OrthoPathway.

The problem

In orthopedic surgery scheduling, friction hits when a patient reschedules a piece of the pre-op care path. If there aren't enough days between events, it creates a real obstacle — a single changed step can quietly break a required test downstream.

Untrained or new scheduling staff allow gaps in care because they don't remember or know all the details, and the incumbent is manual scheduling inside the existing EHR, where dates slip through the cracks. Patients shouldn't be able to request dates that negatively impact procedures downstream, but today nothing reliably catches that.

The solution

OrthoPathway is an AI scheduling assistant that catches scheduling issues before they become problems. It assesses patient risk — especially cardiac risk affecting anesthesia clearance — recommends scheduling options against hospital policy, and surfaces its reasoning while preserving human override.

The assistant is baked into the existing scheduling interface as an additional pane, so staff work the way they already do. It issues structured APPROVE/DENY decisions with clinical reasoning: deny when a reschedule makes it impossible to complete required pre-op steps in time, approve when the timeline still allows all steps to be completed safely — with the ability to override and escalate when a patient can't meet the schedule.

How it works

The assistant takes health history, current doctor, and a suggested surgery date, and checks the proposed schedule against ACC/AHA perioperative guidelines, required buffer windows, and department availability, with local department policies chunked into a RAG layer so the model reasons from loaded policy rather than inventing rules. The demo contrasts multiple prompt styles and shows how using more than one LLM (Claude and Gemini Flash were both tested) helps validate a response.

Evaluation at this stage was manual: the same test cases — a simple schedule, a complex multi-step schedule, and a reschedule — were run through five prompt variations. Only the "Detailed Clinical" variation consistently produced the right call with sound reasoning; Vague and No Context failed, Overly Cautious denied safe schedules, and Overly Aggressive broke buffer rules. A key system fix constrained the model to only offer dates between today and the surgery date, so impossible options are never on the table. Future production anticipates a HIPAA-compliant or local model.

Who it's for

This is a B2B internal tool. The buyers are hospital administration, and the end users are scheduling staff — internal users who interface with patients during the scheduling process.

It's especially valuable for newer or untrained schedulers who don't yet carry all the policy in their heads, encoding rules they'd otherwise learn slowly on the job. It is not a public-facing product; the assistant supports staff decisions rather than replacing them.

Why it matters

Orthopedic surgery volume rises every year, so scheduling volume keeps growing, at a projected 4-6.5% rate. The headwind is that hospitals adopt technology slowly due to budgets, compliance, and training, but the broader spread of AI is lowering that resistance.

Launch is a pilot in one orthopedic department, expanding department by department roughly eight weeks after each is judged successful — loading a vetted policy set per department rather than retraining a model. Success is measured by fewer downstream scheduling errors, a declining override rate signaling growing trust, and shorter ramp time for new staff. Because every output is an overridable recommendation inside the hospital's HIPAA-covered environment, the audit trail is the core compliance control.

At a glance

Project

OrthoPathway

Built by

Paul Osborne

One-liner

OrthoPathway is an AI scheduling assistant for orthopedic surgery workflows, especially for patients whose pre-op needs are affected by cardiac risk and other complexity.