Healthcare
21 columns
200 rows
8 formats

Hospital Admission Risk Factors

This dataset provides detailed records of hospital admissions, including patient demographics, diagnoses, comorbidities, risk factors, and calculated risk scores. It is designed to support predictive analytics for identifying high-risk patients, optimizing resource allocation, and guiding targeted interventions in healthcare settings.

last updated 20 Aug 2025
by GoMask

The brief that made it

Predictive modeling for hospital admission risk

Sample rows

preview · 8 of 200 rows · all 21 columns

admission_idstring	discharge_statusstring	length_of_stayfloat	comorbiditiesstring	patient_idstring	admission_datedate	ageinteger	genderstring	ethnicitystring	primary_diagnosisstring	secondary_diagnosisstring	admission_typestring	admission_sourcestring	risk_scorefloat	risk_factorsstring	insurance_typestring	address_streetstring	address_citystring	address_statestring	address_postal_codestring	address_countrystring
ADM0001	recovered	2.1	blank	PAT1001	2022-03-19	3	male	Asian	J45.909 Asthma, unspecified	R05 Cough	emergency	self	0.11	prematurity	public	12 Elmwood Dr	San Jose	CA	95112	USA
ADM0002	transferred	18.3	hypertension, diabetes	PAT1002	2022-05-27	72	female	Jewish	I50.9 Heart failure, unspecified	E11.9 Type 2 diabetes mellitus	urgent	transfer	0.91	hypertension, diabetes	public	blank	blank	blank	blank	blank
ADM0003	recovered	1.9	blank	PAT1003	2023-12-01	27	female	Hispanic/Latino	F41.1 Generalized anxiety disorder	blank	elective	self	0.13	smoking	private	51 Oakridge Ave	Miami	FL	33101	USA
ADM0004	recovered	9.7	diabetes, hypertension	PAT1004	2021-10-25	58	male	African American	N18.9 Chronic kidney disease, unspecified	I10 Essential hypertension	emergency	referral	0.72	hypertension, diabetes	public	blank	blank	blank	blank	blank
ADM0005	recovered	1.5	blank	PAT1005	2023-02-18	5	unknown	Native American	B34.9 Viral infection, unspecified	blank	emergency	self	0.05	blank	public	32 Willow St	Tulsa	OK	74104	USA
ADM0006	recovered	5.3	diabetes, hypertension	PAT1006	2024-01-10	44	male	Middle Eastern	E11.9 Type 2 diabetes mellitus	I10 Essential hypertension	urgent	referral	0.33	diabetes, hypertension, obesity	private	89 Pinecrest Rd	Dearborn	MI	48120	USA
ADM0007	deceased	44.6	hypertension	PAT1007	2021-04-04	67	female	White	F03 Unspecified dementia	blank	other	transfer	0.97	dementia, hypertension	public	blank	blank	blank	blank	blank
ADM0008	recovered	4.8	smoking	PAT1008	2023-09-15	38	female	Pacific Islander	C34.90 Lung cancer, unspecified	blank	elective	referral	0.24	smoking, cancer	private	441 Sunset Blvd	Honolulu	HI	96813	USA
ADM0009	deceased	38.1	kidney disease, hypertension, diabetes	PAT1009	2022-06-30	82	male	White	I50.9 Heart failure, unspecified	N18.9 Chronic kidney disease	emergency	transfer	0.99	kidney disease, hypertension, diabetes	public	blank	blank	blank	blank	blank
ADM0010	recovered	3.6	blank	PAT1010	2023-07-22	15	female	African American	J18.9 Pneumonia, unspecified	blank	urgent	self	0.09	blank	public	1048 Maple St	Atlanta	GA	30303	USA
ADM0011	recovered	0.8	blank	PAT1011	2020-12-08	29	male	White	F19.20 Substance abuse, unspecified	blank	other	other	0.17	substance abuse	uninsured	blank	blank	blank	blank	blank
ADM0012	recovered	5.7	blank	PAT1012	2022-09-27	51	female	Asian	C50.919 Breast cancer, unspecified	blank	elective	referral	0.41	cancer	private	2312 Jasmine Ln	San Francisco	CA	94110	USA
ADM0013	transferred	26.9	hypertension	PAT1013	2024-03-11	89	female	White	F03 Unspecified dementia	blank	emergency	referral	0.88	dementia, hypertension	public	blank	blank	blank	blank	blank
ADM0014	recovered	7.1	obesity, diabetes	PAT1014	2021-06-16	36	male	Hispanic/Latino	K85.9 Acute pancreatitis, unspecified	blank	urgent	self	0.39	obesity, diabetes	public	8711 Redbud St	Houston	TX	77002	USA
ADM0015	transferred	36.4	hypertension, diabetes	PAT1015	2023-04-09	92	female	Jewish	I25.10 Coronary artery disease	E11.9 Diabetes mellitus	emergency	transfer	0.96	coronary disease, diabetes, hypertension	public	blank	blank	blank	blank	blank
ADM0016	recovered	3.7	hypertension	PAT1016	2023-01-20	48	male	African American	E66.9 Obesity, unspecified	blank	elective	referral	0.29	obesity, hypertension	private	74 Franklin Ave	Baltimore	MD	21201	USA
ADM0017	other	1.2	blank	PAT1017	2022-11-06	22	female	Caucasian/White	F32.9 Major depressive disorder	blank	elective	self	0.16	depression	private	3925 Oak St	Denver	CO	80202	USA
ADM0018	deceased	19.2	diabetes, hypertension, COPD	PAT1018	2020-08-04	86	male	Other	N18.9 Chronic kidney disease, unspecified	blank	urgent	transfer	0.93	kidney disease, diabetes, hypertension, COPD	public	blank	blank	blank	blank	blank
ADM0019	recovered	2.2	blank	PAT1019	2022-01-15	6	female	Asian	J18.9 Pneumonia, unspecified	blank	emergency	self	0.13	blank	public	143 Cherry Hill	Seattle	WA	98109	USA
ADM0020	recovered	2.8	blank	PAT1020	2022-08-18	40	male	Middle Eastern	I10 Essential hypertension	blank	elective	referral	0.19	hypertension	private	6651 Willow Dr	Cleveland	OH	44113	USA

What the 200 rows show

from the 200-row sample

Deceased (discharge status) stands out: mean length_of_stay is 33.7, against 7.6 for the rest.

4.6median length_of_stay
4genders
4admission types
4admission sources
4insurance types
15ethnicities

Mean length_of_stay by discharge_status200 rows

length_of_stay200 rows, in bands of 10

Median 4.6, from 0.40 to 60.0.

comorbidities102 rows with a value · 98 left blank

hypertension26
hypertension, diabetes17
diabetes, hypertension10
hypertension,diabetes8
obesity7
diabetes5
smoking3
smoking, hypertension3
obesity, diabetes2
dementia, diabetes2

21 columns by typefrom the column list below

string 17
integer 1
float 2
date 1

Columns

21 columns in three groups

blueprint · 21 columns
column	type	description	example
Text 17 columns
`admission_id`	string	Unique identifier for each hospital admission recordunique	ADM0001
`patient_id`	string	Unique identifier for the patient	PAT1001
`gender`	string	Gender of the patientmale · female · other · unknown	male
`ethnicity`	string	Ethnicity of the patientoptional	Asian
`primary_diagnosis`	string	Primary diagnosis code or description for admission	F03 Unspecified dementia
`secondary_diagnosis`	string	Secondary diagnosis code or description, if applicableoptional	R05 Cough
`comorbidities`	string	Comma-separated list of comorbid conditions present at admissionoptional	hypertension, diabetes
`admission_type`	string	Type of admission (e.g., emergency, elective, urgent)emergency · elective · urgent · other	emergency
`admission_source`	string	Source of admission (e.g., referral, transfer, self)referral · transfer · self · other · optional	self
`discharge_status`	string	Patient status at discharge (e.g., recovered, deceased, transferred)recovered · deceased · transferred · other · optional	recovered
`risk_factors`	string	Comma-separated list of identified risk factors (e.g., diabetes, hypertension, smoking)optional	prematurity
`insurance_type`	string	Type of insurance coverage for the patientprivate · public · uninsured · other · optional	public
`address_street`	string	Patient's street addressoptional	12 Elmwood Dr
`address_city`	string	Patient's city of residenceoptional	San Jose
`address_state`	string	Patient's state or province of residenceoptional	CA
`address_postal_code`	string	Patient's postal codeoptional	95112
`address_country`	string	Patient's country of residenceoptional	USA
Numbers 3 columns
`age`	integer	Age of the patient at time of admission0 to 120	3
`length_of_stay`	float	Length of hospital stay in days0 or more · optional	2.1
`risk_score`	float	Calculated risk score for likelihood of admission based on risk factors0 to 1 · optional	0.11
Dates and times 1 column
`admission_date`	date	Date of hospital admission	2022-03-19

Use it for

A healthcare dashboard
Length_of_stay by discharge_status and a breakdown of comorbidities. Excel, Power BI or Tableau.
Why do the 27 deceased rows have a mean length_of_stay of 33.7?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Admissions200
ADM00012.1recovered
ADM000218.3transfer…
ADM00031.9recovered
A software demo
Believable admissions with patient_id, admission_date and age to fill a screen in front of a buyer.

This dataset200 rows21 columns

Yours1,000 rows21 columnsaddress_street: UK only

blueprint · hospital-admission-risk-factors

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 21 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 patient demographics and medical history
Flag admissions related to chronic conditions
Exclude emergency-only admissions
Provide reason for admission

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

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: hospital-admission-risk-factors

What should your data show?

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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?