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Flagship case study · Constraint-based matching

Designing Human-Supervised Provider Assignment

Using rules-based matching and explainable recommendation logic while preserving authorized human review.

AutomationHealthcareWorkflowProduct Strategy

Every provider identity and scenario is fictional. Licensure and matching logic are simplified and are not used for clinical decisions.

Ola’s roleLed product discovery, business-rule definition and UAT
Product statusPublic-safe product concept based on generalized workflow experience
Product typeConstraint-based matching and human-supervised automation
Business problemManual assignment can create delay, inconsistency, and limited transparency. A rules-based product needed to explain recommendations, support review and override, and escalate safely when no eligible match existed.
Strategic decisionTreat qualification and simplified licensure as hard constraints.
Intended outcomeDesigned to support faster, more consistent assignment without removing authorized human judgment.

01 · Executive summary

The product challenge in one minute.

A fictionalized decision-support model that evaluates simplified qualification, availability, capacity, and timing constraints while keeping an authorized coordinator in control.

02 · Context and business problem

Why the problem mattered.

Time-sensitive review requests may need to be matched with an appropriately qualified and available provider under multiple operating constraints.

Business problem

What had to change

Manual assignment can create delay, inconsistency, and limited transparency. A rules-based product needed to explain recommendations, support review and override, and escalate safely when no eligible match existed.

03 · Users and stakeholders

The people making, supporting and governing the decision.

Primary usersAuthorized coordinatorsProvider reviewersOperations leadersAudit and compliance partners

Partnered with coordinators, domain experts, engineering, and operational leaders.

04 · Constraints and complexity

The happy path was not the product.

Timing, partial outcomes, missing data, conflicting state and system boundaries shaped the product strategy from the beginning.

CONSTRAINT 01

No eligible provider

CONSTRAINT 02

Two equally eligible providers

CONSTRAINT 03

Capacity changes after a recommendation

CONSTRAINT 04

A restricted case has one eligible provider

CONSTRAINT 05

A time-sensitive request enters an existing queue

CONSTRAINT 06

An authorized user selects a different eligible provider

05 · Product strategy

Reduce ambiguity before adding automation.

The strategy connected the operating problem to explicit states, rules, ownership, exceptions and a measurable outcome. Product definition focused on the user decision and the behavior required to make it reliable.

Ola’s product-leadership role

Led product discovery, business-rule definition and UAT

Led definition of matching variables, hard and soft constraints, override behavior, and audit needs.

06 · Prioritization decision

Choose the decision that unlocks operating value.

Treat qualification and simplified licensure as hard constraints.Primary strategic decision

The priority was selected because it addressed the core workflow constraint and created a foundation for safer automation, clearer ownership or more reliable downstream behavior.

See how the MIN–MAX Product Model supports strategic prioritisation and transparent trade-offs

07 · Workflow, rules and product model

The visible experience and the logic beneath it.

1Eligibility checks
2Specialty matching
3Simplified licensure logic
4Availability and capacity
5Recommendation explanation
6Authorized human override
7No-match escalation
8Decision trace

08 · Alternatives and trade-offs

Make product trade-offs explicit.

  1. 01

    Use capacity and timing as transparent ranking inputs.

  2. 02

    Explain why a recommendation is eligible before asking for acceptance.

  3. 03

    Allow authorized users to review or override a recommendation.

  4. 04

    Escalate to manual review when no eligible match exists.

09 · Cross-functional leadership

Align domain expertise with technical delivery.

  • Facilitated scenario design with operational coordinators
  • Aligned domain and operating constraints
  • Partnered with engineering on deterministic matching behavior
  • Defined review, override, and audit acceptance criteria
  • Designed measures for speed, balance, and user trust

10 · Delivery and rollout

Connect discovery to release readiness.

Led definition of matching variables, hard and soft constraints, override behavior, and audit needs. Delivery linked product rules to user stories, measurable acceptance criteria, UAT scenarios, operational readiness and post-launch monitoring.

Explore the Discovery-to-Optimisation Process for discovery, validation and release readiness

11 · Measurement framework

Define success before development.

The intended outcome is clearly distinguished from a verified result. A production measurement plan would pair the goal with adoption, task efficiency, quality, reliability and exception signals.

12 · Results

An intended outcome, not an invented metric.

Designed to support faster, more consistent assignment without removing authorized human judgment.

13 · Lessons and next decisions

Use evidence to decide what expands next.

Rules-based automation earns trust when users can understand the recommendation, see the constraints, and intervene when judgment is required.Product lesson
1

Study override patterns

2

Refine ranking inputs

3

Improve proactive capacity visibility

Related product artifacts

Sanitized examples, built for review.

See how product thinking becomes tangible through public-safe decision tools.

View requirements, roadmap, workflow and measurement examples in the Product Artifacts Library

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