Problems Solved by MedExpertMatch¶
This document lists concrete problems that MedExpertMatch addresses in healthcare workflows.
1. Consultation Delays and Length of Stay¶
Problem: Patients wait days or weeks for specialist consultations. Delayed consults increase length of stay and can worsen outcomes.
How MedExpertMatch helps:
- Matches cases to specialists in minutes using hybrid GraphRAG (vector + graph + historical performance).
- Replaces ad-hoc "who do I know?" with a consistent, data-driven matching process.
- Reduces time-to-consultation and can shorten hospital length of stay.
Use case: Specialist Matching for Complex Inpatient Cases
2. Slow Second Opinions and Wrong Sub-Specialist¶
Problem: Second opinions take days to arrange. Cases are often sent to a generic specialty (e.g. "oncologist") instead of the right sub-specialist.
How MedExpertMatch helps:
- Cuts turnaround for second opinions from days to minutes.
- Matches by diagnosis, ICD-10/SNOMED codes, and complexity so the case goes to the right sub-specialist.
- Can prioritize telehealth-enabled doctors for remote second opinions.
Use case: Online Second Opinion / Telehealth
3. Urgent Consults Buried in FIFO Queues¶
Problem: Consult requests are processed first-come-first-served. Urgent cases wait behind non-urgent ones; high-risk patients face unnecessary delays.
How MedExpertMatch helps:
- Prioritizes the consultation queue by clinical urgency (CRITICAL / HIGH / MEDIUM / LOW).
- Uses case analysis (symptoms, urgency, risk) and priority scoring so the sickest patients are seen first.
- Gives coordinators a sorted queue and optional suggested specialists.
Use case: Prioritizing the Consultation Queue
4. Implicit, Anecdotal Expertise ("Who Is Good at What")¶
Problem: Organizations rely on informal knowledge about who handles what. Real expertise is hidden; routing and planning are suboptimal.
How MedExpertMatch helps:
- Makes expertise visible via network analytics on the graph (doctors, cases, conditions, facilities).
- Shows who actually handles complex cases in specific domains (e.g. by ICD-10 code).
- Supports data-driven routing, capability planning, and mentorship/learning programs.
Use case: Network Analytics
5. Lack of Structured Decision Support and Evidence¶
Problem: Specialists need structured case analysis, differential diagnosis, evidence-based recommendations, and easy access to colleagues for complex cases.
How MedExpertMatch helps:
- Provides an AI copilot: case summary, differential diagnosis, ICD-10 extraction, risk assessment.
- Retrieves evidence: clinical guidelines, PubMed, GRADE-style summaries.
- Generates recommendations: diagnostic workup, treatment options, monitoring, follow-up.
- Suggests colleagues and multidisciplinary experts via Semantic Graph Retrieval.
Use case: Human-in-the-Loop Decision Support
6. Facility–Case Mismatch in Regional Networks¶
Problem: Complex cases are sent to facilities and specialists without considering real capabilities, outcomes, or capacity. Mismatches and inefficiencies are common in hierarchical systems.
How MedExpertMatch helps:
- Routes cases by diagnosis, severity, and required resources (e.g. PCI, ECMO).
- Scores facilities using case complexity, historical outcomes, capacity, and geography (Semantic Graph Retrieval).
- Returns ranked facilities with suggested lead specialists and explanations.
- Makes referrals and transfers more transparent, consistent, and measurable.
Use case: Cross-Organization / Regional Routing
Summary Table¶
| Problem | Main capability | Outcome |
|---|---|---|
| Long waits for specialist consultation | Fast, data-driven specialist matching | Shorter time-to-consult, potentially shorter LOS |
| Slow or wrong second opinions | Diagnosis- and complexity-based matching | Faster second opinions, right sub-specialist |
| Urgent consults not prioritized | Urgency-based queue prioritization | Sickest patients seen first |
| Hidden expertise, anecdotal routing | Network analytics on graph data | Data-driven expertise and routing |
| Missing structured analysis and evidence | Case analysis + evidence retrieval + recommendations + expert suggestions | Better decision support and collaboration |
| Facility–case mismatch in regions | Facility routing with SGR scoring | Better outcomes and resource use |
Last updated: 2026-02-08