that looks right. Chiefly checks that it is right: every rule, the same way, each time the schedule is built or changed. Two things make the difference.

Your program’s rules outlast the chief. Even when you’ve built up context in a ChatGPT conversation, that context lives in one person’s account, and it doesn’t transfer from one chief to the next. Nothing makes sure every rule is still being applied, either. In Chiefly, our team loads your rules once, and they stay in place: from one build to the next, and from one chief class to the next, for the new chiefs to keep or edit.

Chiefly never fails silently. Language models hallucinate, and they do it confidently. In scheduling that isn’t a typo: it’s a service left understaffed or a resident who doesn’t graduate on time, found weeks later. Chiefly’s optimization engine doesn’t guess, and every rule it can’t meet is flagged before anything goes out. To compare other tools, see the best residency scheduling software and what block schedule software should do.

Researchers recommend the same pairing for language models: the model proposes, a separate checker decides (ICML 2024). Rules-based scheduling is where it works: a pediatric residency that used a constraint-optimization solver, with chiefs reviewing drafts, saw the spread of calls per resident-month roughly halve (JMIR, 2026).

Yes, in the one place it helps and can’t hurt. Programs can turn on Ask Chiefly, an AI assistant built into Chiefly, and pair it with Chiefly’s proprietary optimization engine: the assistant understands plain language, and the engine, which builds and checks every schedule, does what a language model can’t.

• Learn Chiefly by asking. Ask how something works, where a setting is, what a status or rule violation means, or how to handle a scheduling situation. It answers from Chiefly’s own help material and your program’s context.
• Make changes in bulk, in plain language. “Set the minimum staffing on all ICU rotations to 3,” “Add a night float tag to every PGY-2” or “Create a requirement for 8 weeks of electives.” Ask Chiefly drafts the edits, and you can attach a spreadsheet or PDF of your rotation rules for it to work from.
• Nothing changes without you. Every edit it proposes shows up as a card with the before and after values. You choose which to accept, and nothing is saved until you review the changes and apply them. Building the schedule, exports and restores always stay in your hands.
• It never touches the solving. Ask Chiefly helps you set up and understand your program. The engine that builds your schedules works exactly the same whether it’s on or off.

Like any AI tool, Ask Chiefly can misread a request, which is exactly why it only ever proposes and never decides. It’s optional, and we turn it on for programs that ask for it.

For a small call schedule, sometimes. Published tests show ChatGPT handling small call schedules once every rule is spelled out, with a separate check of what it produced. A full-year block schedule is a different problem: how far ChatGPT or Claude gets depends on the program’s size, how many rules interact and how precisely you write them, and in our advisors’ experience it stalls on larger builds or drops rules without saying so. What neither can do is guarantee that a full year follows every rule, or prove that it does. Two recent studies, and what they found:

• Ophthalmology, 2025: it worked for five residents, once. A Yale team had ChatGPT-4 write Python code for a five-resident call schedule, with rules such as one resident a day and no back-to-back calls. After an iterative back-and-forth, the code produced a schedule that met every rule (Journal of Academic Ophthalmology). The downsides:
  • Only a separate validation script and the chief resident could confirm the schedule was right.
  • Residents couldn’t request a change or a swap through it; the result was a Google Calendar to view.
  • Nothing in it carried residents’ history from one year to the next.
  • The next chief would inherit Python code rather than a tool.
• Orthopedic surgery, 2026: 28 of 30 runs. OpenAI’s GPT-5.2 drafted month-long call schedules for simulated residents under 14 rules, in about 4 to 8 minutes a run, and 28 of 30 runs (93%) ended compliant (J Med Educ Curric Dev). The downsides:
  • It only got there because a separate rule-checker rejected the drafts that broke rules.
  • The 2 failures were backup-call blocks that had worked twice on the same inputs, then failed the third time.
  • It was one month of call for simulated residents, not a year of real ones.
  • There was nothing for residents to use, or for the next chief to inherit.

That is the line between a draft and a schedule you can publish: every rule checked, for every resident and every week, by something that checks the same way every time.

Everywhere a real residency schedule is hard: where every rule has to hold at once, for every resident, all year.

• It fails silently. Language models hallucinate, and a wrong schedule looks as tidy as a right one; a 2025 paper argues that training and scoring reward a confident guess over admitting uncertainty (Kalai et al.). In scheduling, a silent mistake means a service left understaffed or a resident who doesn’t graduate on time.
• Its context doesn’t pass to the next chief. Whatever you’ve built up in a conversation lives in your account, so the next chief class starts over, and nothing guarantees every rule is still being applied.
• Hard constraints across a year. Your program’s rules and ACGME requirements all have to hold together, for every resident, all year (see the 2026 duty-hour rules). Fixing one rule can quietly break another.
• Counting and coverage. ChatGPT and Claude write word by word and keep no running tally unless they run code, yet staffing each service every week is counting.
• Consistency when one thing changes. Ask it to move one vacation and it may rewrite the grid, changing assignments you didn’t mention, so every change means rechecking everything.
• No proof of compliance. “All rules are met” is generated text, not a check. In a 2024 Google DeepMind benchmark, the strongest models solved 41% to 49% of calendar-scheduling problems, and asking them to fix their own trip plans made results worse (Natural Plan). The orthopedic study got compliant schedules from GPT-5.2, but only with a separate checker rejecting drafts that broke rules. Chiefly keeps the proof: dashboards for program directors track each resident’s progress year over year, something ChatGPT or Claude can’t integrate the way a platform of record can.
• How much it can keep in view. A year of scheduling data is a long input, and models use what sits in the middle of a long input worse than what’s at the start or end (TACL, 2024), so a request buried mid-prompt can be missed.
• Repeatability. In the orthopedic study, two blocks produced compliant schedules twice, then failed a third time on the same inputs. And the rules live in a chat, not somewhere the next chief class inherits.

Some do, and they’re split on whether it saves time. In r/Residency threads such as “Anyone use AI to create resident schedules?”, chiefs land in two camps:

• It worked. Chiefs who report success gave it detailed parameters, had it write code, or kept it to Excel formulas and smaller tasks. The downsides:
  • Detailed parameters take time to write, and they stay in one chief’s chats instead of passing to the next class. One pediatrics team spent about 40 hours building scheduling macros with ChatGPT to cut a scheduling session from about 4 hours to about 2 (Cureus, 2026).
  • Code only helps if someone can read it, and the next chief inherits it.
  • Formulas and smaller tasks check pieces of the schedule; they don’t build it.
  • Either way, the chief still checks every output by hand.
• It didn’t. Others say it breaks down on a full program’s schedule, and that fixing its output takes longer than building by hand.

Both camps describe the same tradeoff: AI saves time when checking its output is quick, and loses time when the checking is the hard part. On a full residency schedule, the checking is the hard part. Between the setup, the double-checking and the silent failures, chiefs usually come away feeling it would be faster, and give them more control, to just build it themselves.

On small tasks around the schedule, where you can see at a glance whether the output is right:

• Spreadsheet formulas. A formula that flags weeks below a staffing minimum; test each on a few rows first.
• Code that checks a schedule you’ve built. A short script that counts each resident’s calls and flags anything over your limits. It reads the schedule without changing it, and you can check its counts against a few rows by hand.
• Emails to residents. Request deadlines, a call for vacation preferences, or a note explaining a change.

Useful, but none of it is the schedule, and every output still needs checking.

No, not in a personal account, and not anything that identifies a resident. Some institutions approve specific AI tools for work data, so check with your GME office first. This is general guidance, not legal advice.

• Know what’s sensitive. Names plus leave dates can reveal a pregnancy, a medical leave or an accommodation, and requests can carry personal reasons. Use codes such as R01 instead of names, keep the key in your own file, and never paste anything about patients.
• Follow your institution’s AI policy. Your hospital or university may approve specific AI tools or accounts for work data and prohibit others.
• Know your account’s settings. In consumer ChatGPT, the “Improve the model for everyone” setting decides whether chats can train OpenAI’s models; even with it off, rating a response can make that chat usable for training. OpenAI says it doesn’t train on Business, Enterprise, Edu or ChatGPT for Healthcare workspaces by default (OpenAI). Consumer Claude users choose whether chats train Anthropic’s models; if they allow it, chats are kept up to five years (Anthropic, 2025).

Scheduling software goes through your institution’s IT review instead. Chiefly, for example, holds workforce scheduling data only, never patient data, keeps it in the United States and encrypted, and has completed security reviews for more than a dozen health systems and academic medical centers (how Chiefly protects your data).

Treat it as a drafting tool and assume every output has a mistake until you’ve checked it. That takes eight steps:

1. Confirm your institution allows the tool; use an approved account if there is one.
2. Replace names with codes, and leave out the reasons for leave.
3. Keep every rule as a numbered list in your own file, and paste it in fresh.
4. Ask for code or formulas instead of a finished grid, so the logic can be read and re-run.
5. Start with one month or one rotation, not the whole year.
6. Check every rule yourself, for every resident and every week, plus each request.
7. After any change, recheck the whole schedule.
8. Save the final schedule and rule list outside the chat, for the next chief class.