Health Service Demand and Operations
Explore which published figures could help a hospital planning team understand demand, beds, and discharge, and where the evidence remains incomplete.
- Published sources
- 2
- Starter data
- 20 synthetic records
- Domain build partner
- build-health-operations
- Last reviewed
From the draft strategy
Opportunity
The draft strategy names AI support for health operational processes as a potential public-service application: help with discharge coordination, bed demand, and how limited capacity is shared out, without saying what the system would be aiming at or what it must never do.
Where the draft says it
Table 2 — potential public-service applications
Source register
Research already done
We reviewed these published sources so you do not have to start from zero. Each source shows what it covers, how it can be accessed, and why it matters.
2 sources
Department of Health
OpenHospital waiting times statistics
- What it covers
- Quarterly outpatient, inpatient and day case, diagnostic, and cancer waiting figures for Northern Ireland, broken down by health and social care trust and by how long people have waited.
- Why it matters here
- It is the published shape this playbook's synthetic records imitate: a specialty, a period, a length-of-wait band, and a count of people, and nothing about any one person.
Northern Ireland Statistics and Research Agency
OpenHealth and social care statistics
- What it covers
- The wider collection of Northern Ireland health statistics, including hospital activity and waiting lists, primary care, social care, workforce, and health inequalities.
- Why it matters here
- It shows how much of the picture a planning team would need sits in separate publications, which is most of the real work in this example.
Starter data
Starter dataset
Use 20 synthetic waiting records to explore a hospital planning question without holding health and social care trust data.
AI-assisted research helped identify and interpret the published sources. We then created a small, non-sensitive synthetic dataset shaped by the information those sources expose. It is for exploration—not evidence, training, or operational decisions.
- Records
- 20
- Fields
- 5
Field preview
table viewidSYN-HO-01 · SYN-HO-02 · SYN-HO-03
specialtyGeneral surgery · Trauma and orthopaedics · Ear, nose and throat
quarter2025-Q3 · 2025-Q4 · 2026-Q1
waitingBand0-6 weeks · over 52 weeks · 14-26 weeks
patientsWaitingBand2500-5000 · 10000-20000 · 5000-10000
How it was prepared
AI authored fictional specialty, quarter, wait-band, and count-band values in the shape of published statistics.
Limitations
- The numbers are invented bands, not rounded real figures, so nothing here should be quoted as how long anyone in Northern Ireland is waiting.
- Real planning needs arrivals, beds, staffing, and discharge delays together, and this file has only one of those.
- A tidy quarterly table hides the day-to-day movement and the individual circumstances that actually decide when someone leaves hospital.
Clone and build
Domain build partner
Build with a domain-aware coding agent
Clone the repository, then ask your coding agent to load the skill that ships with it. The skill brings the opportunity, sources, starter data, known unknowns and constraints into the conversation before anything is proposed. It follows the open Agent Skills standard, so it works in Claude Code, Codex and any other agent that reads skills.
01 / Clone
git clone https://github.com/Hypership-Software/lab-notes.ai.git02 / Invoke
- Claude Code
/build-health-operations- Codex
$build-health-operations
Conditions, not a checklist
Before you build
These are not footnotes. They are conditions that any responsible prototype would need to address.
Operational records are sensitive
Operational records describe people who are ill, so the real version of this work involves sensitive information even when the output looks like a chart.
Single targets can distort care
Aiming at one number, such as average flow, can quietly make things worse for people whose needs are complicated or unusual.
Discharge decisions remain clinical
Knowing how many people are likely to arrive does not tell anyone whether a particular person is ready to go home, and that decision has to stay with clinicians.