Healthcare
24 columns
100 rows
8 formats

Coordination of Benefits (COB) and Dual Coverage

This dataset provides a comprehensive view of coordination of benefits (COB) for patients with multiple insurance coverages, detailing primary/secondary determination, payment sequencing, and special rules such as the birthday rule and Medicare coordination. It enables precise tracking of claims, payments, and insurance relationships, supporting compliance, analytics, and operational efficiency in healthcare billing and insurance management.

last updated 2 Nov 2025
by GoMask

The brief that made it

Automated COB processing and payment sequencing for healthcare claims

Sample rows

preview · 8 of 100 rows · all 24 columns

patient_idstring	claim_statusstring	payment_amountfloat	subrogation_flagboolean	insurance_company_namestring	patient_first_namestring	patient_last_namestring	patient_date_of_birthdate	patient_genderstring	insurance_coverage_idstring	insurance_policy_numberstring	insurance_group_numberstring	insurance_plan_typestring	insurance_effective_datedate	insurance_termination_datedate	coverage_priorityinteger	birthday_rule_appliedboolean	medicare_coordination_appliedboolean	payment_sequence_numberinteger	payment_datedate	claim_idstring	claim_service_datedate	claim_total_amountfloat	coordination_notesstring
PAT00001	paid	350.75	false	Blue Shield	James	Smith	1965-05-14	male	ICOV10001	BS-73489922	GRP-5138	PPO	2021-01-01	blank	1	false	false	1	2023-06-12	CLM-30001	2023-06-10	470	Primary coverage processed, standard claim.
PAT00002	paid	150	true	UnitedHealthcare	Linda	Nguyen	1982-11-23	female	ICOV10002	UHC-52938872	GRP-9827	HMO	2022-03-01	blank	1	false	false	1	2023-04-22	CLM-30002	2023-04-20	300	Accident claim, subrogation initiated.
PAT00003	paid	80	false	Aetna	Evelyn	Turner	2009-09-08	female	ICOV10003	AET-43129902	GRP-7715	Commercial	2023-01-01	blank	1	true	false	1	2023-05-01	CLM-30003	2023-04-30	100	Birthday rule applied for dependent coverage.
PAT00004	paid	420	false	Medicare	Michael	Garcia	1950-03-16	male	ICOV10004	MCR-88992231	blank	Medicare	2015-07-01	blank	1	false	true	1	2023-03-02	CLM-30004	2023-02-28	600	Medicare primary, coordination applied.
PAT00005	paid	70	false	Cigna	Sofia	Khan	1997-07-29	female	ICOV10005	CIG-77233128	GRP-1128	PPO	2020-09-12	blank	2	false	false	2	2023-05-27	CLM-30005	2023-05-25	150	Secondary coverage processed.
PAT00006	paid	230	false	Medicare	Henry	Lee	1946-01-04	male	ICOV10006	MCR-00238821	blank	Medicare	2011-05-01	blank	1	false	true	1	2023-01-18	CLM-30006	2023-01-15	350	Medicare coordination for aged patient.
PAT00007	paid	50	false	Aetna	Olivia	Martinez	2012-04-11	female	ICOV10007	AET-99872130	GRP-4421	Commercial	2023-02-01	blank	2	true	false	2	2023-05-03	CLM-30007	2023-04-29	110	Birthday rule for dependent, secondary coverage.
PAT00008	paid	95	false	Humana	John	Wong	1978-09-17	male	ICOV10008	HUM-11172553	GRP-7662	HMO	2019-11-01	blank	1	false	false	1	2023-07-14	CLM-30008	2023-07-12	190	Routine primary claim.
PAT00009	paid	80	false	Blue Cross	Maria	Olsen	1989-12-02	female	ICOV10009	BC-29811227	GRP-3355	PPO	2021-04-01	blank	2	false	false	2	2023-03-04	CLM-30009	2023-03-01	200	Secondary PPO payment.
PAT00010	paid	300	false	Medicare	David	King	1957-07-05	male	ICOV10010	MCR-55788161	blank	Medicare	2017-02-01	blank	1	false	true	1	2023-08-02	CLM-30010	2023-07-30	450	Medicare patient, coordination applied.
PAT00011	paid	140	true	Kaiser Permanente	Ava	Brown	1972-10-21	female	ICOV10011	KP-82239918	GRP-6152	HMO	2022-06-01	blank	1	false	false	1	2023-01-17	CLM-30011	2023-01-15	180	Accident claim, subrogation process started.
PAT00012	paid	65	false	Cigna	Nathan	Patel	1992-02-10	male	ICOV10012	CIG-90081227	GRP-2148	PPO	2020-05-01	2023-05-31	2	false	false	2	2023-05-26	CLM-30012	2023-05-24	120	Secondary PPO, coverage ending soon.
PAT00013	paid	120	false	Aetna	Isabella	Wilson	1987-08-24	female	ICOV10013	AET-11022819	GRP-4499	Commercial	2022-02-01	blank	1	false	false	1	2023-04-20	CLM-30013	2023-04-19	150	Routine primary claim.
PAT00014	paid	210	false	Medicare	Elijah	Kim	1949-05-09	male	ICOV10014	MCR-71183245	blank	Medicare	2014-11-01	blank	1	false	true	1	2023-02-10	CLM-30014	2023-02-09	290	Medicare coordination for claim.
PAT00015	paid	45	false	Blue Shield	Chloe	Rodriguez	2006-10-30	female	ICOV10015	BS-92018321	GRP-1295	PPO	2023-02-01	blank	2	true	false	2	2023-08-04	CLM-30015	2023-08-03	95	Birthday rule, secondary coverage.
PAT00016	paid	180	false	UnitedHealthcare	Lucas	Walker	1976-02-14	male	ICOV10016	UHC-22018231	GRP-6632	HMO	2021-07-01	blank	1	false	false	1	2023-09-16	CLM-30016	2023-09-14	250	Routine primary HMO claim.
PAT00017	paid	95	false	Aetna	Grace	Allen	1984-12-15	female	ICOV10017	AET-00563218	GRP-1542	Commercial	2020-03-01	blank	2	false	false	2	2023-10-10	CLM-30017	2023-10-08	200	Secondary commercial, routine.
PAT00018	adjusted	115	true	Cigna	Jack	Hernandez	2002-07-21	male	ICOV10018	CIG-88712316	GRP-1599	PPO	2018-06-01	blank	1	false	false	1	2023-07-15	CLM-30018	2023-07-14	220	Accident claim, payment adjusted.
PAT00019	adjusted	75	false	Blue Cross	Emily	Young	1990-03-19	female	ICOV10019	BC-11029387	GRP-6572	PPO	2022-01-01	blank	2	false	false	2	2023-09-20	CLM-30019	2023-09-18	150	Secondary payment, claim adjusted.
PAT00020	paid	160	false	Humana	Matthew	Diaz	1974-06-14	male	ICOV10020	HUM-71293422	GRP-8829	HMO	2020-12-01	blank	1	false	false	1	2023-12-03	CLM-30020	2023-12-02	200	Routine primary claim.

What the 100 rows show

from the 100-row sample

Adjusted (claim status) stands out: 9 of its 11 rows have subrogation_flag = true, against 6 of 89 for the rest.

15%subrogation_flag = true
185.0median payment_amount
4patient genders
4insurance plan types
250.0median claim_total_amount

Subrogation flag rate by claim_statussubrogation_flag = true

payment_amount100 rows, in bands of 100

Median 185.0, from 45.0 to 995.0.

insurance_company_name100 rows · top 10 of 12 values

Medicare18
Cigna14
UnitedHealthcare11
Aetna10
Humana10
Blue Cross7
Blue Cross CA7
Anthem7
Aetna TX6
Blue Shield CA5

24 columns by typefrom the column list below

string 12
integer 2
float 2
date 5
boolean 3

Columns

24 columns in four groups

blueprint · 24 columns
column	type	description	example
Text 12 columns
`patient_id`	string	Unique identifier for the patientunique	PAT00001
`patient_first_name`	string	Patient's first name	James
`patient_last_name`	string	Patient's last name	Smith
`patient_gender`	string	Patient's gendermale · female · other · unknown · optional	male
`insurance_coverage_id`	string	Unique identifier for the insurance coverage recordunique	ICOV10001
`insurance_company_name`	string	Name of the insurance company12 names	Blue Shield
`insurance_policy_number`	string	Policy number for the insurance coverage	BS-73489922
`insurance_group_number`	string	Group number for the insurance policy, if applicableoptional	GRP-5138
`insurance_plan_type`	string	Type of insurance plan (e.g., PPO, HMO, Medicare, Medicaid, Commercial)6 values	PPO
`claim_id`	string	Unique identifier for the claim associated with this coverage	CLM-30001
`claim_status`	string	Status of the claim (e.g., pending, paid, denied, adjusted)pending · paid · denied · adjusted	paid
`coordination_notes`	string	Free-text notes on COB processing, exceptions, or special handlingoptional	Routine primary claim.
Numbers 4 columns
`coverage_priority`	integer	Order of coverage (1=Primary, 2=Secondary, etc.)1 or more	1
`payment_sequence_number`	integer	Sequence number indicating the order in which payments are processed for this coverage1 or more	1
`payment_amount`	float	Amount paid by this coverage for the claim0 or more · optional	350.75
`claim_total_amount`	float	Total amount billed for the claim0 or more	470
Dates and times 5 columns
`patient_date_of_birth`	date	Patient's date of birth	1965-05-14
`insurance_effective_date`	date	Start date of insurance coverage	2021-01-01
`insurance_termination_date`	date	End date of insurance coverage (if applicable)optional	2023-05-31
`payment_date`	date	Date of payment by this coverageoptional	2023-06-12
`claim_service_date`	date	Date of service for the claim	2023-06-10
True or false 3 columns
`birthday_rule_applied`	boolean	Indicates if the birthday rule was used to determine primary coverage	false
`medicare_coordination_applied`	boolean	Indicates if Medicare coordination rules were applied	false
`subrogation_flag`	boolean	Indicates if subrogation applies to this coverage (e.g., accident, liability)	false

Use it for

A healthcare dashboard
The subrogation_flag rate, payment_amount by claim_status and a breakdown of insurance_company_name. Excel, Power BI or Tableau.
Why do 15 of 100 rows have subrogation_flag = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Patients100
PAT00001350.75paid
PAT00002150paid
PAT00011140paid
A software demo
Believable patients with patient_first_name, patient_last_name and patient_date_of_birth to fill a screen in front of a buyer.

This dataset100 rows24 columns

Yours10,000 rows24 columns

blueprint · coordination-of-benefits-cob-and-dual-coverage

Behind this dataset

Same schema. As many rows as you need.

These 100 rows came out of a blueprint — 24 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

Primary payer determination rules
Birthday rule for dependent children (parent with earlier birthday is primary)
Active vs retired employee coverage
Medicare Secondary Payer (MSP) questionnaire
Workers compensation as primary for job injuries
Auto insurance primary for auto accidents (medical payments)
Liability third-party payer for injuries
Subrogation claims for recovery from third parties

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

Open the blueprint in Data Factory

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Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: coordination-of-benefits-cob-and-dual-coverage

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?