Datasets:

xpertsystems
/

hlt008-sample

license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - tabular-regression
language:
  - en
tags:
  - synthetic
  - healthcare
  - claims
  - insurance-claims
  - icd-10
  - cpt
  - hcpcs
  - drg
  - mdc
  - ndc
  - rxnorm
  - atc
  - hedis
  - hcc
  - ccw
  - x12-837
  - x12-835
  - carc
  - fraud-detection
  - healthcare-fraud
  - nhcaa
  - adherence
  - pdc
  - mpr
  - payer-mix
  - medical-loss-ratio
  - medicare-advantage
  - medicaid
  - commercial-insurance
  - claims-adjudication
  - prior-authorization
  - denial-management
  - value-based-care
pretty_name: HLT-008 Synthetic Healthcare Claims Dataset (Sample Preview)
size_categories:
  - 10K<n<100K

HLT-008 — Synthetic Healthcare Claims Dataset (Sample Preview)

A free, schema-identical preview of the full HLT-008 commercial product from XpertSystems.ai.

A fully synthetic healthcare claims dataset spanning members → providers → medical claims → pharmacy claims → adherence — modeling commercial, Medicare Advantage, Medicaid, and self-insured payer populations with X12 837/835-compliant claim structure, CMS HCC risk adjustment, CMS CCW chronic conditions, HEDIS quality measures, X12 CARC denial codes, NHCAA-aligned fraud patterns, and Pharmacy Quality Alliance PDC/MPR adherence metrics.

⚠️ PRIVACY & SYNTHETIC NATURE Every record in this dataset is 100% synthetic. No real claim records, no PHI, no real member identifiers, no real NPIs. Population-level distributions match published CMS / NHIS / NHCAA / Pharmacy Quality Alliance benchmark sources but the claims are computationally generated.

What's in this sample

File	Rows	Cols	Description
`members.csv`	500	44	Member master — demographics, payer/plan type, HCC risk score, 21+ CCW chronic conditions, dual-eligible/LIS flags, enrollment window
`providers.csv`	150	7	Provider directory — NPI, specialty, NUCC taxonomy, network status
`medical_claims.csv`	~12,800	54	Medical claim lines — ICD-10-CM (primary + 4 secondary), CPT/HCPCS, MS-DRG/MDC, modifiers, denial codes, HEDIS measure flags, fraud labels
`pharmacy_claims.csv`	~18,300	35	Pharmacy claim lines — NDC-11, RxNorm therapeutic class, ATC code, BIN/PCN, formulary tier, AWP/NADAC pricing, DIR fees
`adherence.csv`	~10,600	7	PDC + MPR by member × therapeutic class (Pharmacy Quality Alliance methodology)

Total: ~8.1 MB across 6 files. Note: This is the largest healthcare sample in the catalog because claims data has natural fan-out (500 members → 30K+ claims over 3 years).

Schema highlights

`members.csv` (44 columns)

Identity & demographics: member_id, age, age_band, sex, race_ethnicity, state, zip_code

Insurance: payer_type (commercial / medicare_advantage / medicaid / self_insured), plan_type (PPO / HMO / EPO / HDHP-HSA / SNP / PFFS / MCO / PCCM / FFS), dual_eligible_flag, lis_flag (Low Income Subsidy)

Risk & quality: hcc_risk_score (CMS HCC v28, mean-normalized to 1.0), n_chronic_conditions, care_management_flag, income_band (FPL-based)

Enrollment: enrollment_start, enrollment_end

21+ CCW chronic conditions (binary flags): ccw_ami, ccw_alzheimer, ccw_anemia, ccw_asthma, ccw_atrialfib, ccw_cataract, ccw_chrnkidn, ccw_copd, ccw_chf, ccw_diabetes, ccw_deprssion, ccw_hyperl, ccw_hyperp, ccw_ihd, ccw_mo_diabetes, ccw_osteoprs, ccw_ra_oa, ccw_stroke_tia, ccw_cancer_colorectal, ccw_cancer_endometrial, ccw_cancer_lung, ccw_cancer_prostate, ccw_cancer_breast, ccw_glaucoma, ccw_hip_fracture, ccw_hipvteib, ccw_bnign_prostate

`medical_claims.csv` (54 columns)

Claim identity: claim_id, member_id, claim_type, service_date_from, service_date_to, adjudication_date, plan_id

Provider attribution: rendering_npi, billing_npi, provider_specialty, network_status

Diagnosis coding: primary_icd10_cm (one of 50 CMS-calibrated codes including I10, E11.9, J06.9, M54.5, etc.), plus 4 secondary diagnoses (dx2 through dx5)

Procedure coding: cpt_code (E&M / Surgery / Radiology / Pathology-Lab / Medicine), cpt_category, modifier1, modifier2, revenue_code, service_units

Inpatient detail: drg_code (MS-DRG), drg_type, mdc_code (Major Diagnostic Category), length_of_stay, poa_flag (Present-on-Admission)

Place of service: place_of_service (CMS POS codes), pos_description

Financials: billed_amount, allowed_amount, paid_amount, member_deductible, member_copay, member_coinsurance, member_oop (out-of-pocket), cob_amount (coordination of benefits)

Adjudication: claim_status (Paid / Denied / Adjusted / Pended), denial_code_carc (CO-15, CO-4, CO-11, CO-18, PR-1, etc.), denial_reason_desc, auth_required_flag, auth_number

Quality/Safety flags: er_flag, preventive_flag, elective_flag, high_cost_flag, readmission_flag_30d

Fraud labels: fraud_label (5% prevalence), fraud_pattern_type ∈ {Upcoding, Phantom, Unbundling, Duplicate, Identity_Theft}

HEDIS quality measures: hedis_bcs (Breast Cancer Screening), hedis_col (Colorectal), hedis_cdc_a1c (Diabetes A1c), hedis_awv (Annual Wellness Visit), hedis_depression

`pharmacy_claims.csv` (35 columns)

Claim identity: rx_claim_id, member_id, fill_date, paid_date, pharmacy_npi, prescriber_npi, plan_id, bin_number, pcn_code

Drug coding: ndc_11 (National Drug Code), drug_name_generic, drug_name_brand, therapeutic_class (RxNorm), atc_code (Anatomical Therapeutic Classification, WHO)

Pricing: ingredient_cost, dispensing_fee, gross_amount_due, copay_amount, plan_paid, dir_fee_amount (Direct/Indirect Remuneration), awp_per_unit (Average Wholesale Price), nadac_per_unit (CMS National Average Drug Acquisition Cost)

Dispensing: formulary_tier, days_supply, quantity_dispensed, refill_number, pharmacy_type, dispense_as_written_code, specialty_rx_flag, compounded_flag, controlled_substance_flag

Anomaly flags: early_refill_flag, fraud_label_rx, diversion_flag

`adherence.csv` (7 columns)

member_id, therapeutic_class, total_days_supply, n_fills, pdc (Proportion of Days Covered), mpr (Medication Possession Ratio), adherence_flag_pdc80 (PDC ≥ 80% threshold per PQA Star Ratings methodology)

Calibration source story

The full HLT-008 generator anchors all distributions to authoritative healthcare claims references:

CMS HCC v28 — Hierarchical Condition Categories risk adjustment methodology
CMS Payer Enrollment Statistics — Commercial / MA / Medicaid / self-insured mix
CMS CPT/HCPCS Category Weights — E&M / Surgery / Radiology / Lab / Medicine
NHCAA (National Health Care Anti-Fraud Association) — Healthcare fraud rate estimates and 5-pattern taxonomy
NHIS / CDC — Adult chronic disease prevalence
CMS CCW (Chronic Conditions Warehouse) — 21-condition framework
X12 835 (HIPAA EDI) — CARC denial codes and adjudication structure
HEDIS 2024 (NCQA) — Healthcare Effectiveness Data and Information Set quality measures
Pharmacy Quality Alliance (PQA) — PDC/MPR adherence methodology, 80% threshold
CMS NDC + FDA Orange Book — National Drug Code coding
WHO ATC — Anatomical Therapeutic Chemical Classification
Lloyd & Lloyd (2016) MLR Analysis — Medical Loss Ratio benchmarks

Sample-scale validation scorecard

Metric	Observed	Target	Tolerance	Status	Source
Payer commercial share	44.2%	45%	±6%	✅ PASS	CMS payer enrollment
HCC risk score mean	1.00	1.00	±0.05	✅ PASS	CMS HCC v28 normalization
Fraud rate (medical)	5.04%	5%	±2%	✅ PASS	NHCAA
Denial rate	10.0%	10%	±3%	✅ PASS	X12 835 / CARC
Diabetes prevalence	10.0%	11%	±4%	✅ PASS	NHIS / CDC
CPT E&M share	30.1%	30%	±5%	✅ PASS	CMS CPT category weights
CCW condition diversity	27	≥21	—	✅ PASS	CMS CCW
Fraud pattern diversity	5	5	—	✅ PASS	NHCAA taxonomy
CARC denial code coverage	10	≥5	—	✅ PASS	X12 835
Claim date validity	99.7%	100%	±1%	✅ PASS	Data hygiene

Grade: A+ (100/100) — verified across 6 random seeds (42, 7, 123, 2024, 99, 1).

Loading examples

Pandas

import pandas as pd

members = pd.read_csv("members.csv")
providers = pd.read_csv("providers.csv")
medical = pd.read_csv("medical_claims.csv")
pharm = pd.read_csv("pharmacy_claims.csv")
adh = pd.read_csv("adherence.csv")

# Payer mix
print(members["payer_type"].value_counts(normalize=True))

# Top ICD-10 codes in medical claims
print(medical["primary_icd10_cm"].value_counts().head(10))

# Fraud pattern breakdown
print(medical.loc[medical["fraud_label"] == 1, "fraud_pattern_type"]
        .value_counts())

# Denial reasons
print(medical.loc[medical["claim_status"] == "Denied", "denial_code_carc"]
        .value_counts().head(10))

Hugging Face Datasets

from datasets import load_dataset

ds = load_dataset("xpertsystems/hlt008-sample", data_files={
    "members":         "members.csv",
    "providers":       "providers.csv",
    "medical_claims":  "medical_claims.csv",
    "pharmacy_claims": "pharmacy_claims.csv",
    "adherence":       "adherence.csv",
})
print(ds)

Fraud detection baseline

import pandas as pd
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

medical = pd.read_csv("medical_claims.csv")

features = ["billed_amount", "allowed_amount", "paid_amount", "service_units",
            "length_of_stay", "auth_required_flag", "er_flag", "high_cost_flag",
            "readmission_flag_30d"]
# Encode categorical
medical["cpt_cat_enc"] = pd.factorize(medical["cpt_category"])[0]
medical["network_enc"] = pd.factorize(medical["network_status"])[0]
features += ["cpt_cat_enc", "network_enc"]

X, y = medical[features].fillna(0), medical["fraud_label"]
Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.25, stratify=y,
                                       random_state=42)
clf = GradientBoostingClassifier(random_state=42).fit(Xtr, ytr)
print(classification_report(yte, clf.predict(Xte)))

HCC risk-adjusted spend analysis

import pandas as pd

members = pd.read_csv("members.csv")
medical = pd.read_csv("medical_claims.csv")

# Per-member medical spend
spend = medical.groupby("member_id")["paid_amount"].sum().rename("total_paid")
m = members.merge(spend, on="member_id", how="left")
m["total_paid"] = m["total_paid"].fillna(0)

# Spend by HCC decile
m["hcc_decile"] = pd.qcut(m["hcc_risk_score"], 10, labels=False)
print(m.groupby("hcc_decile")["total_paid"].agg(["mean", "std", "median"]))

Adherence intervention targeting

import pandas as pd

adh = pd.read_csv("adherence.csv")

# Low-adherence patients (PDC < 0.80) by therapeutic class
low_adh = adh[adh["adherence_flag_pdc80"] == 0]
print(low_adh["therapeutic_class"].value_counts().head(10))

Suggested use cases

Claims fraud detection — train binary or multi-class classifiers on fraud_label + fraud_pattern_type with features from medical + pharmacy + provider tables
Denial management — predict claim_status (Paid/Denied/Adjusted/Pended) and denial_code_carc from claim features
HCC risk adjustment — train risk score predictors from diagnosis codes for value-based contracts
HEDIS gap analysis — identify members not meeting hedis_* measures, predict who needs outreach
High-cost claimant identification — predict high_cost_flag or top-decile spend from baseline features
Adherence intervention modeling — predict PDC < 0.8 in chronic medication users
Drug switching / brand-vs-generic analysis — RxNorm therapeutic class transitions
Provider network optimization — analyze in-network vs out-of-network financial impact
Prior authorization optimization — predict which auth_required_flag claims will be denied
Care management targeting — identify members for case management based on chronic conditions + spend
30-day readmission prediction — readmission_flag_30d ML
X12 837/835 ETL pipeline testing — schema-compliant synthetic data for EDI pipelines
Healthcare analytics platform development — synthetic data for warehousing, reporting, BI demos

Sample vs. full product

Aspect	This sample	Full HLT-008 product
Members	500	100,000+ (default) up to 5M
Years	3 (2021-2023)	Configurable, multi-year longitudinal
Providers	150	5,000+
Schema	identical	identical
Calibration	identical	identical
License	CC-BY-NC-4.0	Commercial license

The full product unlocks:

Up to 5M members for population-scale fraud detection and risk adjustment training
Configurable multi-year longitudinal windows for spend trend analysis
Larger provider network (5,000+) for realistic network analysis
Commercial use rights

Contact us for the full product.

Limitations & honest disclosures

Sample is preview-only. 500 members × 3 years × ~30K claims is enough to demonstrate schema and calibration, but is not statistically sufficient for serious fraud detection model training (would need ≥100K members for reliable detection of rare fraud patterns) or rare condition analysis. Use the full product for serious work.
Sample is on the larger side (8 MB). Claims data has natural fan-out — even at 500 members, you get ~30K claim records. This is the largest healthcare sample in the catalog. The full product scales linearly with member count.
Adherence PDC denominator is the full observation window, not actual therapy initiation. The generator computes PDC as total_days_supply / 1096_days (3-year observation window), rather than the clinically-canonical "days from first fill to obs end." This produces lower PDC values (~0.1) than the typical 0.7-0.8 reported in real PDC analyses. The field is structurally correct (between 0 and 1, deterministic), just calibrated against the observation window. For clinically-typical PDC values, compute it as total_days_supply / (obs_end - first_fill_date) from the raw fills, which is a one-line post-processing step.
Fraud labels are statistically assigned, not adjudicated. fraud_label = 1 flags follow a 5% Bernoulli draw with pattern types assigned by category-rule mapping. They represent realistic fraud taxonomy proportions but are NOT validated against real fraud detection adjudication.
ICD-10 coding uses 50 most common codes, not the full ~70K codeset. Realistic for general analytics but not exhaustive — rare-disease analysis requires the full HLT-008 product with extended code coverage.
NDC codes are placeholder 11-digit strings, not real FDA Orange Book entries. drug_name_generic / drug_name_brand / therapeutic_class / atc_code are populated; the NDC-11 string itself is synthetic. Use therapeutic class + ATC for drug-level analysis.
NPIs are synthetic 10-digit strings. Provider directory has realistic specialty + NUCC taxonomy + network status but the NPI numbers themselves are not real CMS NPPES numbers.
State distribution focuses on top-10 US states. Member distribution is concentrated in CA/TX/FL/NY/PA/IL/OH/GA/NC/MI; all 50 states are not represented at uniform frequency.
No real CMS BSA / DRG payment rates. paid_amount is calibrated to overall paid-to-billed ratios (~0.63), not specific DRG reimbursement schedules. The full product can be tuned to specific year IPPS/OPPS rates.
Synthetic, not derived from real claims data. Distributions match published CMS / NHIS / NHCAA references but do NOT reflect any specific real payer or member cohort.

Ethical use guidance

This dataset is designed for:

Healthcare fraud detection ML methodology development
Claims analytics platform development
HCC risk adjustment model research
HEDIS quality measure pipeline testing
X12 837/835 EDI ETL pipeline development
Educational use in health services research and actuarial science
Healthcare AI pretraining for claims-based prediction tasks

This dataset is not appropriate for:

Making payment decisions about real claims
Insurance underwriting, pricing, or claim adjudication for real members
Fraud accusations against real providers
Discriminatory analyses targeting protected demographic groups
Training models that produce real claim decisions without separate validation on real data

Companion datasets in the Healthcare vertical

HLT-001 — Synthetic Patient Population (5K patients × 79 cols, CDC/NHANES calibrated)
HLT-002 — Synthetic EHR Dataset (4K encounters + FHIR R4 bundles)
HLT-003 — Synthetic Clinical Trial Dataset (3 endpoint types + power sweep)
HLT-004 — Synthetic Disease Progression Dataset (NSCLC + Heart Failure longitudinal)
HLT-005 — Synthetic Hospital Admission Dataset (5K admissions + bed utilization)
HLT-006 — Synthetic Medical Imaging Dataset (1K studies + COCO annotations + reports)
HLT-007 — Synthetic Drug Response Dataset (3K patient-treatments × 25 drug classes + PGx + PK)
HLT-008 — Synthetic Healthcare Claims Dataset (you are here)

Use HLT-001 through HLT-008 together for the full healthcare ML data stack: population → EHR → trials → progression → hospital ops → imaging → pharmacology → claims & reimbursement.

Citation

If you use this dataset, please cite:

@dataset{xpertsystems_hlt008_sample_2026,
  author       = {XpertSystems.ai},
  title        = {HLT-008 Synthetic Healthcare Claims Dataset (Sample Preview)},
  year         = 2026,
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/xpertsystems/hlt008-sample}
}

Contact

Web: https://xpertsystems.ai
Email: pradeep@xpertsystems.ai
Full product catalog: Cybersecurity, Insurance & Risk, Materials & Energy, Oil & Gas, Healthcare, and more

Sample License: CC-BY-NC-4.0 (Creative Commons Attribution-NonCommercial 4.0) Full product License: Commercial — please contact for pricing.