DoctorAnywhere
I designed an explainable recommendation layer that brings specialty, location, availability, language and coverage into the agent’s existing booking flow.
A concise project brief that makes my individual contribution, collaborators and definition of success clear before the detailed process.
Provider choice depended on individual memory and signals scattered across tools. Under time pressure, inconsistent decisions caused slower confirmations and avoidable re-referrals.
Decision support should increase confidence, not take accountability away from the person doing the work.
I connected behavioural, operational and qualitative evidence before choosing a solution. Each signal created a concrete design implication.
Experienced agents carried provider knowledge in memory, making speed and consistency dependent on who handled the case.
The interface needed to externalise useful judgement without pretending every case had one automatic answer.
Specialty, location, availability, language and coverage were reviewed across separate information sources.
The highest-value product move was to unify the comparison at the moment of choice.
Agents were already operating inside a case flow under time pressure; switching to a standalone recommendation tool would add friction.
Decision support needed to sit inside the existing case and lead directly into booking.
The blueprint keeps the customer journey, visible product behaviour and operational responsibilities connected across the same sequence.
The patient’s referral, preferences and constraints define the request.
The request is clarified before provider options are presented.
The agent confirms specialty, urgency, coverage and preference context.
Only relevant providers should remain in consideration.
Unsuitable options are filtered from the comparison.
Rules apply specialty, coverage, location, language and availability signals.
Trade-offs become easier to discuss and understand.
A ranked view exposes the most relevant options and why they fit.
Signal-level rationale supports, rather than replaces, agent judgement.
A suitable provider is selected with clear reasoning.
The agent can review, override and explain the recommendation.
The final choice and exception reasoning remain accountable.
The selected option progresses into appointment coordination.
The recommendation connects directly to booking actions.
The existing case and ticket workflow continues without a separate hand-off.
Scroll horizontally on smaller screens to follow the complete service.
The artefacts were useful because they helped the team make choices. This is the evidence-to-decision trail behind the final experience.
Patient preferences, clinical context and operational exceptions could not be reduced to one deterministic answer.
Rank and explain suitable options while keeping the final decision with the agent.
A score alone would be difficult to trust, explain or override responsibly.
Expose the matching signals behind each recommendation at the comparison point.
Agents already had a complex case workflow and could not absorb another destination or tool.
Embed recommendations inside the case flow with a direct continuation into booking.
I treated the operating experience and the interface as one system. Each lens solved a different part of the same problem.
Shaping the ecosystem, hand-offs and operating model.
Turning service decisions into clear, usable product behaviour.
Focused the problem on the judgement agents make—not merely the provider data they search. This clarified which signals mattered and where guidance would create value.
Brought specialty fit, location, availability, language and coverage into a single ranked view so agents could compare options without switching tools.
Made the rationale for every recommendation visible. Agents could assess the evidence, handle exceptions and remain accountable for the final choice.
Placed recommendations inside the case flow with a direct path into booking, turning decision support into acceleration rather than another step.
I used low-fidelity flows to test the order of questions and decision points before visual design. The high-fidelity experience then translated that logic into a branded, guided journey from patient intake to a confident specialist selection.
The first sketches modelled the concierge as a progressive conversation. Each response unlocked the next relevant question, keeping the interaction focused while showing how specialty, hospital and preferred dates would shape the recommendation.
The final direction combined conversational intake with focused task surfaces. Patients could upload a referral, choose up to two preferred time windows and compare recommended specialists without leaving the concierge journey.
These constraints shaped the solution, the order of work and the compromises I made with the wider team.
A recommendation can only be as dependable as availability, coverage and provider information.
The experience exposed the signals used and preserved agent review rather than presenting certainty the data could not guarantee.
A black-box ranking could reduce trust and make exceptions harder to handle.
Each recommendation showed why it matched, allowing the agent to compare, explain and override it.
Extra screens or tools could cancel out the time saved by recommendation logic.
The feature reused the existing case context and connected the selected provider directly to the next booking action.
This case presents validated design direction and workflow evidence. I do not claim a post-launch business metric until production measurement is available.
Turned a memory-dependent decision into guided, evidence-based matching.
Made recommendations explainable rather than opaque.
Reduced the cognitive load of comparing provider options.
Extended the concierge platform without fragmenting the agent workflow.
I designed the recommendation as accountable decision support—combining useful ranking with transparent rationale and human control.
In a sensitive service, explainability is not supporting copy; it is a core interaction requirement that enables trust and exception handling.
I would validate and monitor time-to-match, override rate, re-referral rate, booking completion and the reasons agents reject a recommendation.