Deep Cultivation Program
Helping Hospitals Go Smart — AcceleratingSmart HealthcareAdoption
We help medical institutions plan, build, and deploy AI solutions under the Deep Cultivation Program, creating real-world smart healthcare environments.
- 30+ Years in Medical Imaging
- 3,500+ Healthcare Institutions
- AI × Imaging Integration Platform
Is Your Hospital Facing These Challenges?
We deeply understand the real difficulties hospitals face when promoting the Deep Plan, and provide targeted solutions.
- Unclear Planning Direction：Uncertainty on how to start the Deep Plan, lacking a clear execution framework and priorities.
- Lack of Clinical Integration for AI：AI deployment requires cross-departmental collaboration, lacking partners with clinical process knowledge.
- High System Integration Complexity：Connecting with existing PACS / HIS / RIS systems is difficult due to inconsistent standards, consuming significant resources.
- Difficulty Going Live After PoC：Proof of concept succeeds, but a huge gap exists between PoC and full clinical deployment.
- High Multi-Vendor Coordination Costs：Multiple vendor interfaces, unclear integration responsibilities, leading to project delays and resource waste.
- Aligning with Government Policy Initiatives：Through data standard frameworks and protocols (FHIR), fully compliant with national digital healthcare technology development directions and applications.
Integrated Support from EBM
From planning to system deployment and AI adoption, we provide a comprehensive one-stop service to reduce the hospital's integration burden.
Planning & Consulting
- Deep Plan direction and scope definition
- Application scenario design (Imaging / AI / Mobile)
- System architecture planning and technical assessment
System Build & Integration
- Full PACS / RIS / HIS integration
- DICOM and non-DICOM imaging integration
- Mobile and cloud architecture deployment
AI Deployment & Validation
- AI model integration (in-house / third-party)
- Multi-center PoC validation support
- Clinical workflow adoption and continuous optimization
Why Choose EBM?
With over 30 years of deep expertise in medical imaging, we have the full-spectrum combat capability from system building to AI deployment, making us your most reliable integration partner.
- 30+ Years in Medical Imaging：Founded in 1988, continuously investing in medical technology R&D.
- 3,500+ Healthcare Institutions：Spanning Taiwan, Japan, Southeast Asia, and the US markets.
- Complete PACS + AI Integration Platform：EBM AI Platform won the 19th National Innovation Award.
- Edge + Cloud AI Hybrid Architecture：Flexible deployment solutions meeting security requirements of all hospital tiers.
- Validated Real-World Clinical Cases：Products like UDE and EPS Pi are already deployed in clinical settings.
Smart Healthcare Applications
Combining EBM's full product line to create a truly clinically relevant smart healthcare environment.
- AI-Assisted Imaging Diagnosis：Integrating multi-modal AI for CT, X-ray, and ultrasound to assist radiologists and clinicians in rapid interpretation, improving diagnostic efficiency.
- Mobile Medicine & Remote Diagnosis：Through the UDE (Ubiquitous Diagnostic Environment) App, enable anytime, anywhere image access and remote consultation, supporting rural and remote healthcare.
- Cross-Institution Image Integration：Build a cross-institution, cross-system image sharing platform for seamless image transfer and inter-hospital consultations.
- Patient Image & Report Digitization：Through the FHR (Family Health Record) App, empower patients with their imaging data for digitized medical records and personalized health management.
Complete Workflow from Planning to Deployment
A clear and transparent five-stage methodology ensuring every milestone is trackable and verifiable.
- Requirements Interview：In-depth understanding of the hospital's current state, goals, and existing system environment to clarify key directions for the Deep Plan.
- Planning & Architecture Design：Draft a comprehensive plan book, technical architecture diagram, and schedule to ensure goals are executable and resources are manageable.
- System Build & Integration：Execute full PACS / HIS / RIS integration, integrate DICOM and non-DICOM imaging, and establish a stable foundational architecture.
- AI Model Deployment & Testing：Integrate hospital-designated or third-party AI models, perform performance testing and clinical validation to ensure models meet clinical needs.
- Clinical Validation & Go-Live：Multi-center PoC validation, continuous clinical workflow optimization, assisting with smooth system go-live and ongoing maintenance support.
Real-World Deep Plan Deployments
Below are real cases where EBM Technologies helped healthcare institutions complete their Deep Plan, from requirement analysis to system deployment.
New Taipei (NT Medical Association) — Healthy Taiwan Deep Plan Case Study
Plan Name：New Taipei 888 Three-High & Cardio-Renal Disease Prevention Mobile Digital Healthcare AI Platform
Plan No.：A2-0022
Hospital Needs
Led by the New Taipei Medical Association, this initiative targets primary clinics' patients with the three highs (hypertension, hyperglycemia, hyperlipidemia) and cardio-renal disease, deploying a digital healthcare AI platform. Primary clinics lack digital management tools, and continuity of care for chronic three-high patients is insufficient — necessitating AI-assisted tools to improve diagnosis efficiency and research capacity. The plan also incorporates a large-scale cluster randomized clinical trial design to validate the platform's clinical impact.
Benefits
- Improved Diagnostic Efficiency：AI-assisted tools reduce the workload on primary care physicians, shortening fundus and ECG interpretation time
- Better Three-High Chronic Care：Digital platform offers continuous tracking, effectively improving medication adherence and health metric control
- Clinical Trial Capacity：Builds primary care teams' real-world data collection and clinical trial capability, producing high-quality research
- Large-Scale Validation：Through cluster randomized trial design, scientifically validates the actual clinical benefits of the digital AI platform
- Policy Diffusion Potential：Outcomes can serve as a national reference model for digital transformation of primary clinics, scalable to other regions
Pain Points & Solutions
| # | Pain Point | Solution |
|---|
| 1 | Primary clinics lack digital tools, making three-high patient tracking difficult | Deploy the 888 Mobile Digital Healthcare Management Platform for digitized chronic care |
| 2 | Early fundus disease detection relies on manual reading — low efficiency | AI fundus camera auto-analyzes retinal images, assisting physician interpretation |
| 3 | ECG interpretation depends on manual review; critical cases not flagged in time | ECG AI analysis system auto-detects abnormalities and alerts |
| 4 | Insufficient integration of AI systems into existing clinic workflows | Design pilot-clinic trial mechanism, phased rollout with training programs |
| 5 | Multi-system integration is complex; data exchange standards are inconsistent | Define exchange mechanisms and standards across systems, conduct compatibility testing |
| 6 | Insufficient incentives for healthy patient behavior; low medication adherence | Virtual AI Health Assistant (Hwa-Han) + digitized questionnaires / consent forms |
Tainan (Tainan AI Colorectal Screening) — Healthy Taiwan Deep Plan Case Study
Plan Name：Tainan AI Colorectal Screening — Colorectal Cancer Expanded Screening & Treatment × AI Referral Smart Platform Enhancement Plan
Plan No.：A2 (Cross-Hospital Alliance)
Hospital Needs
Led by the Tainan Medical Association, in collaboration with the Tainan City Government Health Bureau, NCKU Hospital, Chi Mei Hospital, An Nan Hospital, Tainan Municipal Hospital, Sinlau Hospital, MOHW Tainan Hospital, and Kuo General Hospital — seven major medical institutions jointly promote colorectal cancer screening enhancement and smart referral governance optimization. Tainan's colorectal cancer screening rate is only 37% (below the national average of 42%), with insufficient colonoscopy completion rate for positive cases, plus broken information flow between primary clinics and hospitals — making an integrated digital referral and screening governance platform essential.
Benefits
- Higher Screening Rate：Through proactive recall and behavioral incentive design, expected to raise Tainan's colorectal screening rate to 50%+
- Shorter Diagnosis Time：Significantly shortens wait time from FIT-positive to colonoscopy completion, reducing case follow-up dropouts
- Digitized Referral Process：Primary clinics can track case progress in real time after referral, eliminating manual communication and information loss
- Cross-Hospital Data Integration：Seven hospitals and primary clinics share screening and referral data through unified platform, supporting health bureau policy evaluation
- Rural Coverage：Mobile medicine demonstrations and community participation mechanisms improve screening accessibility for rural and underserved populations
- Scalable Model：Modular platform design — can be extended to breast cancer, oral cancer, and other cancer screening domains in the future
Pain Points & Solutions
| # | Pain Point | Solution |
|---|
| 1 | Low screening participation (Tainan 37%); public lacks incentives | Build competition scoring, behavioral incentive design, and LINE Bot proactive recall |
| 2 | Positive case colonoscopy follow-up dropouts; insufficient completion rate | "Tainan Colorectal Screening · Referral Hub" platform tracks case progress and sends reminders |
| 3 | Fragmented referral information; primary clinics cannot grasp follow-up in real time | Digital referral platform connects primary clinics to medical centers with real-time feedback |
| 4 | Incomplete medical record access and authorization mechanisms; cross-hospital retrieval is difficult | Referral authorization server + digital medical record certification and digital fingerprint technology |
| 5 | Lack of AI-assisted decisions; physicians struggle to triage complex cases quickly | Deploy Multi-Agent AI triage modules and GenAI medical record summarization |
| 6 | Scattered data systems; health bureau cannot evaluate effectiveness | Connect Tainan Care Cloud, NHI Cloud Records, and HPA Colorectal Screening Database |
Shin Kong Hospital — Healthy Taiwan Deep Plan Case Study
Hospital Needs
As the number of AI models continues to increase, Shin Kong Hospital's radiology department faces the dilemma of fragmented multi-vendor model management. Each vendor's AI workflow and interface is independent, forcing physicians to switch between multiple systems to view all interpretation results — severely impacting diagnostic efficiency. The RIS system cannot display AI text interpretation results, and critical cases lack automated prioritization. Moreover, as a central hospital, Shin Kong needs to support cross-hospital AI imaging interpretation services for surrounding satellite institutions.
Benefits
- Unified AI Management：All radiology AI models centralized on a single platform, dramatically reducing management complexity and hardware footprint
- Improved Reporting Efficiency：Physicians can directly reference AI interpretation results within RIS without switching systems, significantly shortening report writing time
- Critical Case Priority Handling：AI automatically prioritizes critical images, ensuring high-risk cases are interpreted and reported first
- Lung Cancer Screening Compliance：Case managers generate reports compliant with HPA's Lung-RADS format directly through the system, eliminating manual entry burden
- Cross-Hospital AI Service：Satellite hospitals can access Shin Kong's AI platform interpretation support without building their own AI infrastructure, improving regional medical quality
Pain Points & Solutions
| # | Pain Point | Solution |
|---|
| 1 | Multi-vendor AI workflows cannot be unified; each runs its own process | EBM AI Platform unifies image dispatch and pending compute list management |
| 2 | Each vendor has its own Viewer; physicians must switch between them | All AI results unified in EBM PACS Viewer |
| 3 | AI machines occupy department space and are difficult to manage | One-stop AI workstation integrates all vendor models |
| 4 | RIS cannot display AI text results; cannot accelerate report writing | RIS AI upgrade: display list, critical sorting, result review and editing |
| 5 | Case managers must manually enter lung cancer screening reports into HPA system | Build Lung-RADS lung cancer screening report interface and customized query website |
| 6 | Satellite hospitals lack AI interpretation capability; remote area service insufficient | Cross-hospital AI Gateway transmits images via UDE App to Shin Kong AI platform for compute and return |
Healthy Taiwan Deep Plan System Integrator — The Role of EBM Technologies
EBM Technologies (商之器) is a Healthy Taiwan Deep Plan deployment partner focused on medical imaging and AI integration. Founded in 1988, with over 30 years in medical imaging and deployments in more than 3,500 hospitals and clinics, and with the EBM AI Platform honored by Taiwan's 19th National Innovation Award, EBM is one of the few vendors able to package the Deep Plan into a single turnkey solution spanning PACS, RIS, HIS, FHIR healthcare data exchange, and AI medical imaging — acting as a single integration point of contact from planning and system integration to AI model deployment and multi-center PoC validation.
Deep Plan case studies and a full FAQ knowledge base are available at the Deep Plan Knowledge Center.
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