Healthcare
25 columns
100 rows
8 formats

Institutional Claims - Facility (UB-04)

This dataset provides comprehensive, standardized records of institutional claims from hospitals and healthcare facilities, including financial charges, clinical codes, patient demographics, and billing details. It enables detailed analysis of healthcare utilization, cost patterns, and claim processing for both inpatient and outpatient services, supporting operational, regulatory, and research needs.

last updated 2 Nov 2025
by GoMask

The brief that made it

Healthcare cost and utilization analysis

Sample rows

preview · 8 of 100 rows · all 25 columns

claim_idstring	service_typestring	facility_chargesfloat	drg_codestring	facility_idstring	patient_idstring	admission_datedate	discharge_datedate	type_of_billstring	revenue_codestring	room_chargesfloat	primary_diagnosis_codestring	secondary_diagnosis_codestring	procedure_codestring	payer_idstring	claim_statusstring	billing_provider_npistring	rendering_provider_npistring	patient_dobdate	patient_genderstring	patient_address_streetstring	patient_address_citystring	patient_address_statestring	patient_address_postal_codestring	patient_address_countrystring
CLM0001	inpatient	8200.75	291	FAC101	PAT0001	2023-04-21	2023-04-26	111	0123	2350.5	J18.9	E11.9	99233	PAYER01	paid	1865478910	1548392018	1952-08-17	female	123 Oak St	Cleveland	OH	44113	USA
CLM0002	outpatient	630	blank	FAC102	PAT0002	blank	blank	131	0450	blank	M54.5	blank	71020	PAYER02	submitted	1839501873	blank	1988-02-13	male	456 Elm Ave	Phoenix	AZ	85016	USA
CLM0003	inpatient	13500	470	FAC103	PAT0003	2023-07-03	2023-07-08	112	0100	3700	I10	E78.5	27130	PAYER03	paid	1729384650	1928374650	1956-11-05	female	78 Maple Lane	Columbus	OH	43215	USA
CLM0004	emergency	950	blank	FAC104	PAT0004	blank	blank	131	0451	blank	R07.9	blank	99285	PAYER04	pending	1987654321	1234567890	2001-09-14	male	99 Pine Ct	Dallas	TX	75204	USA
CLM0005	inpatient	7400	193	FAC105	PAT0005	2023-05-10	2023-05-12	111	0120	2100.25	J44.1	E11.65	31575	PAYER05	denied	1234567891	blank	1975-03-22	female	101 Cedar Dr	Miami	FL	33101	USA
CLM0006	outpatient	210	blank	FAC106	PAT0006	blank	blank	131	0300	blank	J45.909	blank	94640	PAYER06	paid	1346798521	1098765432	2010-01-30	other	250 Spruce Blvd	Denver	CO	80203	USA
CLM0007	inpatient	9000	177	FAC107	PAT0007	2023-08-19	2023-08-25	113	0118	3000	I63.9	F17.200	70450	PAYER07	paid	1982345678	1543219876	1969-06-10	male	345 Birch St	Seattle	WA	98101	USA
CLM0008	other	320	blank	FAC108	PAT0008	blank	blank	131	0420	blank	L20.84	blank	96372	PAYER08	pending	1230987654	blank	1991-12-20	female	789 Willow Rd	Atlanta	GA	30303	USA
CLM0009	inpatient	8500.2	291	FAC109	PAT0009	2023-06-15	2023-06-19	111	0123	2500	E11.9	I10	99232	PAYER09	adjusted	1876543210	blank	1964-03-24	male	56 Aspen Way	Boston	MA	02118	USA
CLM0010	emergency	720	blank	FAC110	PAT0010	blank	blank	132	0452	blank	S06.0X0A	blank	99284	PAYER10	submitted	1923456789	1432109876	2003-05-18	male	45 Poplar St	Chicago	IL	60616	USA
CLM0011	outpatient	180	blank	FAC101	PAT0011	blank	blank	131	0301	blank	K21.9	blank	43239	PAYER01	paid	1865478910	1548392018	1970-10-11	female	321 Chestnut St	Cleveland	OH	44113	USA
CLM0012	inpatient	14200	470	FAC102	PAT0012	2023-09-02	2023-09-05	112	0100	4100	N39.0	blank	51702	PAYER02	pending	1839501873	blank	1938-06-22	female	987 Oakridge Dr	Phoenix	AZ	85016	USA
CLM0013	outpatient	320	blank	FAC103	PAT0013	blank	blank	131	0305	blank	L03.031	blank	96372	PAYER03	submitted	1729384650	blank	1981-12-31	male	67 Walnut Ave	Columbus	OH	43215	USA
CLM0014	emergency	1040	blank	FAC104	PAT0014	blank	blank	132	0451	blank	R50.9	blank	93010	PAYER04	denied	1987654321	1234567890	2014-04-09	unknown	234 Fir St	Dallas	TX	75204	USA
CLM0015	inpatient	9100	177	FAC105	PAT0015	2023-10-17	2023-10-21	113	0118	3250	I63.9	blank	70450	PAYER05	adjusted	1234567891	blank	1959-07-15	male	400 Sycamore Rd	Miami	FL	33101	USA
CLM0016	outpatient	280	blank	FAC106	PAT0016	blank	blank	131	0300	blank	J45.909	blank	94640	PAYER06	paid	1346798521	blank	2012-11-25	female	150 Willow Blvd	Denver	CO	80203	USA
CLM0017	inpatient	8700	291	FAC107	PAT0017	2023-12-05	2023-12-10	111	0123	2600	J18.9	blank	99233	PAYER07	paid	1982345678	1543219876	1945-02-06	male	777 Ash St	Seattle	WA	98101	USA
CLM0018	other	340	blank	FAC108	PAT0018	blank	blank	131	0420	blank	L20.84	blank	96372	PAYER08	pending	1230987654	blank	1993-10-21	female	456 Sequoia Ave	Atlanta	GA	30303	USA
CLM0019	inpatient	13900	470	FAC109	PAT0019	2023-06-01	2023-06-04	112	0100	4000	I10	E78.5	27130	PAYER09	submitted	1876543210	blank	1966-04-19	female	123 Dogwood Dr	Boston	MA	02118	USA
CLM0020	emergency	995	blank	FAC110	PAT0020	blank	blank	131	0450	blank	S06.0X0A	blank	99285	PAYER10	paid	1923456789	1432109876	2005-12-13	male	8 Redwood St	Chicago	IL	60616	USA

What the 100 rows show

from the 100-row sample

Inpatient (service type) stands out: mean facility_charges is 7,750, against 637.9 for the rest.

1,203median facility_charges
4patient genders
5claim statuses
12type of bills
15rendering provider npis
18billing provider npis

Mean facility_charges by service_type100 rows

facility_charges100 rows, in bands of 2,000

Median 1,203, from 175.0 to 14,500.

drg_code44 rows with a value · 56 left blank

47010
1778
2917
1933
2923
2932
1942
6832
0811
3921

25 columns by typefrom the column list below

string 20
float 2
date 3

Columns

25 columns in three groups

blueprint · 25 columns
column	type	description	example
Text 20 columns
`claim_id`	string	Unique identifier for each institutional claimunique	CLM0001
`facility_id`	string	Unique identifier for the hospital or facility submitting the claim	FAC101
`patient_id`	string	Unique identifier for the patient receiving services	PAT0001
`service_type`	string	Type of service provided (e.g., inpatient, outpatient, emergency)inpatient · outpatient · emergency · other	inpatient
`type_of_bill`	string	Code representing the type of bill submitted (UB-04 standard)12 bills	111
`drg_code`	string	Diagnosis Related Group code for the claimoptional	291
`revenue_code`	string	Revenue code indicating the type of service or department	0123
`primary_diagnosis_code`	string	Primary diagnosis code (ICD-10 or ICD-9) for the claim	J18.9
`secondary_diagnosis_code`	string	Secondary diagnosis code (ICD-10 or ICD-9) for the claim, if applicable11 codes · optional	E11.9
`procedure_code`	string	Procedure code (CPT/HCPCS) for services providedoptional	99233
`payer_id`	string	Identifier for the insurance payer or responsible party	PAYER01
`claim_status`	string	Current status of the claim (e.g., submitted, paid, denied, pending)submitted · paid · denied · pending · adjusted	paid
`billing_provider_npi`	string	National Provider Identifier (NPI) of the billing provider	1865478910
`rendering_provider_npi`	string	National Provider Identifier (NPI) of the rendering provideroptional	1548392018
`patient_gender`	string	Gender of the patientmale · female · other · unknown · optional	female
`patient_address_street`	string	Street address of the patientoptional	123 Oak St
`patient_address_city`	string	City of the patientoptional	Cleveland
`patient_address_state`	string	State of the patientoptional	OH
`patient_address_postal_code`	string	Postal code of the patientoptional	44113
`patient_address_country`	string	Country of the patientoptional	USA
Numbers 2 columns
`room_charges`	float	Total charges for room and board during the stay0 or more · optional	2350.5
`facility_charges`	float	Total charges billed by the facility for services rendered0 or more	8200.75
Dates and times 3 columns
`admission_date`	date	Date the patient was admitted to the facilityoptional	2023-04-21
`discharge_date`	date	Date the patient was discharged from the facilityoptional	2023-04-26
`patient_dob`	date	Date of birth of the patientoptional	1952-08-17

Use it for

A healthcare dashboard
Facility_charges by service_type and a breakdown of drg_code. Excel, Power BI or Tableau.
Why do the 44 inpatient rows have a mean facility_charges of 7,750?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Claims100
CLM00018200.75inpatient
CLM0002630outpatie…
CLM000313500inpatient
A software demo
Believable claims with facility_id, patient_id and admission_date to fill a screen in front of a buyer.

This dataset100 rows25 columns

Yours1,000 rows25 columnspatient_address_street: UK only

blueprint · institutional-claims-facility-ub-04

Behind this dataset

Same schema. As many rows as you need.

These 100 rows came out of a blueprint — 25 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

Claim ID unique per facility claim
UB-04 (CMS-1450) form for institutional billing
Type of bill: inpatient, outpatient, ER, observation
Revenue codes for departments (room, pharmacy, lab, radiology)
HCPCS codes for procedures
DRG (Diagnosis Related Group) for inpatient
MS-DRG with weight for Medicare severity adjustment
Admission date, discharge date, length of stay

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: institutional-claims-facility-ub-04

What should your data show?

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Sample rows

What the 100 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?