xpertsystems/hlt008-sample

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.