Healthcare
16 columns
200 rows
8 formats

Healthcare Patient Acuity Levels

This dataset provides simulated hospital patient admission records with detailed acuity scores and categorical labels, supporting advanced triage modeling and resource planning. It includes patient demographics, diagnoses, admission details, and outcome data, making it ideal for healthcare analytics, operational optimization, and predictive modeling of patient care needs.

last updated 12 Jul 2025
by GoMask

The brief that made it

Developing and validating triage models for emergency departments

Sample rows

preview · 8 of 200 rows · all 16 columns

patient_idstring	acuity_levelstring	resource_needs_scorefloat	secondary_diagnosis_codesstring	admission_idstring	admission_datetimedatetime	discharge_datetimedatetime	ageinteger	genderstring	primary_diagnosis_codestring	acuity_scorefloat	triage_categorystring	departmentstring	attending_physician_idstring	length_of_stay_daysfloat	outcomestring
PAT0001	low	2.3	blank	ADM0001	2022-03-07T11:23:00Z	2022-03-09T14:21:00Z	0	female	P07.3	1.4	non-urgent	Neonatal	PHY011	2.1	recovered
PAT0002	moderate	4.1	blank	ADM0002	2021-12-15T08:42:00Z	2021-12-19T15:10:00Z	13	male	J45.2	3.1	urgent	Pediatrics	PHY007	4.3	recovered
PAT0003	high	44.6	E11.9	ADM0003	2023-04-21T20:03:00Z	2023-04-22T13:00:00Z	87	female	I50.9	19.8	urgent	Geriatrics	blank	0.7	recovered
PAT0004	moderate	2.7	blank	ADM0004	2024-01-10T03:15:00Z	blank	54	female	F32.1	6.3	unknown	Psychiatry	blank	blank	ongoing care
PAT0005	moderate	3.6	blank	ADM0005	2022-08-25T16:47:00Z	2022-08-28T10:00:00Z	24	other	E11.65	3.7	urgent	Endocrinology	PHY003	2.7	recovered
PAT0006	critical	90.8	I10	ADM0006	2022-05-12T09:37:00Z	blank	118	unknown	J44.0	21.3	emergent	ICU	PHY001	blank	ongoing care
PAT0007	high	8.3	I12.9	ADM0007	2023-07-02T19:19:00Z	2023-07-08T12:00:00Z	64	male	N18.5	10.2	urgent	Nephrology	PHY012	5.7	recovered
PAT0008	low	2.2	blank	ADM0008	2021-11-03T06:51:00Z	2021-11-03T16:45:00Z	0	female	P22.0	2.1	non-urgent	Neonatal	PHY015	0.4	recovered
PAT0009	critical	112.3	E11.9	ADM0009	2023-03-14T14:20:00Z	blank	92	male	I63.9	24.6	emergent	ICU	PHY002	blank	unknown
PAT0010	moderate	4.7	blank	ADM0010	2022-06-18T21:44:00Z	2022-06-20T12:16:00Z	19	female	J18.9	4.4	urgent	Pulmonology	PHY008	1.6	recovered
PAT0011	high	18.6	blank	ADM0011	2022-09-09T13:34:00Z	2022-09-09T20:18:00Z	84	unknown	J44.1	8.8	urgent	Geriatrics	blank	0.3	recovered
PAT0012	moderate	4.1	blank	ADM0012	2023-05-22T11:09:00Z	2023-05-24T18:00:00Z	36	male	E78.2	6.8	urgent	Endocrinology	PHY016	2.3	recovered
PAT0013	low	3	blank	ADM0013	2021-08-02T17:27:00Z	blank	15	male	F84.0	2.3	unknown	Pediatrics	PHY013	blank	ongoing care
PAT0014	high	7.7	I10	ADM0014	2021-02-13T10:45:00Z	2021-02-15T09:30:00Z	51	female	E11.22	9.5	urgent	General Medicine	PHY009	1.9	recovered
PAT0015	low	1.2	blank	ADM0015	2024-02-08T05:50:00Z	2024-02-08T22:10:00Z	0	male	P59.0	0.7	non-urgent	Neonatal	PHY017	0.7	recovered
PAT0016	moderate	2.9	blank	ADM0016	2023-11-17T23:13:00Z	2023-11-18T14:54:00Z	17	female	J45.9	2.9	urgent	Pediatrics	PHY006	0.7	recovered
PAT0017	moderate	2.2	blank	ADM0017	2023-09-14T12:38:00Z	blank	58	female	F20.0	5.4	unknown	Psychiatry	blank	blank	ongoing care
PAT0018	high	72.9	I50.9	ADM0018	2024-03-14T07:09:00Z	2024-03-15T11:00:00Z	105	unknown	E11.9	15.3	urgent	Geriatrics	blank	1.2	recovered
PAT0019	high	9.2	blank	ADM0019	2023-01-22T18:26:00Z	2023-01-28T13:38:00Z	39	male	I21.9	12.7	urgent	Cardiology	PHY005	5.8	recovered
PAT0020	critical	97.1	I10	ADM0020	2023-06-29T03:12:00Z	blank	120	male	J44.1	22.4	emergent	ICU	PHY001	blank	ongoing care

What the 200 rows show

from the 200-row sample

Critical (acuity level) stands out: mean resource_needs_score is 86.5, against 8.6 for the rest.

5.2median resource_needs_score
4genders
4triage categories
5outcomes
11departments
47median age

Mean resource_needs_score by acuity_level200 rows

resource_needs_score200 rows, in bands of 20

Median 5.2, from 1.2 to 120.0.

secondary_diagnosis_codes44 rows with a value · 156 left blank

E11.910
I109
E78.53
E66.93
J44.02
J44.12
I12.91
I50.91
E78.21
Z51.51

16 columns by typefrom the column list below

string 10
integer 1
float 3
datetime 2

Columns

16 columns in three groups

blueprint · 16 columns
column	type	description	example
Text 10 columns
`patient_id`	string	Unique identifier for each patient recordunique	PAT0001
`admission_id`	string	Unique identifier for each hospital admission or encounterunique	ADM0001
`gender`	string	Gender of the patientmale · female · other · unknown	female
`primary_diagnosis_code`	string	Primary diagnosis code (e.g., ICD-10) assigned at admission	P07.3
`secondary_diagnosis_codes`	string	Comma-separated list of secondary diagnosis codes (e.g., ICD-10)optional	E11.9
`acuity_level`	string	Categorical acuity level label (e.g., low, moderate, high, critical)low · moderate · high · critical	low
`triage_category`	string	Triage category assigned at admission (e.g., emergent, urgent, non-urgent)emergent · urgent · non-urgent · unknown · optional	non-urgent
`department`	string	Hospital department or unit where the patient was admitted (e.g., ICU, ER, General Medicine)11 departments	Neonatal
`attending_physician_id`	string	Identifier for the attending physician responsible for the patientoptional	PHY011
`outcome`	string	Patient outcome at discharge (e.g., recovered, transferred, deceased, ongoing care)recovered · transferred · deceased · ongoing care · unknown · optional	recovered
Numbers 4 columns
`age`	integer	Age of the patient at the time of admission0 to 120	0
`acuity_score`	float	Numerical acuity score representing patient severity (higher indicates more severe)0 or more	1.4
`length_of_stay_days`	float	Total length of stay in days (calculated as discharge minus admission)0 or more · optional	2.1
`resource_needs_score`	float	Score estimating the expected resource needs for the patient (e.g., nursing hours, equipment)0 or more · optional	2.3
Dates and times 2 columns
`admission_datetime`	datetime	Date and time when the patient was admitted to the hospital	2022-03-07T11:23:00Z
`discharge_datetime`	datetime	Date and time when the patient was discharged from the hospitaloptional	2022-03-09T14:21:00Z

Use it for

A healthcare dashboard
Resource_needs_score by acuity_level and a breakdown of secondary_diagnosis_codes. Excel, Power BI or Tableau.
Why do the 19 critical rows have a mean resource_needs_score of 86.5?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Patients200
PAT00012.3low
PAT00024.1moderate
PAT000344.6high
A software demo
Believable patients with admission_id, admission_datetime and discharge_datetime to fill a screen in front of a buyer.

This dataset200 rows16 columns

Yours1,000 rows16 columns

blueprint · healthcare-patient-acuity-levels

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 16 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Include age, chief complaint, and acuity score
Record vital signs and admission type
Label as low/high/critical acuity
No PHI included

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. New accounts start with 25 free credits.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: healthcare-patient-acuity-levels

What should your data show?

No signup. No card.

Sample rows

What the 200 rows show

Columns

Use it for

A healthcare dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Healthcare

What should your data show?