Healthcare
22 columns
200 rows
8 formats

Healthcare Readmission Risk Factors

This dataset provides detailed records of patient hospital episodes, including demographics, clinical diagnoses, procedures, risk factors, and readmission outcomes. Designed to support clinical decision support, resource allocation, and value-based care analytics, it enables robust analysis of factors influencing hospital readmissions and patient outcomes.

last updated 22 Jul 2025
by GoMask

The brief that made it

Predictive modeling for hospital readmission risk

Sample rows

preview · 8 of 200 rows · all 22 columns

episode_idstring	insurance_typestring	comorbidity_indexfloat	readmittedboolean	readmission_typestring	patient_idstring	admission_datedate	discharge_datedate	readmission_datedate	ageinteger	sexstring	race_ethnicitystring	primary_diagnosis_codestring	secondary_diagnosis_codesstring	procedure_codesstring	length_of_stay_daysinteger	discharge_dispositionstring	num_prior_admissions_12mointeger	num_medications_at_dischargeinteger	discharge_instructions_givenboolean	followup_appointment_scheduledboolean	primary_care_provider_idstring
E0001	medicare	6.8	false	blank	P1001	2021-04-10	2021-04-14	blank	81	female	White	I50.9	E11.9	5A1955Z	4	skilled nursing facility	6	12	true	false	PCP123
E0002	medicaid	0.2	false	blank	P1002	2021-06-21	2021-06-22	blank	2	male	Hispanic	J21.9	blank	blank	1	home	0	1	true	true	blank
E0003	medicare	7.1	true	unplanned	P1003	2021-02-15	2021-02-18	2021-03-08	76	male	Black	E11.65	I10	30233N1	3	home	4	10	false	false	PCP101
E0004	private	1.3	false	blank	P1004	2021-03-11	2021-03-19	blank	35	female	Asian	M54.5	blank	blank	8	home	1	3	true	true	PCP134
E0005	medicaid	0	false	blank	P1005	2021-01-12	2021-01-16	blank	0	female	White	P07.3	blank	blank	4	home	0	0	true	false	blank
E0006	medicare	9.2	true	emergency	P1006	2021-04-22	2021-04-27	2021-05-10	88	female	Black	J44.1	I25.10	5A1221Z	5	skilled nursing facility	10	15	false	false	PCP108
E0007	private	0.7	false	blank	P1007	2021-07-05	2021-07-08	blank	40	male	Asian	K21.0	blank	blank	3	home	0	2	true	true	PCP106
E0008	medicaid	0.1	false	blank	P1008	2021-05-18	2021-05-19	blank	8	female	White	J45.909	blank	blank	1	home	0	1	true	true	blank
E0009	medicare	8.6	false	blank	P1009	2021-04-02	2021-04-12	blank	94	female	White	G30.9	I10	blank	10	rehab	8	13	true	false	PCP111
E0010	private	0.4	false	blank	P1010	2021-03-15	2021-03-16	blank	27	male	Black	S06.0X0A	blank	blank	1	home	0	1	true	false	PCP131
E0011	medicaid	0	false	blank	P1011	2021-01-27	2021-01-28	blank	1	female	Asian	A09	blank	blank	1	home	0	0	true	false	blank
E0012	medicare	7.8	true	unplanned	P1001	2021-09-19	2021-09-23	2021-10-10	82	female	White	N39.0	I25.2	blank	4	rehab	7	14	false	false	PCP123
E0013	private	1.1	false	blank	P1012	2021-07-23	2021-07-25	blank	49	female	Hispanic	F41.9	blank	blank	2	home	0	2	true	true	PCP112
E0014	medicare	9.1	true	elective	P1013	2021-08-01	2021-08-10	2021-08-23	87	male	Black	I63.9	I10,I25.10	03CG0ZZ	9	skilled nursing facility	14	15	false	false	PCP114
E0015	private	1.4	false	blank	P1014	2021-05-03	2021-05-09	blank	36	female	White	G43.909	blank	blank	6	home	0	2	true	true	PCP116
E0016	medicare	5.9	false	blank	P1015	2021-02-18	2021-03-07	blank	73	male	White	I25.10	blank	blank	17	rehab	3	8	true	false	PCP109
E0017	medicaid	0.3	false	blank	P1016	2021-06-03	2021-06-04	blank	6	unknown	Black	J06.9	blank	blank	1	home	0	0	true	false	blank
E0018	medicare	6.5	true	emergency	P1017	2021-03-03	2021-03-06	2021-03-27	69	female	White	E11.9	I10	3E05317	3	home	3	8	false	false	PCP121
E0019	private	1.2	false	blank	P1018	2021-07-10	2021-07-13	blank	32	male	Asian	R07.9	blank	blank	3	home	0	2	true	false	PCP113
E0020	medicare	8.1	false	blank	P1019	2021-08-15	2021-08-24	blank	78	female	White	I48.91	I10	blank	9	rehab	6	11	true	true	PCP122

What the 200 rows show

from the 200-row sample

Medicare (insurance type) stands out: mean comorbidity_index is 8.2, against 1.00 for the rest.

19%readmitted = true
1.9median comorbidity_index
3sexes
6discharge dispositions
8race ethnicities
15secondary diagnosis codeses

Mean comorbidity_index by insurance_type200 rows

comorbidity_index200 rows, in bands of 2

Median 1.9, from 0.0 to 10.2.

readmission_type37 rows with a value · 163 left blank

emergency18
unplanned10
planned5
elective3
other1

22 columns by typefrom the column list below

string 11
integer 4
float 1
date 3
boolean 3

Columns

22 columns in four groups

blueprint · 22 columns
column	type	description	example
Text 11 columns
`episode_id`	string	Unique identifier for each patient episode or hospital admission.unique	E0001
`patient_id`	string	Unique identifier for the patient associated with this episode.	P1001
`readmission_type`	string	Type of readmission (e.g., planned, unplanned, emergency, elective).planned · unplanned · emergency · elective · other · optional	unplanned
`sex`	string	Sex of the patient.male · female · other · unknown	female
`race_ethnicity`	string	Race or ethnicity of the patient as recorded in the medical record.8 ethnicities · optional	White
`primary_diagnosis_code`	string	Primary diagnosis code (e.g., ICD-10) for the episode.	I50.9
`secondary_diagnosis_codes`	string	Comma-separated list of secondary diagnosis codes (e.g., ICD-10) for the episode.optional	E11.9
`procedure_codes`	string	Comma-separated list of procedure codes (e.g., CPT, ICD-10-PCS) performed during the episode.optional	5A1955Z
`discharge_disposition`	string	Patient's status or destination at discharge (e.g., home, skilled nursing facility, deceased).6 values	skilled nursing facility
`insurance_type`	string	Type of insurance coverage (e.g., Medicare, Medicaid, Private, Self-pay).medicare · medicaid · private · self-pay · other · optional	medicare
`primary_care_provider_id`	string	Identifier for the patient's primary care provider at the time of discharge.optional	PCP123
Numbers 5 columns
`age`	integer	Age of the patient at the time of admission.0 to 120	81
`length_of_stay_days`	integer	Total length of stay in days for the episode.0 or more	4
`comorbidity_index`	float	Comorbidity risk score (e.g., Charlson or Elixhauser index) for the patient at the time of admission.0 or more · optional	6.8
`num_prior_admissions_12mo`	integer	Number of prior admissions for the patient in the 12 months preceding this episode.0 or more · optional	6
`num_medications_at_discharge`	integer	Number of active medications prescribed at discharge.0 or more · optional	12
Dates and times 3 columns
`admission_date`	date	Date when the patient was admitted for this episode.	2021-04-10
`discharge_date`	date	Date when the patient was discharged from this episode.	2021-04-14
`readmission_date`	date	Date of readmission if applicable; null if not readmitted.optional	2021-03-08
True or false 3 columns
`readmitted`	boolean	Indicates whether the patient was readmitted within the defined follow-up period (e.g., 30 days).	false
`discharge_instructions_given`	boolean	Indicates whether discharge instructions were provided to the patient.optional	true
`followup_appointment_scheduled`	boolean	Indicates whether a follow-up appointment was scheduled at discharge.optional	false

Use it for

A healthcare dashboard
The readmitted rate, comorbidity_index by insurance_type and a breakdown of readmission_type. Excel, Power BI or Tableau.
Why do the 73 medicare rows have a mean comorbidity_index of 8.2?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Episodes200
E00016.8medicare
E00037.1medicare
E00069.2medicare
A software demo
Believable episodes with patient_id, admission_date and discharge_date to fill a screen in front of a buyer.

This dataset200 rows22 columns

Yours1,000 rows22 columns

blueprint · healthcare-readmission-risk-factors

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 22 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

Chronic conditions increase readmission risk
Care transition notes impact outcomes
Socioeconomic metrics included
Time-from-discharge tracked
Comorbidity index calculated

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: healthcare-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?