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Recalibrate availability ranking before patients abandon at booking

You’re the ai / ml engineer. Your team is in the room. Printed Oct 19, 2026.

Availability can look current in search while failing at the booking handoff.

Higher booking starts depend on useful availability, while ranking changes must not raise cancellations.

Who you’d be doing this for

“I spent twenty minutes narrowing this down, then every evening slot disappeared when I tried to book.”

Dorota Nistor · Therapy-seeking patient

She is using in-network search after work and needs an appointment that fits her schedule.

What is at stake

Booking starts have fallen to 18% when evening availability is selected. You have to weigh faster ranking improvement against cancellation risk from weaker match quality.

Why it isn’t already fixed

Every obvious fix costs something else. That’s the part you’d have to decide.

  • booking starts vs cancellation protection
  • fresh availability vs stable ranking signals
  • fast experiment learning vs reliable rollback

Why Headway

In-network search is the route patients use to turn insurance coverage into a first therapy appointment.

Written with these in mind

applied ML engineerranking engineerhealthcare ML practitioner

Not your kind of problem? 75 more at Headway, or browse every organization.

This is the setup. The work is inside.

Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.