Healthcare
29 columns
200 rows
8 formats

Hospital Readmission Risk Factors

This dataset provides detailed records of hospital admissions, capturing patient demographics, clinical diagnoses, procedures, comorbidities, and key risk factors for readmission. Designed for healthcare analytics, it enables predictive modeling, quality improvement, and targeted intervention strategies to reduce hospital readmissions and improve patient outcomes.

last updated 12 Jul 2025
by GoMask

The brief that made it

Predictive modeling of hospital readmission risk

Sample rows

preview · 8 of 200 rows · all 29 columns

admission_idstring	discharge_dispositionstring	num_prior_admissionsinteger	readmitted_within_30_daysboolean	ethnicitystring	patient_idstring	admission_datedate	discharge_datedate	ageinteger	genderstring	racestring	primary_diagnosis_codestring	secondary_diagnosis_codesstring	procedure_codesstring	admission_typestring	admission_sourcestring	length_of_stayinteger	num_chronic_conditionsinteger	comorbiditiesstring	insurance_typestring	primary_physician_idstring	hospital_idstring	hospital_departmentstring	discharge_medicationsstring	follow_up_appointment_scheduledboolean	smoking_statusstring	alcohol_usestring	body_mass_indexfloat	socioeconomic_statusstring
ADM10001	home	3	false	Not Hispanic or Latino	PAT10001	2024-01-03	2024-01-10	78	female	White	I50.20	E11.9,I10	0JH60XZ,3E03305	emergency	ER	7	4	heart failure, diabetes, hypertension, CKD	Medicare	PHY100	HSP001	cardiology	lisinopril, metformin, furosemide	true	former	never	27.2	medium
ADM10002	home	1	false	blank	PAT10002	2024-02-14	2024-02-21	44	male	Black	J18.9	blank	5A1955Z	urgent	clinic	7	1	asthma	Private	PHY109	HSP002	pulmonology	albuterol	true	never	former	25	high
ADM10003	home	0	false	blank	PAT10003	2024-03-01	2024-03-03	0	female	Mixed	P07.3	blank	blank	newborn	born in hospital	2	0	blank	Medicaid	PHY102	HSP003	neonatal	blank	false	unknown	unknown	13.1	low
ADM10004	home	2	true	Not Hispanic or Latino	PAT10004	2024-01-16	2024-01-22	63	male	Black	E11.65	I10	blank	emergency	ER	6	3	diabetes, hypertension, obesity	Private	PHY105	HSP002	endocrinology	metformin, atorvastatin	true	former	current	32.4	medium
ADM10005	home	0	false	blank	PAT10005	2024-04-10	2024-04-15	28	female	Asian	L40.0	blank	blank	elective	physician referral	5	1	psoriasis	Private	PHY101	HSP004	dermatology	adalimumab	false	never	never	21.9	high
ADM10006	skilled nursing facility	4	false	Not Hispanic or Latino	PAT10001	2024-02-04	2024-02-11	79	female	White	I25.10	E78.5	027034Z	urgent	clinic	7	5	coronary artery disease, hyperlipidemia, diabetes, CKD, hypertension	Medicare	PHY100	HSP001	cardiology	atorvastatin, lisinopril, metformin	true	former	never	26.8	medium
ADM10007	home	0	false	blank	PAT10006	2024-01-11	2024-01-14	19	male	White	F32.9	blank	blank	elective	physician referral	3	1	depression	Self-pay	PHY106	HSP005	psychiatry	sertraline	true	never	never	22.3	high
ADM10008	home	2	false	blank	PAT10007	2024-03-08	2024-03-13	64	female	Black	C34.91	E11.9,I10	0WQF0ZZ	emergency	ER	5	3	lung cancer, diabetes, hypertension	Private	PHY107	HSP006	oncology	metformin, amlodipine	true	current	current	24.9	medium
ADM10009	home	0	false	blank	PAT10008	2024-02-20	2024-02-21	0	male	Pacific Islander	P29.3	blank	blank	newborn	born in hospital	1	0	blank	Medicaid	PHY102	HSP004	neonatal	blank	false	unknown	unknown	13.9	low
ADM10010	home	1	false	Hispanic or Latino	PAT10009	2024-03-14	2024-03-16	35	female	White	N39.0	blank	blank	urgent	walk-in	2	0	blank	Private	PHY108	HSP007	urology	ciprofloxacin	false	never	never	23.5	medium
ADM10011	skilled nursing facility	8	false	blank	PAT10010	2024-02-05	2024-02-21	88	female	White	G30.9	I10,E11.9	blank	emergency	nursing home	16	6	dementia, hypertension, diabetes, CKD, osteoporosis, depression	Medicare	PHY110	HSP002	geriatrics	memantine, amlodipine, metformin	true	former	former	19.2	low
ADM10012	home	0	false	Hispanic or Latino	PAT10011	2024-01-22	2024-01-23	22	male	Hispanic	R10.9	blank	blank	emergency	ER	1	0	blank	Self-pay	PHY112	HSP008	emergency	blank	false	never	never	24	medium
ADM10013	home	1	false	blank	PAT10012	2024-02-28	2024-03-03	56	other	Asian	K21.9	blank	blank	urgent	clinic	4	2	GERD, hypertension	Private	PHY113	HSP009	gastroenterology	omeprazole	false	former	current	28.5	medium
ADM10014	skilled nursing facility	6	false	blank	PAT10013	2024-02-01	2024-02-05	71	male	White	I63.9	I10,E11.9	03CG0ZZ	emergency	ER	4	4	stroke, hypertension, diabetes, CKD	Medicare	PHY114	HSP010	neurology	aspirin, metformin, amlodipine	true	former	never	25.7	low
ADM10015	home	1	false	blank	PAT10014	2024-03-21	2024-03-23	31	female	American Indian	F41.1	blank	blank	elective	physician referral	2	1	generalized anxiety	Private	PHY115	HSP011	psychiatry	escitalopram	true	never	never	21.1	medium
ADM10016	deceased	12	false	blank	PAT10015	2024-01-25	2024-02-04	90	unknown	Unknown	I50.9	E11.9,I10	blank	other	nursing home	10	8	heart failure, diabetes, hypertension, CKD, dementia, COPD, anemia, osteoporosis	Medicare	PHY116	HSP012	ICU	blank	false	former	former	16.2	low
ADM10017	home	2	false	blank	PAT10016	2024-03-02	2024-03-08	53	male	White	M16.11	blank	0SR902Z	elective	physician referral	6	2	osteoarthritis, hypertension	Private	PHY117	HSP013	orthopedics	acetaminophen, lisinopril	true	never	current	28.7	medium
ADM10018	home	0	false	blank	PAT10017	2024-02-12	2024-02-13	1	male	Pacific Islander	J21.9	blank	blank	emergency	ER	1	0	blank	Medicaid	PHY103	HSP014	pediatrics	blank	false	unknown	unknown	15.2	medium
ADM10019	home	0	false	blank	PAT10018	2024-01-09	2024-01-11	27	female	White	N87.9	blank	blank	urgent	clinic	2	0	blank	Self-pay	PHY104	HSP015	gynecology	azithromycin	false	never	never	23.8	medium
ADM10020	home	3	true	blank	PAT10019	2024-03-18	2024-03-25	72	female	White	I10	blank	blank	emergency	ER	7	4	hypertension, diabetes, CKD, hyperlipidemia	Medicare	PHY118	HSP016	internal medicine	lisinopril, metformin	true	former	never	26.1	medium

What the 200 rows show

from the 200-row sample

Home (discharge disposition) stands out: mean num_prior_admissions is 2.0, against 11.9 for the rest.

3%readmitted_within_30_days = true
1median num_prior_admissions
3socioeconomic statuses
4genders
4smoking statuses
4alcohol uses

Mean num_prior_admissions by discharge_disposition200 rows

num_prior_admissions200 rows, in bands of 5

Median 1, from 0 to 30.

ethnicity41 rows with a value · 159 left blank

Non-Hispanic25
Not Hispanic5
Not Hispanic or Latino4
Hispanic3
Hispanic or Latino2
Hispanic/Latino1
Unknown1

29 columns by typefrom the column list below

string 20
integer 4
float 1
date 2
boolean 2

Columns

29 columns in four groups

blueprint · 29 columns
column	type	description	example
Text 20 columns
`admission_id`	string	Unique identifier for each hospital admission event.unique	ADM10001
`patient_id`	string	Unique identifier for the patient associated with the admission.	PAT10001
`gender`	string	Patient's gender.male · female · other · unknown	female
`race`	string	Patient's self-identified race.9 races · optional	White
`ethnicity`	string	Patient's self-identified ethnicity.7 ethnicities · optional	Not Hispanic or Latino
`primary_diagnosis_code`	string	ICD code for the primary diagnosis at admission.	I50.20
`secondary_diagnosis_codes`	string	Comma-separated ICD codes for secondary diagnoses at admission.12 codeses · optional	E11.9,I10
`procedure_codes`	string	Comma-separated codes for procedures performed during admission.optional	0JH60XZ,3E03305
`admission_type`	string	Type of admission (e.g., emergency, elective, urgent).6 values	emergency
`admission_source`	string	Source from which the patient was admitted (e.g., physician referral, transfer, ER).8 sources · optional	ER
`discharge_disposition`	string	Disposition of the patient at discharge (e.g., home, skilled nursing facility, deceased).3 dispositions	home
`comorbidities`	string	Comma-separated list of comorbid conditions (e.g., diabetes, hypertension).optional	asthma
`insurance_type`	string	Type of insurance coverage (e.g., Medicare, Medicaid, private, self-pay).6 types · optional	Medicare
`primary_physician_id`	string	Unique identifier for the primary physician overseeing the admission.optional	PHY100
`hospital_id`	string	Unique identifier for the hospital where the admission occurred.	HSP001
`hospital_department`	string	Department or unit where the patient was admitted (e.g., cardiology, ICU).optional	cardiology
`discharge_medications`	string	Comma-separated list of medications prescribed at discharge.optional	albuterol
`smoking_status`	string	Patient's smoking status at admission.current · former · never · unknown · optional	former
`alcohol_use`	string	Patient's alcohol use status at admission.current · former · never · unknown · optional	never
`socioeconomic_status`	string	Socioeconomic status category (e.g., low, medium, high) if available.3 statuses · optional	medium
Numbers 5 columns
`age`	integer	Age of the patient at the time of admission (in years).0 to 120	78
`length_of_stay`	integer	Number of days between admission and discharge.0 or more	7
`num_prior_admissions`	integer	Number of prior hospital admissions for the patient.0 or more · optional	3
`num_chronic_conditions`	integer	Number of chronic conditions diagnosed for the patient.0 or more · optional	4
`body_mass_index`	float	Patient's BMI at the time of admission.10 to 80 · optional	27.2
Dates and times 2 columns
`admission_date`	date	Date when the patient was admitted to the hospital.	2024-01-03
`discharge_date`	date	Date when the patient was discharged from the hospital.	2024-01-10
True or false 2 columns
`readmitted_within_30_days`	boolean	Indicates if the patient was readmitted within 30 days of discharge.	false
`follow_up_appointment_scheduled`	boolean	Indicates if a follow-up appointment was scheduled at discharge.optional	true

Use it for

A healthcare dashboard
The readmitted_within_30_days rate, num_prior_admissions by discharge_disposition and a breakdown of ethnicity. Excel, Power BI or Tableau.
Why do the 169 home rows have a mean num_prior_admissions of 2.0?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Admissions200
ADM100013home
ADM100042home
ADM100203home
A software demo
Believable admissions with patient_id, admission_date and discharge_date to fill a screen in front of a buyer.

This dataset200 rows29 columns

Yours1,000 rows29 columns

blueprint · hospital-readmission-risk-factors

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 29 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 demographics, diagnoses, and length of stay
Label if patient readmitted within 30 days
Capture insurance type and discharge status
Ensure no PHI is present

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: hospital-readmission-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?