Architecting the ShiftOS Scheduling Engine

ShiftOS is a workforce operating system for healthcare teams.

Scheduling is its core engine - the system everything else depends on.


My work focused on designing this scheduling engine: the workflows, interactions, intelligence layer, and visual system that allow hospitals and pharmacy networks to staff safely, efficiently, and predictably.

ShiftOS is a workforce operating system for healthcare teams.

Scheduling is its core engine - the system everything else depends on.


My work focused on designing this scheduling engine: the workflows, interactions, intelligence layer, and visual system that allow hospitals and pharmacy networks to staff safely, efficiently, and predictably.

92% faster onboarding

from 3–5 hours to under 15 minutes.

Self-onboarding at 68%

onboard clients without customer support intervention.

7x more clients handled

during onboarding cycles.

76% template reuse rate

for saved templates, cutting repeat setups by hours.

94% fewer data-entry errors

with zero data loss across 400+ onboarded firms.

What I Built
What I Built

ShiftOS Scheduling is the module that sits at the center of healthcare operations.

My work focused on building the scheduling engine, the surface where every staffing decision begins.


The goal wasn’t to redesign a calendar.

It was to design a system that blends:


  • manual control for decisions that require human judgment

  • AI-driven reasoning for decisions that require speed, outreach, and constraint logic

  • consistent actions across the entire interface

  • predictable workflows across departments and facilities



This is the module future ShiftOS features will rely on.

Why This Needed to Exist

Healthcare staffing breaks down because reality changes faster than tools can respond. Decades of legacy systems, designed around billing and compliance rather than clinical workflows, have grown into a $66B administrative burden. They don't understand:

  • last-minute call-offs

  • overtime and fatigue rules

  • licensing and certification requirements

  • PRN workflows

  • multi-facility assignments

  • EHR-specific training

  • cascading coverage gaps

It's all the same

Onboarding was supposed to be simple. Until it started costing the company money.
Legacy tools only display the schedule.
They don’t understand the rules, conflicts, or ripple effects underneath.
Designing the System Model
Designing the System Model
The goal wasn’t to build new buttons or new screens, it was to design a behavioral system and what the scheduler is likely trying to accomplish.

Everything in the scheduling engine is built around this principle:

Surface the most meaningful next action, based on real-world operational context - not UI menus.

To achieve this, I designed the engine around three architectural layers:
When Data Starts to Organize Itself
When Data Starts to Organize Itself
The entity view was designed for clarity at scale

Every client, entity, and sub-entity neatly structured for instant understanding.

It’s not just a list - it’s a map of relationships that grows as the business does.
The entity view was designed for clarity at scale

Every client, entity, and sub-entity neatly structured for instant understanding.
It’s not just a list - it’s a map of relationships that grows as the business does.
The entity view was designed for clarity at scale

Every client, entity, and sub-entity neatly structured for instant understanding.
It’s not just a list - it’s a map of relationships that grows as the business does.
The left sidebar isn’t static - it’s alive.

Each document uploaded reshapes the hierarchy in real time: new entities, updated savings, and refined structures - all of which can happen at any time, without breaking flow.
The left sidebar isn’t static - it’s alive.

Each document uploaded reshapes the hierarchy in real time: new entities, updated savings, and refined structures - all of which can happen at any time, without breaking flow.
The left sidebar isn’t static - it’s alive.

Each document uploaded reshapes the hierarchy in real time: new entities, updated savings, and refined structures - all of which can happen at any time, without breaking flow.
Designing for a Proactive AI

AI shouldn’t wait for instructions - it should anticipate them. I wanted the system to think before the user did.


In traditional tax workflows, professionals hunt through data, compare strategies, and calculate savings manually. Here, AI does that groundwork quietly in the background, pre-selecting the most relevant tax strategies based on uploaded documents.

AI shouldn’t wait for instructions - it should anticipate them. I wanted the system to think before the user did.


In traditional tax workflows, professionals hunt through data, compare strategies, and calculate savings manually. Here, AI does that groundwork quietly in the background, pre-selecting the most relevant tax strategies based on uploaded documents.
Instead of a long list of options, the interface opens with projected savings and pre-selected strategies, turning choice into confirmation.
Instead of a long list of options, the interface opens with projected savings and pre-selected strategies, turning choice into confirmation.
Built for Speed & Efficiency
Built for Speed & Efficiency
Direct Integration

Direct IRS and QuickBooks integrations pull existing client data instantly - no re-entry, no setup from scratch.

Save and Reuse

Any onboarding can be saved as a template and loaded again, keeping structure and settings consistent across clients.

The Result
92% faster onboarding

from 3–5 hours to under 15 minutes.

Self-onboarding at 68%

firms now onboard clients without customer support intervention.

7x more clients handled per tax professional

during onboarding cycles.

76% template reuse rate (as of November 2025)

for saved templates, cutting repeat setups by hours.

94% fewer data-entry errors

with zero data loss across 400+ onboarded firms.

92% faster onboarding

from 3–5 hours to under 15 minutes.

Self-onboarding at 68%

onboard clients without customer support intervention.

7x more clients handled

during onboarding cycles.

76% template reuse rate

for saved templates, cutting repeat setups by hours.

94% fewer data-entry errors

with zero data loss across 400+ onboarded firms.

92% faster onboarding

from 3–5 hours to under 15 minutes.

Self-onboarding at 68%

onboard clients without customer support intervention.

7x more clients handled

during onboarding cycles.

76% template reuse rate

for saved templates, cutting repeat setups by hours.

94% fewer data-entry errors

with zero data loss across 400+ onboarded firms.

AI became the quiet partner. The work didn’t just save time; it redefined how time is used.
AI became the quiet partner. The work didn’t just save time; it redefined how time is used.

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