SolutionAI for public hospitals
An AI copilot for nurses and doctors in a busy public hospital. It checks every arrival for red flags, drafts the consultation note for the doctor to sign, and follows patients at home after discharge, so the few who are getting worse are found before they come back as an emergency.
The nurse view of the hospital copilot. The patients are synthetic and the demo further down is the same console.
The context
Hospital teams work from vitals, notes, past visits and follow-up calls. The AI opportunity is to surface the patient who needs attention, draft the paperwork, and keep a clear record of who decided what.
Public hospitals in Thailand and elsewhere care for more patients than their staff can follow. The gaps are at the front door, in the consult room and after the patient goes home.
Heart failure, COPD, diabetes and elderly patients go home and can deteriorate quietly. Many come back as emergencies.
Patient records are sensitive personal data. The Personal Data Protection Act B.E. 2562 (2019), where it applies, and each hospital's own rules shape who may see what.
Nurses, doctors, patients and hospital directors each need their own view of the same record, and each action needs a name against it.
General background, not medical advice. Specific protocols, systems and volumes will come from the hospital during discovery.
What is the current issue?
The issues under each area are working hypotheses to test with clinical teams, not findings.
Every arrival checked against the same red-flag rules
AI opportunity. The nurse types what the patient says and the vitals. Fixed red-flag rules set the level, AI writes the summary and the nurse confirms.
See the arrivals pageAI drafts, the doctor decides
AI opportunity. Speech-to-text writes the conversation, AI reads past visits, drafts the note and suggests medicines. The doctor edits, resolves every flag and signs.
See the audit log of AI and human actionsCheck-ins after discharge, set by the nurse, done by AI
AI opportunity. AI suggests check-in days and writes questions for the patient's condition. The nurse edits and approves the plan and the patient answers in their own app.
See the follow-up pageThe right person hears in time, with a record of who decided what
AI opportunity. Fixed protocol rules read the answers and raise alerts, AI explains and drafts the action, and a nurse or doctor handles each alert or dismisses it with a reason. Every step is logged.
See the alerts pageHow can AI help?
Each workflow starts from work nurses and doctors already do. Fixed rules and models do the checking, an AI agent drafts, and a named person decides.
The nurse starts from a ranked, explained list and confirms how urgent each arrival is.
Types what the patient says and the vitals.
Fixed rules set the level: Emergency, Urgent or Routine.
Summarises the note and says which rule fired and why.
Confirms it, or changes it with a reason.
The doctor sees patients in order, with the reason on screen.
Tools the assistant calls
Illustrative function names for a pilot design.
Today
Urgency depends on who sees the patient first, and the reason is rarely written down.
With AI
Every arrival is checked against the same rules, and the nurse confirms with the reason on screen.
The intelligence layer
The four steps share one record and one set of AI services, so each new use reuses what is already built. Data flows up, and rules, checks and the audit log wrap every step.
Records, vitals and visits stay in the hospital's own system
Fixed protocols decide alerts, a model scores risk
AI reads the record and writes drafts, a person approves
One console for the care team
What would the user see?
This is the nurse view of the copilot: the morning briefing, arrivals, follow-up, alerts, patients and the audit log. Approve a follow-up plan, handle an alert and see every action recorded.
Hospital copilotDemo provincial hospital (fictional)Tue 13 Oct 2026 · Nurse Anna
Counting today's work…4 steps
Check new arrivals, approve follow-up plans and act on alerts and missed check-ins.
Run the consult with an AI scribe, sign prescriptions and review alerts.
Ask about their own care in plain words and answer the care team's questions from home.
Watch readmission and workload, add staff accounts and read the audit log.
Key screens
Each tab opens the same console on one page and works on its own. All patients are synthetic and the answers are pre-written demonstrations, not medical advice.
Watch the briefing read the record, then open the alert it points to.
Hospital copilotDemo provincial hospital (fictional)Tue 13 Oct 2026 · Nurse Anna
Counting today's work…4 steps
A morning briefing for the nurse: what needs attention now, in plain words, from the record and the open alerts.
What comes next?
This page is the concept and working demo. Pilot scope, data, budget and timeline are agreed only after the hospital has seen it.
The scope areas this solution covers.
This page, with a working console on synthetic patients.
Adjust as more information arrives.
Validate the problem, data and needs.
Scope, data, integrations, criteria, budget, timeline.
Show the idea working, so stakeholders react to something concrete.
Output: a stakeholder-ready demo.
One ward or clinic and an agreed set of real, approved records, alongside today's process.
Output: a scoped pilot with agreed measures.
More wards and clinics on the same platform, run and supported.
Output: a supported service.
Built for clinical work
A clinical team will ask where a flag came from and who decided what. The design answers that from the start.
The console here replays pre-written answers on synthetic patients in a fictional hospital. A live system would read the hospital's own records.
Every name, vital sign and score on this page is made up. The red-flag rules shown are for illustration, not a real triage protocol.
Nothing is acted on until a nurse or doctor has read it, and each decision is recorded against a name.
The audit log shows every AI, model, rule and human action: who, what, when and which model.
Review the demo with your team.Tell us what a pilot must prove.
Decision support, not a diagnosis. All patients on this page are synthetic and the hospital is fictional. The red-flag rules shown are for illustration only; a real deployment uses the hospital's approved protocol. This page is not an official system of any hospital.