Insurance
21 columns
200 rows
8 formats

Insurance Claims and Medical Linkage

This dataset provides a comprehensive, privacy-preserving linkage between insurance claims and anonymized medical records, including claim details, patient demographics, provider information, and medical coding. It enables advanced analytics for healthcare utilization, cost analysis, and outcomes research across the insurance and medical sectors.

last updated 22 Jul 2025
by GoMask

The brief that made it

Cross-sector analysis of healthcare utilization and insurance claim patterns

Sample rows

preview · 8 of 200 rows · all 21 columns

claim_idstring	service_typestring	claim_amountfloat	provider_specialtystring	policy_idstring	anonymized_patient_idstring	claim_datedate	claim_statusstring	amount_paidfloat	diagnosis_codestring	procedure_codestring	provider_idstring	provider_namestring	service_datedate	patient_genderstring	patient_birth_yearinteger	patient_zip_codestring	admission_datedate	discharge_datedate	length_of_stayinteger	primary_insured_relationshipstring
CLAIM0001	outpatient	235.42	family medicine	POL10234	PAT0001001	2024-02-15	submitted	blank	J20.9	99213	PRV001	Lakeshore Family Clinic	2024-02-13	female	1994	62704	blank	blank	blank	self
CLAIM0002	inpatient	7852.67	cardiology	POL18921	PAT0001010	2023-07-12	approved	5890	I25.10	92928	PRV002	Metro Heart Institute	2023-07-10	male	1950	48201	2023-07-10	2023-07-14	4	self
CLAIM0003	preventive	120	optometry	POL10234	PAT0001001	2023-08-05	pending	blank	H52.4	blank	PRV003	Bright Eyes Vision Care	2023-08-04	female	1994	62704	blank	blank	blank	self
CLAIM0004	inpatient	18500	oncology	POL10023	PAT0001022	2022-11-19	approved	18000	C34.1	99291	PRV004	Starlight Oncology Center	2022-11-16	female	1960	81657	2022-11-16	2022-11-19	3	self
CLAIM0005	outpatient	6700.5	orthopedics	POL20331	PAT0001075	2023-03-21	denied	0	S72.001A	27130	PRV005	Summit Orthopedics Group	2023-03-20	male	1955	81657	blank	blank	blank	spouse
CLAIM0006	inpatient	1250000	rare diseases	POL99999	PAT0001120	2024-01-01	closed	850000	G12.21	63685	PRV006	blank	2023-12-28	female	2016	99999	2023-12-28	2024-01-01	4	child
CLAIM0007	preventive	44.5	pediatrics	POL10023	PAT0001023	2023-10-05	submitted	blank	J06.9	blank	PRV007	Green Valley Pediatrics	2023-10-03	male	2013	19104	blank	blank	blank	child
CLAIM0008	outpatient	110.95	internal medicine	POL10234	PAT0001002	2024-03-17	approved	100	R53.83	99395	PRV008	Downtown Internal Medicine	2024-03-15	male	1990	73301	blank	blank	blank	self
CLAIM0009	preventive	12.75	pediatrics	POL20331	PAT0001076	2022-06-22	pending	blank	Z00.129	blank	PRV009	Happy Kids Wellness	2022-06-20	female	2015	10001	blank	blank	blank	child
CLAIM0010	outpatient	487.99	dermatology	POL18921	PAT0001101	2023-12-18	approved	487.99	L70.0	12011	PRV010	River Dermatology Center	2023-12-16	male	1988	73301	blank	blank	blank	self
CLAIM0011	outpatient	8700	orthopedics	POL20331	PAT0001075	2023-04-22	approved	8500	M17.11	29881	PRV005	Summit Orthopedics Group	2023-04-19	male	1955	81657	blank	blank	blank	spouse
CLAIM0012	outpatient	87.7	internal medicine	POL10234	PAT0001002	2022-08-21	approved	87.7	E11.9	83036	PRV008	Downtown Internal Medicine	2022-08-20	male	1990	73301	blank	blank	blank	self
CLAIM0013	outpatient	780	psychiatry	POL18777	PAT0001102	2023-05-10	approved	780	F32.0	90837	PRV011	Cedar Grove Behavioral	2023-05-09	other	1985	60605	blank	blank	blank	self
CLAIM0014	inpatient	95000	oncology	POL10999	PAT0001110	2021-10-12	closed	90000	C50.911	19301	PRV012	Hope Breast Center	2021-10-05	female	1951	19104	2021-10-05	2021-10-12	7	spouse
CLAIM0015	preventive	88.5	optometry	POL12222	PAT0001155	2023-09-01	submitted	blank	H25.9	blank	PRV013	Visionary Eye Center	2023-08-30	male	2016	60605	blank	blank	blank	child
CLAIM0016	outpatient	1234.56	internal medicine	POL88776	PAT0001201	2022-03-10	approved	1234.56	E78.5	80061	PRV014	Evergreen Medical Group	2022-03-08	female	1977	73301	blank	blank	blank	self
CLAIM0017	inpatient	1250000	rare diseases	POL99999	PAT0001120	2024-03-03	reopened	900000	G12.21	63685	PRV006	blank	2024-02-28	female	2016	99999	2024-02-28	2024-03-03	4	child
CLAIM0018	outpatient	92	family medicine	POL10234	PAT0001003	2022-10-14	approved	92	J30.9	95024	PRV001	Lakeshore Family Clinic	2022-10-12	female	1999	62704	blank	blank	blank	self
CLAIM0019	outpatient	200	psychiatry	POL18777	PAT0001103	2023-06-02	pending	blank	F41.1	blank	PRV011	Cedar Grove Behavioral	2023-06-01	unknown	1982	19104	blank	blank	blank	other
CLAIM0020	preventive	76.8	optometry	POL12222	PAT0001155	2024-01-02	approved	76.8	H54.7	blank	PRV013	Visionary Eye Center	2023-12-30	male	2016	60605	blank	blank	blank	child

What the 200 rows show

from the 200-row sample

Inpatient (service type) stands out: mean claim_amount is 237,014, against 12,796 for the rest.

322.5median claim_amount
4patient genders
4primary insured relationships
6claim statuses
20patient zip codes
396.0median amount_paid

Mean claim_amount by service_type200 rows

claim_amount200 rows, in bands of 200k

Median 322.5, from 0.0 to 1,250,000.

provider_specialty200 rows · top 10 of 28 values

pediatrics25
internal medicine21
optometry18
pharmacy15
oncology14
orthopedics14
family medicine12
rare diseases10
cardiology9
psychiatry9

21 columns by typefrom the column list below

string 13
integer 2
float 2
date 4

Columns

21 columns in three groups

blueprint · 21 columns
column	type	description	example
Text 13 columns
`claim_id`	string	Unique identifier for each insurance claim recordunique	CLAIM0001
`policy_id`	string	Unique identifier for the insurance policy associated with the claim	POL10234
`anonymized_patient_id`	string	Anonymized unique identifier for the patient, used to link claims with medical records	PAT0001001
`claim_status`	string	Current status of the claim (e.g., submitted, approved, denied, pending)6 values	submitted
`diagnosis_code`	string	Primary diagnosis code (e.g., ICD-10) associated with the medical event	J20.9
`procedure_code`	string	Primary procedure code (e.g., CPT, HCPCS) for the medical service renderedoptional	99213
`provider_id`	string	Unique identifier for the healthcare provider or facility	PRV001
`provider_name`	string	Name of the healthcare provider or facilityoptional	Lakeshore Family Clinic
`provider_specialty`	string	Medical specialty of the provider (e.g., cardiology, orthopedics)optional	family medicine
`service_type`	string	Type of medical service (e.g., inpatient, outpatient, emergency, pharmacy)6 values	outpatient
`patient_gender`	string	Anonymized gender of the patientmale · female · other · unknown · optional	female
`patient_zip_code`	string	Anonymized ZIP or postal code of the patient's residenceoptional	62704
`primary_insured_relationship`	string	Relationship of the patient to the primary insured (e.g., self, spouse, child)self · spouse · child · other · optional	self
Numbers 4 columns
`claim_amount`	float	Total monetary amount claimed0 or more	235.42
`amount_paid`	float	Amount paid out by the insurer for this claim0 or more · optional	5890
`patient_birth_year`	integer	Year of birth of the patient (anonymized for privacy)1,900 to 2,024 · optional	1994
`length_of_stay`	integer	Length of hospital stay in days (if applicable)0 or more · optional	4
Dates and times 4 columns
`claim_date`	date	Date when the insurance claim was filed	2024-02-15
`service_date`	date	Date when the medical service was provided	2024-02-13
`admission_date`	date	Date of hospital admission (if applicable)optional	2023-07-10
`discharge_date`	date	Date of hospital discharge (if applicable)optional	2023-07-14

Use it for

An insurance dashboard
Claim_amount by service_type and a breakdown of provider_specialty. Excel, Power BI or Tableau.
Why do the 28 inpatient rows have a mean claim_amount of 237,014?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Claims200
CLAIM0001235.42outpatie…
CLAIM00027852.67inpatient
CLAIM0003120preventi…
A software demo
Believable claims with policy_id, anonymized_patient_id and claim_date to fill a screen in front of a buyer.

This dataset200 rows21 columns

Yours1,000 rows21 columnspatient_zip_code: UK only

blueprint · insurance-claims-and-medical-linkage

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

Each claim is matched to a unique medical event.
Records include claim type, amount, and resolution status.
Medical data covers diagnosis, procedures, and outcomes.
Patient identifiers are encrypted.
Temporal link between claim and treatment is preserved.

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: insurance-claims-and-medical-linkage

What should your data show?

No signup. No card.

Sample rows

What the 200 rows show

Columns

Use it for

An insurance dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Insurance

What should your data show?