Specialist capacity was constrained
MediCore operated 25+ diagnostic centers with radiologist shortages, inconsistent diagnostic quality, and long patient wait times.
2024·14 weeks·5 engineers
AI-Powered Diagnostic Platform
35% improvement in diagnostic accuracy, 50% reduction in patient wait times
Client:MediCore Health
35% improvement in diagnostic accuracy, 50% reduction in patient wait times
Overview
MediCore Health partnered with Boolean & Beyond to deliver aI-assisted diagnostic platform integrating medical imaging analysis, patient records, and clinical decision support for multi-specialty hospital chains. The engagement focused on improving how the business operated day to day while creating a platform that could scale with demand.
The Problem
MediCore operated 25+ diagnostic centers with radiologist shortages, inconsistent diagnostic quality, and long patient wait times. Patient records were fragmented across locations. MediCore Health needed a delivery partner who could translate that pressure into a product and systems implementation that improved speed, visibility, and reliability without adding more manual overhead.
Challenges
What made this hard
Patient data was fragmented
Patient records were fragmented across locations.
The business needed a more scalable foundation
MediCore Health needed an implementation path that solved the immediate bottlenecks while building a more scalable operating model for healthtech & diagnostics.
How we delivered
Our approach
Phase 01
Discovery & workflow mapping
We began by mapping the current workflow end to end, identifying the steps creating the most friction for MediCore Health, and prioritizing the journeys where better UX, cleaner data flow, or deeper automation would create the fastest business lift.
Phase 02
Architecture & experience design
From there, we translated the business goals into a delivery plan and architecture centered on AI-assisted diagnostic platform integrating medical imaging analysis, patient records, and clinical decision support for multi-specialty hospital chains, so the build could improve adoption, operator control, and room for scale at the same time.
Phase 03
Build, integrations & intelligence
The build was structured across the experience layer (React), core services and data (FHIR), the intelligence layer (PyTorch, MONAI, and NVIDIA Clara), and production integrations and ops (AWS). We built an AI platform that assists radiologists with image analysis, provides clinical decision support, streamlines patient flow, and unifies patient records across the network.
Phase 04
Launch hardening & optimization
Before and after launch, we tuned the workflows, operational guardrails, and instrumentation against live usage so the platform could reliably support 35% improvement in diagnostic accuracy, 50% reduction in patient wait times.
What we built
Solution highlights
01
Core product experience
AI-assisted diagnostic platform integrating medical imaging analysis, patient records, and clinical decision support for multi-specialty hospital chains.
02
Conversational automation
We built an AI platform that assists radiologists with image analysis, provides clinical decision support, streamlines patient flow, and unifies patient records across the network.
03
Production-ready rollout
The implementation was hardened for production so MediCore Health could scale the experience and operational workflow behind 35% improvement in diagnostic accuracy, 50% reduction in patient wait times.
04
Operational control and visibility
The delivery gave business and operations teams a clearer view of what was happening inside the workflow so they could tune the system as adoption grew.
The full story
Journey & Execution
AI-Powered Diagnostic Platform
MediCore Health needed a diagnostic platform that would improve turnaround and consistency without trying to remove clinicians from the loop. The product had to support specialist teams across a network of centers where time, context, and reporting quality directly affected patient care.
That made the work both technical and operational. Better models alone would not help unless they fit how reports were reviewed, finalized, and shared.
Business Context
Radiologist capacity was constrained, reporting quality varied between centers, and fragmented patient records slowed diagnosis and follow-up. The platform needed to reduce this friction without adding noise to already busy clinical workflows.
The team also needed a shared view of patient and diagnostic context so decisions were not made in isolated local systems.
MediCore operated 25+ diagnostic centers with radiologist shortages, inconsistent diagnostic quality, and long patient wait times. Patient records were fragmented across locations.
How We Approached the Build
Discovery and workflow mapping
We mapped how imaging moved through the network, where reviews stalled, how clinicians accessed context, and where assistive intelligence could shorten the path to a confident final report.
Architecture and product design
The solution combined image analysis support, a connected clinical data layer, and workflow surfaces for specialists and operations teams so diagnostic decisions could happen with more context and less delay.
Delivery and integration
We treated the platform as decision support infrastructure, not an isolated AI feature. It had to improve the rhythm of clinical work while staying accountable to the need for expert review.
What We Implemented
The implementation focused on the operational touchpoints with the most impact on quality and speed:
- Image analysis support to help clinicians triage and review studies more efficiently.
- Connected patient record access so the diagnostic team could work with the right history and context.
- Clinical decision support surfaces that reduced back-and-forth during report preparation.
- Operational visibility across centers to identify where throughput and turnaround were being held up.
AI and automation layer
The AI layer was built to augment clinical review, not replace it. Low-confidence or complex cases still required expert judgment, while assistive intelligence was used to speed up the repetitive parts of the workflow and highlight what deserved closer attention.
Stack and engineering decisions
The implementation used PyTorch, MONAI, FHIR, React, AWS, and NVIDIA Clara across the experience, service, data, intelligence, and integration layers.
- Experience & channels: React
- Core services & data: FHIR
- AI/ML & automation: PyTorch, MONAI, and NVIDIA Clara
- Cloud, integrations & ops: AWS
Rollout and measurable outcomes
Rollout required careful calibration because trust matters more than novelty in clinical software. The platform had to prove that it improved flow and consistency without undermining review discipline.
- Diagnostic Accuracy: +35%
- Wait Times: -50%
- Report Turnaround: -40%
- Centers Connected: 25+
Why This Delivered Business Value
The strongest outcome was not just faster reporting. It was a more connected diagnostic workflow where expertise, patient context, and operational visibility could work together.
Taken together, the engagement delivered 35% improvement in diagnostic accuracy, 50% reduction in patient wait times by aligning product experience, workflow design, and implementation detail around the same business objective.
Under the hood
Technical Deep Dive
We treated the implementation as a product system rather than a single feature build, with React on the delivery surface and FHIR behind it. We built an AI platform that assists radiologists with image analysis, provides clinical decision support, streamlines patient flow, and unifies patient records across the network. Intelligence was embedded through PyTorch, MONAI, and NVIDIA Clara, giving MediCore Health automation and decision support inside the workflow instead of forcing teams into separate tools. Deployment, integrations, and production operations were supported by AWS, which helped the team roll out reliably and keep improving after launch.
Intelligence layer
AI Capabilities
Computer vision and image analysis
Visual intelligence was used to detect patterns, hazards, or clinical signals faster than manual review alone and to surface the findings inside the operational workflow.
Tech Stack
Technologies used
Experience & channels
React
Core services & data
FHIR
AI/ML & automation
PyTorchMONAINVIDIA Clara
Cloud, integrations & ops
AWS
Impact at a glance
Results that speak for themselves
Every metric shown is measured in production — not projected, not estimated. These are the real numbers from the deployed system.
Measurable outcomes
94%
Diagnostic Accuracy
AI-assisted diagnostic accuracy across supported conditions, validated against pathologist review over 10,000 cases
-70%
Report Time
Average time from sample collection to digital report delivery reduced from 48 hours to under 15 hours
+45%
Early Detection
Improvement in early-stage detection for critical conditions, enabled by AI pattern recognition on imaging data
3x
Patient Volume
Tripled diagnostic throughput without proportional staff increase, through workflow automation and smart scheduling
“
"The AI-assisted diagnostics don't replace our doctors — they make them faster and more accurate. Early detection rates improved dramatically, and our patients get results in minutes instead of days."
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