A practical healthcare AI platform for institutions that need higher service capacity, safer documentation, stronger patient continuity, and auditable clinical workflows.
Hong Kong Enterprise IT Leader Since 1985
Founded in 1985, Microware Group is Hong Kong's trusted IT infrastructure partner, supporting enterprise and public-sector transformation with secure, scalable delivery across the region.
Microware has multiple AI product lines. This deck focuses on Micromeet AI for healthcare, while Microcraft and Compliance AI are supporting capabilities.
Clinical AI applications for institutions: MCU CoPilot, Voice to Notes, Care Loop, Claim Solver, and AI CRMS operations.
Shared enterprise AI capabilities for document automation, internal knowledge workflows, and team productivity.
Governance and compliance layer for policy control, auditability, and regulated process requirements.
A decision-maker view: start with visible service bottlenecks, then connect operations, governance, care continuity, and deployment readiness.
High-pressure workflows where institutions feel cost, quality, access, and patient-continuity pressure (Slides 6-9).
Verified medical sources and institution knowledge that can be reused safely across teams (Slide 10).
The operating command layer for patient records, work queues, service conversion, and accountability (Slides 11-14).
Connects records, referrals, and care interactions across hospitals, primary care, doctors, and patients (Slides 15-16).
Cloud, hybrid, and on-premise controls required for regulated clinical environments (Slide 17).
From weeks to minutes: doctor-governed report generation that raises MCU report quality, team efficiency, service capacity, and patient satisfaction.
| Turnaround | From weeks to minutes against baseline |
| Report quality | Standardized structure, doctor review, exception checks |
| Capacity ceiling | More reports and corporate clients without linear HC growth |
| Post-report value | Risk stratification, company reports, Care Loop handoff |
Efficient, high-quality medical records: helps doctors produce standardized electronic records faster while institutions keep clinical governance.
| Doctor efficiency | Less time spent on writing |
| Institution efficiency | Faster record completion and fewer handoff gaps |
| Record quality | Standardized structure and doctor-reviewed content |
| Patient value | Clearer summaries, follow-up, referral context |
Turns every report or consultation into a governed patient-management pathway, so institutions do not lose patients after the encounter.
| Patient continuity | Fewer lost follow-ups after reports and visits |
| Risk management | High-risk patients are routed to next actions |
| Institution receiver | Queues for booking, doctor review, and patient-management teams |
| Long-term value | Recheck, consultation, referral, chronic-care management |
Reduces claim rework by turning clinical records into review-ready claim context.
| Leadership Pressure | Governance Support |
|---|---|
| High claim volume | Priority queues for reviewer focus |
| Incomplete evidence | Submission-readiness checks before escalation |
| Coding variability | ICD / procedure suggestions for human confirmation |
| Risk management | Risk flags with reasons for human review |
| Auditability | Claim-ready package with traceable evidence |
A governed medical knowledge layer turns guidelines, literature, institutional protocols, historical corrections, and de-identified cases into reusable structures for doctors, teams, and agents.
Answers are assembled from approved sources and linked back to guidelines, literature, policies, and institutional references.
Protocols, report logic, templates, and historical doctor corrections become searchable, callable knowledge assets.
Key findings, interpretation context, and recommended next-step scaffolds are packaged for clinical review.
Specialty cards, question banks, and pathway materials support resident training, affiliate clinics, and primary-care teams.
De-identification, scoped access, source traceability, and approval checkpoints control clinical use.
The operations command layer that makes patient records, queues, booking continuity, service conversion, and audit visible to management.
The same AI foundation supports documentation, reports, patient management, claims, and management visibility without rebuilding each workflow from zero.
Clinical and operational answers remain linked to approved knowledge, policies, and institution references.
Consultation speech becomes structured, doctor-reviewed electronic medical record content.
Paper reports, attachments, and legacy documents become structured inputs for review and follow-up.
Cross-language clinical content keeps medical terminology and institutional wording consistent.
Doctors, nurses, front desk, and operations teams can work from shared, versioned records.
Role-based access, audit logs, and controlled system links keep deployment accountable.
Decision makers need AI workflows that can be controlled, audited, and scaled across teams, not isolated tools that create new risk.
Shows where AI connects to daily service operations without replacing existing hospital systems.
Each pilot should agree on a small set of measurable indicators: turnaround time, doctor review rate, queue backlog, high-risk follow-up, claim rework, patient return actions, and audit completeness.
Start with official entry-point products, then connect hospital knowledge, AI CRMS operations, cross-institution coordination, and deployment readiness.
The five product layers can be deployed in cloud, hybrid, or on-premise models, with data control, security, service resilience, and procurement needs agreed institution by institution.
Leadership issue: multi-week report backlog limits employer reporting, abnormal-result follow-up, and population-health visibility.
Leadership issue: inconsistent evidence, coding context, and document completeness create avoidable claim rework before submission.
Leadership issue: referrals, patient context, and post-visit actions are fragmented across departments, institutions, and patient-management teams.
All client identities anonymised per disclosure policy. Case details reflect signed, active, or discussed engagements.
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