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An LLM can understand a scheduling request and explain a proposed appointment plan without reliably calculating every date, interval, duration, or conflict. For medical scheduling, use the model to interpret and draft—not as the final calculator or authority. Verify the result with explicit clinic rules, deterministic date-and-time calculations, and the authoritative scheduling system before a booking is confirmed.
Why fluent reasoning can still produce an invalid schedule
A valid schedule depends on more than a plausible explanation. The system must identify the right facts, apply the right rules, and calculate correctly. A mistake at any step can make a proposed appointment or roster unusable even if the surrounding prose sounds confident.
In the NeurIPS 2024 MedCalc-Bench paper, Khandekar and coauthors describe errors such as extracting the wrong entities, choosing an incorrect equation or rule, and performing arithmetic incorrectly. Their benchmark included more than 1,000 manually reviewed instances across 55 medical calculation tasks. It evaluates medical calculations, not appointment scheduling, but it illustrates why wording quality is not proof that quantitative work is right.
Clinical calculations are not merely verbal explanations: they require quantitative equations or rule-based reasoning. The MedCalc-Bench authors concluded that the evaluated LLMs were not effective enough for clinical settings. Separately, a 2024 Nature Medicine evaluation reported limitations in clinical decision tasks, including sensitivity to information order and difficulty interpreting numbers and arithmetic. That finding applies to the models and cases studied then; it is not a universal ranking of current systems.
#1 Best Overall
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Dates and constraints are easy to misread or omit
Scheduling requests often mix natural language with exact values: “two weeks after the scan,” “every third Tuesday,” or “leave 30 minutes between visits.” The system needs to know which date anchors the interval, what “two weeks” means in context, which time zone applies, whether the rule recurs, and whether other constraints take precedence. If an input is ambiguous or missing, generating a precise-looking answer can conceal the uncertainty rather than resolve it.
A 2024 PLOS Digital Health study examined cognitive-exam date and score extraction from 135,307 clinical notes spanning January 12, 2010, through May 24, 2023. It reports that ChatGPT extraction errors included wrong dates. This is evidence about extracting exam details from notes—not a measured error rate for appointment booking—so it supports caution about date extraction without establishing how often a scheduling system will fail.
Rank #2
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What scheduling studies establish—and what they do not
A 2026 SAGE study simulated orthopedic resident call scheduling with GPT-5.2 paired with a deterministic Python rule-checker. It directly studies a scheduling task and a hybrid design, but the task was resident call schedules, not outpatient appointment allocation. It therefore demonstrates a studied approach in one setting, not proof that every clinic booking workflow is safe when paired with a checker.
Evaluation quality matters too. HealthBench, introduced by OpenAI on May 12, 2025, identifies shortcomings in evaluations that do not reflect realistic health scenarios or lack rigorous validation against expert medical opinion. A strong result on a narrow test is not, by itself, evidence that a system handles a clinic’s actual rules, data, and exceptions.
Rank #3
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A September 14, 2026 NUMBERS preprint reports that, in controlled experiments involving 1,300 questions across 16 model configurations, providing explicit inputs and a calculation request raised patient-level near-target answers from 29.7% to 78.8% across 2,340 pairs. In a separate direct-action experiment, supplying a complete rule raised action accuracy from 85.63% to 99.41%. These are the authors’ results for their tested prompts and tasks, not guarantees for live scheduling or evidence that a model can replace a clinic’s booking system.
How to divide the work between an LLM and scheduling software
A safer design separates language interpretation from calculation and booking. The model can turn a request into candidate fields or explain a draft. A deterministic component should apply the clinic’s explicit rules, calculate dates and durations, and check for conflicts. An authorized person or scheduling system should resolve uncertainty and commit the appointment.
Rank #4
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- Capture the request as structured inputs. Record the intended date or anchor event, duration, recurrence, time zone, patient or resource constraints, and any required interval. Keep the original wording available so a reviewer can compare it with the extracted fields.
- Make the applicable rules explicit. Specify allowable hours, required buffers, recurrence behavior, resource qualifications, priority rules, and exception handling. Do not ask the model to infer a local clinic rule that has not been supplied.
- Calculate with deterministic logic. Use date-and-time representations and software that applies the stated rules consistently. Keep arithmetic and conflict checks reproducible rather than relying on a prose explanation.
- Escalate ambiguity instead of guessing. If a date anchor, time zone, duration, or rule is missing or contradictory, request clarification or route the case to an authorized scheduler.
- Check against the authoritative clinic system. Confirm availability and constraints in the system that actually controls the schedule before committing a booking; a generated draft alone does not establish that a slot is available.
- Log changes and overrides. Preserve the proposed values, rule-check outcome, corrections, and person or system that approved the final schedule so errors can be investigated and changes traced.
What to check when choosing a scheduling workflow
There is no head-to-head evidence here ranking calendar APIs, operations-research solvers, or scheduling products. Assess a workflow by how it handles the details that determine whether a schedule is valid:
Quick Recap
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- Date and time representation: Are time zones, durations, and recurring events explicit rather than inferred?
- Rule coverage: Can clinic constraints be written down and applied consistently?
- Reproducibility: Are calculations and conflict checks deterministic and auditable?
- Uncertainty handling: Does the workflow stop for missing or ambiguous details instead of inventing values?
- System-of-record validation: Is the proposed schedule checked against the authoritative clinic system?
- Audit trail: Are errors, edits, and human overrides recorded?
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