One public hospital or Local Hospital Network, one financial year from 2011-12 and one urgency category. It gives the number of elective surgeries, the median days waited, the share of patients treated within the recommended time, and the peer group's median and average beside them.

What are the urgency categories?

The treating doctor assigns each patient to urgent (surgery recommended within 30 days), semi-urgent (within 90 days) or non-urgent (within 365 days). The Institute notes that states and hospitals assign the categories differently, which affects comparisons.

What do the blanks mean?

The Institute writes <5 for a count under five and NP where the data did not meet its criteria. Those cells are null here with a suppressed flag. A dash, meaning no patients were reported, and Not peered are null. The share within recommended time is a fraction, so 0.95 means 95 per cent.

Datasets / Commonwealth / AIHW / AIHW MyHospitals

Elective surgery waiting times by hospital and urgency category, Australia

Name: Elective surgery waiting times by hospital and urgency category, Australia
Creator: Australian Institute of Health and Welfare
Published: 2026-10-03
License: https://creativecommons.org/licenses/by/4.0/

live

Publisher Australian Institute of Health and WelfareUpdated several times a yearLicence CC BY 4.0Latest 2026-10-03Rows 12,159Fields 11

The Urgency category sheet of the MyHospitals elective surgery waiting times data extract from the AIHW MyHospitals Data API. Each row is one reporting unit, financial year and urgency category, with the number of surgeries, the median waiting time in days, the percentage of surgeries within the recommended time and the peer group's median and average.

Also called: Surgery waiting list times by hospital, Elective surgery median wait days, Public hospital surgery wait times Australia, Percentage of elective surgery within recommended time.

Part of AIHW MyHospitals, 3 tables the publisher releases together: Hospitals, Local Hospital Networks and Primary Health Networks in MyHospitals, Australia, Emergency department patients seen on time by hospital and triage category, Australia.

Before you use it

What is in each row?: One public hospital or Local Hospital Network, one financial year from 2011-12 and one urgency category. It gives the number of elective surgeries, the median days waited, the share of patients treated within the recommended time, and the peer group's median and average beside them.
What are the urgency categories?: The treating doctor assigns each patient to urgent (surgery recommended within 30 days), semi-urgent (within 90 days) or non-urgent (within 365 days). The Institute notes that states and hospitals assign the categories differently, which affects comparisons.
What do the blanks mean?: The Institute writes <5 for a count under five and NP where the data did not meet its criteria. Those cells are null here with a suppressed flag. A dash, meaning no patients were reported, and Not peered are null. The share within recommended time is a fraction, so 0.95 means 95 per cent.

Made for agents

Connect your AI agent

Any agent that connects to publicdata.au/mcp can find this dataset, read its 11 fields, count and filter across all 12,159 rows and diff its versions. Every answer names the version and carries AIHW's attribution. No key.

Then ask it: What does Elective surgery waiting times by hospital and urgency category, Australia hold, and what changed in the newest version?

claude mcp add --transport http publicdata https://publicdata.au/mcp

One command. No key, no account, no sign-up.

https://publicdata.au/mcp

Streamable HTTP. Cursor, VS Code or any client that connects to a remote MCP server.

Using https://publicdata.au/llms.txt, how many people died on Queensland roads in 2025, by month?

For an assistant that reads the web and has no connector.

ExploreChart every row in your browser

A dashboard over every row. Click to filter, add panels, share a link or embed a frame.

Query APIFilter and count from a URL

aggregate?group=reporting_unit&metric=max.median_wait_days&year=eq.2024%E2%80%9325&reporting_unit_type=eq.Hospital&urgency_category=eq.Non-urgent%20elective%20surgery

Median days waited for non-urgent surgery at each hospital in the newest year: 448 Fiona Stanley Hospital, 427 Gladstone Hospital, 394 John Hunter Hospital, 393 The Queen Elizabeth Hospital.

Download it as Excel, CSV, JSON and 6 more formats

Pick a format and a version. Excel is picked first because it opens in the tools most offices have. The URL is yours to keep. A dated version never changes.

Format

Version "Latest" redirects to the newest version and will change when the publisher releases again. A dated version never changes.

https://publicdata.au/d/au-elective-surgery-waiting-times/latest/data.xlsx

Download

In your own tools

Use it in Excel, R, Python and more

Excel and Power BI read the CSV from its address and refresh from it. The R and Python packages take any dataset on this site by its slug, so a new dataset needs no new release. The DuckDB file attaches read-only over HTTPS, and a query reads only the blocks it touches.

The dated URL in the code never changes. https://publicdata.au/d/au-elective-surgery-waiting-times/latest/ redirects to the newest version.

https://publicdata.au/d/au-elective-surgery-waiting-times/latest/data.csv

In Excel choose Data, then From Web, and paste this address. Excel keeps it, so Refresh All reads the newest version. A sheet holds about a million rows; past that, load the query to the Data Model.

https://publicdata.au/d/au-elective-surgery-waiting-times/latest/data.csv

In Power BI Desktop choose Get data, then Web, paste this address and choose Anonymous when asked how to sign in. A scheduled refresh reads the same address, so the report follows each new version.

library(publicdataau)
df <- pd_read("au-elective-surgery-waiting-times")
pd_attribution(df)

install.packages("publicdataau"). pd_read() fetches the version's Parquet file; pd_rows() and pd_aggregate() ask the query API instead, and pd_connect() attaches the DuckDB file.

import publicdata_au as pd_au
df = pd_au.read("au-elective-surgery-waiting-times")
df.attrs["publicdata"]["attribution"]

pip install "publicdata-au[pandas]". read() fetches the version's Parquet file; rows() and aggregate() ask the query API, and connect() attaches the DuckDB file.

INSTALL httpfs; LOAD httpfs;
ATTACH 'https://publicdata.au/d/au-elective-surgery-waiting-times/v/2026-10-03/data.duckdb' AS au_elective_surgery_waiting_times (READ_ONLY);
SELECT state, count(*) FROM au_elective_surgery_waiting_times.records GROUP BY 1 ORDER BY 2 DESC;

The DuckDB file attaches read-only over HTTPS and only the blocks a query touches are read. Parquet works the same way: FROM read_parquet(url).

const res = await fetch("https://publicdata.au/api/v1/datasets/au-elective-surgery-waiting-times/aggregate?group=reporting_unit&metric=max.median_wait_days&year=eq.2024%E2%80%9325&reporting_unit_type=eq.Hospital&urgency_category=eq.Non-urgent%20elective%20surgery");
const { rows, publicdata } = await res.json();
console.log(rows, publicdata.attribution);

The query API answers a page on any site as well as Node, with no key. It returns the rows and the attribution the licence asks for.

What is in it

Column names are made snake_case and the publisher's original header is kept beside each one. Blank cells are null. Nothing is added, removed, ranked or summarised.

Field	Type	Publisher's header	Note
reporting_unit	string	Reporting unit
reporting_unit_type	string	Reporting unit type
state	string	State
peer_group	string	Peer group
year	string	Year
urgency_category	string	Urgency category
surgeries	integer	Number of surgeries
median_wait_days	integer	Median waiting time (days)
peer_group_median_days	integer	Peer group median (days)
share_within_recommended_time	number	Percentage of surgeries within recommended time	The fraction of surgeries done within the recommended time, 0.95 for 95 per cent.
peer_group_average	number	Peer group average
suppressed	array		Names of the fields the publisher suppressed in this row. The cells are null.

Download this table as a data dictionary (Excel), or read it as a Frictionless Table Schema at schema.json. The key is reporting_unit, reporting_unit_type, year, urgency_category.

A sample of 10 rows

Urgent elective surgery at the busiest hospitals in the newest year, the busiest hospital in each state first, then the next busiest. A blank cell is shown as null.

reporting_unit	reporting_unit_type	state	peer_group	year	urgency_category	surgeries	median_wait_days	peer_group_median_days	share_within_recommended_time	peer_group_average
Princess Alexandra Hospital	Hospital	Qld	Major hospitals	2024–25	Urgent elective surgery	7803	14	14	0.82	0.88
Royal Adelaide Hospital	Hospital	SA	Major hospitals	2024–25	Urgent elective surgery	5544	26	14	0.6	0.88
Fiona Stanley Hospital	Hospital	WA	Major hospitals	2024–25	Urgent elective surgery	5354	16	14	0.77	0.88
Royal Hobart Hospital	Hospital	Tas	Major hospitals	2024–25	Urgent elective surgery	4475	18	14	0.66	0.88
Royal Melbourne Hospital [City Campus]	Hospital	Vic	Major hospitals	2024–25	Urgent elective surgery	4388	13	14	1	0.88
The Canberra Hospital	Hospital	ACT	Major hospitals	2024–25	Urgent elective surgery	4115	19	14	0.82	0.88
Westmead Hospital	Hospital	NSW	Major hospitals	2024–25	Urgent elective surgery	3321	14	14	1	0.88
Royal Darwin Hospital	Hospital	NT	Major hospitals	2024–25	Urgent elective surgery	1295	18	14	0.69	0.88
Royal Brisbane & Women's Hospital	Hospital	Qld	Major hospitals	2024–25	Urgent elective surgery	6692	20	14	0.86	0.88
Royal Perth Hospital Wellington Street Campus	Hospital	WA	Major hospitals	2024–25	Urgent elective surgery	4589	14	14	0.87	0.88

The first row as JSON

As it appears in data.json.

GET https://publicdata.au/d/au-elective-surgery-waiting-times/latest/data.json
{
  "reporting_unit": "Armidale Hospital",
  "reporting_unit_type": "Hospital",
  "state": "NSW",
  "peer_group": "Medium regional hospitals",
  "year": "2011–12",
  "urgency_category": "Non-urgent elective surgery",
  "surgeries": 411,
  "median_wait_days": 280,
  "peer_group_median_days": 97,
  "share_within_recommended_time": 1.0,
  "peer_group_average": 0.96,
  "suppressed": []
}

Query it without downloading

The query API returns only the rows you ask for, as JSON, NDJSON or CSV, from the same typed rows as the files. It needs no key. Build a query here and run it, then copy the URL or the code into your own work.

Try rows or aggregate for this dataset. The query builder needs JavaScript.

How filters work

Each filter is field=operator.value in the query string, and every filter must match. A number or date field compares as a number or date, and a text field compares as text. Values are case-sensitive except with ilike. Prefix not. to negate a filter, as not.eq.value.

Operator	What it matches
eq.value	Equal to the value.
neq.value	Not equal to the value. A blank cell does not match.
gt.value	Greater than the value.
gte.value	Greater than or equal to the value.
lt.value	Less than the value.
lte.value	Less than or equal to the value.
like.text	Matches a pattern where * stands for any run of characters. Case-sensitive.
ilike.text	The same as like, ignoring case.
in.(a,b,c)	Equal to any value in the list. A value cannot contain a comma.
is.null	Blank in the source, or suppressed by the publisher.

Parameter	What it does
select	Fields to return, comma-separated. Every field when absent.
order	field.asc or field.desc, comma-separated. The publisher's row order when absent.
limit	Rows per page, 1 to 10,000. 100 when absent.
offset	Rows to skip. The next URL in each answer sets it for you.
group	On aggregate, fields to group by, comma-separated.
metric	On aggregate, count, sum.field, avg.field, min.field or max.field, comma-separated. count when absent.
format	json, ndjson or csv. JSON carries the provenance header, and the others carry it in response headers.

Without a version the API answers from the newest loaded version, and that answer changes when the publisher releases again. Put versions/<date>/ before rows or aggregate for an answer that never changes. /api/v1/datasets/<slug>/versions lists the loaded versions. The 1 newest version is loaded; versions lists them.

The API allows 60 requests in 10 seconds from one address. Above that it answers 429 for 10 seconds with a Retry-After header, a RateLimit-Policy header and a JSON body that gives the limit. Every API answer carries the same RateLimit-Policy. A client should wait the Retry-After seconds, or read the files, which have no limit. openapi.json describes this dataset's query paths for client generators and agents.

https://publicdata.au/mcp is a remote MCP server over Streamable HTTP with the same tools, defined in the same place, so a tool added to the pages is added here too. It needs no key and no account. The row tools are held to the query API's limit of 60 queries in 10 seconds from one address, and each call is two queries because it also counts the matching rows. Add it to Claude Code with claude mcp add --transport http publicdata https://publicdata.au/mcp, add the URL in Claude as a custom connector, or give it to any client that connects to remote MCP servers. Each queryable dataset is also a resource at https://publicdata.au/d/<slug>/fields.json, which lists its fields with their types, the publisher's descriptions, their ranges and the values they hold, so an agent can read a dataset's shape before it writes a query.

Smaller files

The same rows split by a field, one JSON file each. Each file carries the same provenance header.

By state (8 files)

ACT NSW NT Qld SA Tas Vic WA

by/state/index.json lists every file with its row count.

What changed

One version for every release the publisher has made since this site started following the dataset. The date is the day the file changed on the portal.

2026-10-03
12,159 rows11 fieldsxlsx696cfb20ab6f

The publisher's server states no change date, so this version is dated by the fetch.

versions.json · changes.json · history.tar.zst (66 KB, every version's Parquet and manifest)

Questions

How do I download Elective surgery waiting times by hospital and urgency category as a CSV file?

Open https://publicdata.au/d/au-elective-surgery-waiting-times/latest/data.csv. It redirects to the newest dated version, which is https://publicdata.au/d/au-elective-surgery-waiting-times/v/2026-10-03/data.csv today. The same path serves Excel, JSON, Parquet, SQLite, DuckDB, NDJSON, Arrow. A dated URL never changes, so use it when the file must stay the same.

Can I open Elective surgery waiting times by hospital and urgency category in Excel?

Yes. https://publicdata.au/d/au-elective-surgery-waiting-times/v/2026-10-03/data.xlsx is a workbook with the 12,159 rows on a records sheet, the field list on a second sheet and the provenance on a third. The CSV also opens in Excel. https://publicdata.au/d/au-elective-surgery-waiting-times/v/2026-10-03/data.csv.gz is the CSV at about a tenth of the size.

What years does Elective surgery waiting times by hospital and urgency category cover?

The current version covers the years since 2011. Each release from AIHW becomes a new dated version here, and earlier versions stay online.

How often is Elective surgery waiting times by hospital and urgency category updated?

AIHW releases it several times a year. This site checks the portal every week and adds a dated version when the file changes.

Can I use Elective surgery waiting times by hospital and urgency category commercially?

Yes. CC BY 4.0 allows commercial use, redistribution and derived works as long as the attribution is kept. The attribution string is in this page's side column and inside every file.

Is this the official source for Elective surgery waiting times by hospital and urgency category?

No. The publisher is Australian Institute of Health and Welfare, and its page is https://www.aihw.gov.au/copyright. This site republishes the publisher's file without changing its content. The original sits beside every version as source.xlsx with its SHA-256, so the two can be compared.

How do I get only the rows for one state?

Every version has one JSON file per value of state, 8 files in the current version, listed with row counts at https://publicdata.au/d/au-elective-surgery-waiting-times/v/2026-10-03/by/state/index.json. For example https://publicdata.au/d/au-elective-surgery-waiting-times/v/2026-10-03/by/state/nsw.json holds the 4,263 rows where state is NSW.

What this site did to the data. Cells were typed, headers were renamed and the encoding was made UTF-8. Rows were left alone. The publisher's file sits beside every version as source.xlsx so the change can be checked.

The publisher releases AIHW MyHospitals as 3 files at once, so all 3 tables here share a version date.

Attribution and citation

The licence requires the publisher's attribution. We ask that you also name publicdata.au and link to the version you used.

Based on Australian Institute of Health and Welfare material. Australian Institute of Health and Welfare, Australian Government, MyHospitals Data API, elective surgery waiting times data extract, sourced 3 October 2026, https://myhospitalsapi.aihw.gov.au/, licensed under CC BY 4.0. Serialised and versioned by National Digital at publicdata.au, https://publicdata.au/d/au-elective-surgery-waiting-times/v/2026-10-03/

The attribution alone is inside every file. Harvard is the author-date form a report or a brief asks for. Paste the HTML on a web page so the links carry.

Licence record

Australian Institute of Health and Welfare states the licence, CC BY 4.0, on its own page, which was read and recorded here on 1 October 2026.

Every fetch reads the licence again and stops when it has changed. The fetch that made version 2026-10-03 read it from www.aihw.gov.au on 3 October 2026.

Also published on

Hugging Faceload_dataset("National-Digital/au-elective-surgery-waiting-times")
Kagglewith a starter notebook
ZenodoDOI 10.5281/zenodo.23123810

Each copy carries the same attribution and links back to this page.

For agents and tools

datapackage.jsonFrictionless data package pointing at the latest version
schema.jsonTable Schema for every field
schema.xlsxthe same field list as an Excel data dictionary
versions.jsonevery version with date, rows, fields and hash
changes.jsondiff for every consecutive pair
manifest.jsonwhere the bytes came from and when
Query APIfiltered rows and counts without a download
openapi.jsonOpenAPI 3.1 for this dataset's query paths
explore/charts and dashboards in the browser, shareable and embeddable
index.mdthis page as Markdown

This page carries a schema.org Dataset record and is listed in /.well-known/ard.json and /catalog.json.

Source

Australian Institute of Health and Welfare publishes this table on myhospitalsapi.aihw.gov.au. Questions about the content go to the publisher. Questions about the files here go to corrections.

Fetched 3 October 2026. Source SHA-256 696cfb20ab6fbcb1…

Improve this dataset

This page is built from register/au-elective-surgery-waiting-times.yaml on GitHub. Anyone can propose a fix to it, such as a clearer description or a field the publisher renamed, as a pull request.

Report a file that differs from the publisher's · How to contribute