Finance-Healthcare Cross-Domain Spending

This dataset provides a unified view of individual healthcare-related spending, linking medical, pharmacy, insurance, and wellness expenses with financial transaction details. It enables comprehensive analysis of out-of-pocket costs, insurance coverage, and spending patterns for holistic wellness and financial planning. The flat, detailed structure supports integration with demographic data and advanced analytics for both healthcare and financial services.

  • last updated 29 Jul 2025
  • by GoMask
The brief that made it

Personalized financial planning and wellness budgeting

Sample rows

preview · 8 of 200 rows · all 19 columns
spend_idstringhealthcare_service_typestringinsurance_amountfloatinsurance_coveredbooleanlocation_citystringperson_idstringtransaction_datedatespend_amountfloatcurrencystringspend_categorystringmerchant_namestringmerchant_typestringpayment_methodstringout_of_pocket_amountfloatlocation_street_addressstringlocation_statestringlocation_postal_codestringlocation_countrystringnotesstring
SPN-00001prescription0falseSan FranciscoUSR-10012024-01-1589.99USDpharmacyCVS Pharmacy Market Stpharmacycredit_card89.99123 Market StCA94103USMonthly prescription refill
SPN-00002supplement0falseNew YorkUSR-10022023-12-28145.75USDnutritionWhole Foods Union Sqgrocerydebit_card145.75789 BroadwayNY10003USSupplements and vitamins purchase
SPN-00003membership0falseSan FranciscoUSR-10032023-11-0745USDfitness24 Hour Fitnessgymcredit_card452100 Van Ness AveCA94109USMonthly gym membership fee
SPN-00004therapy0falseAustinUSR-10042024-03-20328.5USDwellnessCalm Spaotherdebit_card328.5400 Wellness BlvdTX78701USTherapy session for stress relief
SPN-00005procedure8000trueSan FranciscoUSR-10052022-09-149740USDmedicalUCSF Medical Centerhospitalinsurance_claim1740505 Parnassus AveCA94143USMinor outpatient surgery partially covered
SPN-00006supplement0falseSeattleUSR-10012022-12-0625.99USDnutritionAmazon.comonlinecredit_card25.99410 Terry Ave NWA98109USMonthly vitamin D supplement
SPN-00007membership0falseSan FranciscoUSR-10062024-02-25125USDinsuranceBlue Shield Insuranceinsurancebank_transfer125800 Howard StCA94103USMonthly insurance premium
SPN-00008prescription140trueSan FranciscoUSR-10072022-08-17189USDpharmacyWalgreens Castropharmacycredit_card492199 Market StCA94114USAntibiotic prescription fill

What the 200 rows show

from the 200-row sample

Procedure (healthcare service type) stands out: mean insurance_amount is 10,111, against 34.8 for the rest.

  • 34%insurance_covered = true
  • 0.0median insurance_amount
  • 6payment methods
  • 7spend categories
  • 7merchant types
  • 9location countries
Mean insurance_amount by healthcare_service_type200 rows
010k20k306.1cons…5 rows32.4pres…39 ro…10,111proc…32 ro…47.7memb…43 ro…0.0supp…37 ro…47.9ther…21 ro…0.0other23 ro…
insurance_amount200 rows, in bands of 5,000
09018017613620102020k40kinsurance_amount →

Median 0.0, from 0.0 to 40,000.

location_city200 rows · top 10 of 30 values
  1. San Francisco38
  2. Austin37
  3. Seattle24
  4. New York23
  5. Los Angeles8
  6. Toronto7
  7. Singapore7
  8. London6
  9. Berlin6
  10. Tokyo5
19 columns by typefrom the column list below
  • string 14
  • float 3
  • date 1
  • boolean 1

Columns

19 columns in four groups
blueprint · 19 columns
columntypedescriptionexample
Text 14 columns
spend_idstringUnique identifier for each aggregated spend recorduniqueSPN-00001
person_idstringUnique identifier for the individual associated with the spend recordUSR-1001
currencystringISO 4217 currency code for the transaction (e.g., USD, EUR)10 currenciesUSD
spend_categorystringCategory of spend (e.g., medical, pharmacy, insurance, wellness, fitness, nutrition, other)7 valuespharmacy
merchant_namestringName of the merchant, provider, or institution where the spend occurredoptionalCVS Pharmacy Market St
merchant_typestringType of merchant or provider (e.g., hospital, pharmacy, gym, insurance, grocery, online, other)7 values · optionalpharmacy
payment_methodstringMethod of payment used (e.g., credit_card, debit_card, bank_transfer, cash, insurance_claim, other)6 values · optionalcredit_card
healthcare_service_typestringType of healthcare service or product (e.g., consultation, prescription, procedure, membership, supplement, therapy, other)7 values · optionalprescription
location_street_addressstringStreet address of the merchant or provider locationoptional123 Market St
location_citystringCity of the merchant or provider locationoptionalSan Francisco
location_statestringState or region of the merchant or provider locationoptionalCA
location_postal_codestringPostal code of the merchant or provider locationoptional94103
location_countrystringCountry of the merchant or provider location (ISO 3166-1 alpha-2 code)9 countries · optionalUS
notesstringOptional notes or comments about the spend recordoptionalAsthma inhaler refill
Numbers 3 columns
spend_amountfloatTotal amount spent in the transaction or health expense0 or more89.99
insurance_amountfloatAmount covered by insurance for this spend (0 if not covered)0 or more · optional0
out_of_pocket_amountfloatAmount paid out-of-pocket by the individual (spend_amount - insurance_amount)0 or more · optional89.99
Dates and times 1 column
transaction_datedateDate when the transaction or health expense occurred2024-01-15
True or false 1 column
insurance_coveredbooleanIndicates if the spend was partially or fully covered by insuranceoptionalfalse

Use it for

  • insurance cove…34%68 of 200 rowsmean insurance amount…306.1cons…32.4pres…10.1kproc…47.7memb…

    A healthcare dashboard

    The insurance_covered rate, insurance_amount by healthcare_service_type and a breakdown of location_city. Excel, Power BI or Tableau.

  • Why do the 32 procedure rows have a mean insurance_amount of 10,111?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable spends with person_id, transaction_date and spend_amount to fill a screen in front of a buyer.

Not quite right?

Make it yours.

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This dataset200 rows19 columns
Yours1,000 rows19 columnslocation_street_address: UK only

blueprint · finance-healthcare-cross-domain-spending

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 19 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
  • Spend events linked to healthcare categories
  • Recurring payments flagged for insurance
  • Out-of-pocket vs covered costs labeled
  • Demographic features anonymized
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
finance-healthcare-cross-domain-spending

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