Health Economics and Outcomes Research
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Registry-driven view of health-economics, reimbursement, outcomes-research, and related evidence datasets. • 1 item • Updated
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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 7 new columns ({'ready_for_prototype', 'analysis_id', 'missing_sources', 'bottleneck_source_score', 'required_source_count', 'missing_source_count', 'mean_source_score'}) and 7 missing columns ({'methods', 'required_sources', 'purpose', 'id', 'policy_insight', 'primary_output', 'caveats'}).
This happened while the csv dataset builder was generating data using
hf://datasets/edithatogo/reimbursement-atlas/data/seed/analysis_readiness.csv (at revision a2b3682b1fd4dc5910a154c15abdc6e9c4199442), ['hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/analysis_catalogue.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/analysis_readiness.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/analysis_recipes.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/conductor_tracks.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/dataset_candidates.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/first_wave_ingestion_plan.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/graph_edges.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/graph_nodes.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/ingestion_readiness.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/jurisdictions.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/mapping_resources.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/ontology_concepts.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/ontology_mapping_templates.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/ontology_registry.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/output_artifact_plans.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/research_questions.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/roadmap_functions.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/runtime_targets.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/source_acquisition_plan.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/source_files.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/source_readiness.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/source_registry.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/source_snapshots.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/source_status.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/source_versions.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
analysis_id: string
bottleneck_source_score: int64
difficulty: string
mean_source_score: double
missing_source_count: int64
missing_sources: double
ready_for_prototype: bool
required_source_count: int64
stage: string
title: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1558
to
{'caveats': Value('string'), 'difficulty': Value('string'), 'id': Value('string'), 'methods': Value('string'), 'policy_insight': Value('string'), 'primary_output': Value('string'), 'purpose': Value('string'), 'required_sources': Value('string'), 'stage': Value('string'), 'title': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 7 new columns ({'ready_for_prototype', 'analysis_id', 'missing_sources', 'bottleneck_source_score', 'required_source_count', 'missing_source_count', 'mean_source_score'}) and 7 missing columns ({'methods', 'required_sources', 'purpose', 'id', 'policy_insight', 'primary_output', 'caveats'}).
This happened while the csv dataset builder was generating data using
hf://datasets/edithatogo/reimbursement-atlas/data/seed/analysis_readiness.csv (at revision a2b3682b1fd4dc5910a154c15abdc6e9c4199442), ['hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/analysis_catalogue.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/analysis_readiness.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/analysis_recipes.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/conductor_tracks.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/dataset_candidates.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/first_wave_ingestion_plan.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/graph_edges.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/graph_nodes.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/ingestion_readiness.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/jurisdictions.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/mapping_resources.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/ontology_concepts.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/ontology_mapping_templates.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/ontology_registry.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/output_artifact_plans.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/research_questions.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/roadmap_functions.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/runtime_targets.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/source_acquisition_plan.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/source_files.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/source_readiness.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/source_registry.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/source_snapshots.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/source_status.csv', 'hf://datasets/edithatogo/reimbursement-atlas@a2b3682b1fd4dc5910a154c15abdc6e9c4199442/data/seed/source_versions.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
caveats string | difficulty string | id string | methods string | policy_insight string | primary_output string | purpose string | required_sources string | stage string | title string |
|---|---|---|---|---|---|---|---|---|---|
Coding and facility/professional bundle boundaries must be manually validated. | medium | cognitive_vs_procedural_ratio | ["basket construction", "PPP conversion", "robustness checks", "uncertainty scoring"] | Identifies whether schedules structurally privilege procedures over diagnostic reasoning, chronic care, counselling and coordination. | Jurisdiction-level index and service-pair basket table. | Compare reimbursement relativities for consultative/cognitive work against procedural/interventional work. | ["au_mbs", "us_cms_pfs", "ca_on_ohip", "de_ebm", "jp_mhlw_fee", "tw_nhi_services"] | design | Cognitive versus procedural reward index |
CPT/MBS/test-directory mapping requires clinical review and LOINC/HPO support. | medium | genomics_coverage_price_diffusion | ["event study", "interrupted time series", "graph mapping", "text classification"] | Shows whether public coverage produces actual uptake and whether restrictions create bottlenecks. | Genomic test graph, coverage chronology and utilisation panel. | Map genomic test eligibility, price/payment, coverage criteria and utilisation trajectories. | ["au_mbs", "au_msac", "us_cms_clfs", "us_cms_mcd", "uk_genomic_test_directory"] | design | Genomics coverage, price and diffusion atlas |
Net prices are often unavailable; output should be labelled as transparency, not exact net price. | medium | published_vs_effective_price_opacity | ["document review", "ordinal scoring", "sensitivity analysis"] | Highlights hidden rebates, confidential deeds, out-of-pocket gaps and negotiated plan/hospital prices. | Opacity index and evidence table. | Score each schedule on how far public prices are from likely effective payer cost. | ["au_pbs", "us_cms_asp", "uk_nhs_drug_tariff", "nz_pharmac", "jp_nhi_drug_prices", "ch_specialities_list", "se_tlv"] | design | Published versus effective price opacity score |
Can become descriptive unless linked to case studies. | low | coverage_decision_architecture | ["qualitative coding", "process mining", "decision log extraction"] | Clarifies institutional design choices: national vs local discretion, explicit HTA, evidence thresholds and appeals. | Process maps and decision taxonomy. | Compare how systems separate evidence assessment, coverage, price setting, restrictions and utilisation management. | ["au_msac", "au_pbac", "us_cms_mcd", "uk_genomic_test_directory", "fr_haspub"] | design | Coverage decision architecture comparison |
Needs careful assumptions and sometimes non-public plan/provider pricing. | high | patient_cost_exposure | ["microsimulation", "scenario modelling", "sensitivity analysis"] | Shows whether public reimbursement actually protects patients from out-of-pocket exposure. | Cost exposure profiles and simulated patient vignettes. | Compare copayment, coinsurance, balance billing, bulk billing and safety-net rules for matched service baskets. | ["au_mbs", "au_pbs", "us_cms_pfs", "us_cms_asp", "uk_nhs_payment_scheme"] | design | Patient cost exposure and gap risk |
US and Canada are more subnational; Australia variation is more provider supply than schedule rules. | medium | local_discretion_postcode_lottery | ["hierarchical modelling", "coverage text comparison", "geospatial joins"] | Distinguishes responsiveness from inequity: local coverage can speed innovation or create access variation. | Variation dashboard and maps. | Measure geographic coverage/payment variation within systems. | ["us_cms_mcd", "us_medicaid_state_fees", "ca_on_ohip", "ca_bc_msp", "ca_ab_somb", "au_mbs"] | design | Local discretion and postcode-lottery index |
DRG systems are not one-to-one; often needs procedure-level vignettes. | high | hospital_professional_unbundling | ["case-mix harmonization", "component mapping", "DRG/APC crosswalk"] | Shows whether apparent cross-country price differences are driven by bundling rather than generosity. | Bundle decomposition schema and exemplar procedures. | Compare facility, professional, device, drug and pathology components across hospital-related interventions. | ["au_ihacpa_nep", "us_cms_ipps", "us_cms_opps", "uk_nhs_payment_scheme", "de_gdrg", "ch_swissdrg", "dk_drg", "se_norddrg"] | design | Hospital-professional unbundling map |
Regulatory approval dates may require additional data sources. | medium | innovation_time_to_reimbursement | ["survival analysis", "milestone extraction", "NLP-assisted date extraction"] | Reveals whether systems trade speed for certainty, price discipline or evidentiary rigor. | Timeline dataset and survival models. | Measure listing lag and coverage lag for high-cost drugs, genomic tests and devices. | ["au_pbs", "au_pbac", "au_msac", "us_cms_mcd", "uk_genomic_test_directory", "nz_pharmac", "fr_haspub"] | design | Time from evidence/approval to reimbursement |
Needs reliable utilisation time series and mapping families. | medium | substitution_and_volume_control | ["difference-in-differences", "synthetic control", "network flow"] | Helps distinguish productive innovation from additive low-value growth. | Utilisation panels and substitution networks. | Test whether new reimbursement items substitute for older items or expand total service volume. | ["au_mbs", "us_cms_pfs", "us_cms_clfs", "tw_nhi_services"] | design | Substitution and volume-control evaluation |
Restrictions may be embedded in PDFs and clinical forms. | medium | medicine_access_restriction_text | ["NLP", "rule extraction", "clinical review"] | Turns opaque listing text into auditable access constraints. | Restriction ontology and strictness score. | Convert reimbursement restrictions into structured criteria and compare strictness. | ["au_pbs", "uk_nhs_drug_tariff", "nz_pharmac", "jp_nhi_drug_prices", "ch_specialities_list", "se_tlv"] | design | Medicine restriction-text strictness index |
Some ontologies have licence restrictions and cannot be mirrored. | low | ontology_maturity_score | ["schema audit", "ontology coverage", "manual mapping sampling"] | Identifies data infrastructure gaps that limit comparative policy analysis. | Maturity matrix and mapping backlog. | Assess how well each schedule links to standard terminologies like LOINC, ATC, ICD, SNOMED CT, HPO and RxNorm. | ["au_mbs", "au_pbs", "us_cms_pfs", "us_cms_clfs", "uk_genomic_test_directory", "tw_nhi_services"] | design | Ontology and mapping maturity score |
A schedule can be transparent while effective net payment remains opaque. | low | schedule_transparency_benchmark | ["metadata scoring", "automated link checks", "schema validation"] | Creates a policy transparency index across public reimbursement systems. | Transparency leaderboard and reproducible registry. | Score public accessibility, machine readability, historical availability, versioning and provenance. | ["au_mbs", "au_pbs", "au_ihacpa_nep", "au_msac", "au_pbac", "us_cms_pfs", "us_cms_clfs", "us_cms_opps", "us_cms_ipps", "us_cms_asp", "us_cms_mcd", "us_medicaid_state_fees", "uk_nhs_payment_scheme", "uk_nhs_drug_tariff", "uk_genomic_test_directory", "ca_on_ohip", "ca_bc_msp", "ca_ab_somb", "nz_pharmac", "de_ebm", "de_gd... | design | Schedule transparency benchmark |
Requires current evidence and regulatory source expansion. | high | ai_diagnostics_reimbursement_readiness | ["taxonomy mapping", "scenario analysis", "regulatory comparison"] | Anticipates policy gaps before AI diagnostics diffuse. | Readiness framework and case studies. | Assess whether schedules can accommodate algorithmic diagnostics, software as a medical device and AI-supported testing. | ["au_msac", "us_cms_pfs", "us_cms_clfs", "us_cms_mcd", "uk_nhs_payment_scheme"] | design | AI diagnostics reimbursement readiness |
Payment loadings may not translate into service availability. | medium | equity_weighting_and_rural_loadings | ["rule extraction", "GIS joining", "scenario modelling"] | Shows where reimbursement corrects or compounds geographic access inequity. | Equity adjustment catalogue. | Map explicit rural, remote, deprivation or workforce-adjustment mechanisms. | ["au_mbs", "us_cms_pfs", "us_cms_ipps", "uk_nhs_payment_scheme", "ca_on_ohip", "no_helfo_tariffs"] | design | Equity weighting and rurality adjustments |
Price changes can reflect coding changes, bundling changes or policy shocks rather than pure revaluation. | medium | price_revision_velocity | ["version differencing", "calendar alignment", "inflation adjustment", "outlier detection"] | Identifies whether schedules actively manage inflation, technology maturity and budget pressure or allow legacy relativities to persist. | Versioned price-change panel and revision-timing dashboard. | Measure how often and how predictably schedules revise prices, weights and relative values. | ["au_mbs", "au_pbs", "us_cms_pfs", "us_cms_clfs", "jp_mhlw_fee", "dk_drg"] | design | Price revision velocity and indexation discipline |
Local discretion may be informal and operational rather than explicit in published schedules. | medium | local_vs_national_discretion | ["document coding", "graph modelling", "restriction extraction"] | Distinguishes responsive local adaptation from postcode-lottery risk and maps where national schedules still hide local access rules. | Coverage-discretion taxonomy and jurisdiction heatmap. | Compare whether coverage decisions are centralised nationally or delegated to local payers, contractors, regions or insurers. | ["us_cms_mcd", "us_medicaid_state_fees", "au_msac", "au_mbs", "nl_nza_dbc", "se_tlv"] | design | Local versus national coverage discretion map |
Confidential price agreements and compassionate-access pathways can be invisible. | high | rare_disease_reimbursement_pathways | ["case studies", "restriction coding", "decision chronology", "qualitative comparative analysis"] | Shows whether systems rely on HTA flexibility, managed entry, exceptional funding, local discretion or delayed access. | Rare-disease pathway typology and case-study matrix. | Map how high-uncertainty, low-volume technologies enter public reimbursement systems. | ["au_pbac", "au_msac", "us_cms_mcd", "us_cms_partd_puf", "uk_nice_guidance", "nz_pharmac", "it_aifa_reimbursement"] | design | Rare disease and ultra-orphan reimbursement pathways |
Hospital procurement discounts and private contracts may dominate effective prices. | high | device_and_prosthesis_visibility | ["basket construction", "bundling classification", "manual validation"] | Reveals where device policy is transparent enough for price comparison versus obscured by procurement and episode bundling. | Device visibility index and exemplar device basket. | Compare whether devices, prostheses and supplies are itemised, bundled or hidden inside facility payments. | ["us_cms_dmepos", "us_cms_opps", "au_ihacpa_nep", "fr_lpp", "nz_pharmac", "ch_swissdrg"] | design | Device and prosthesis price visibility comparison |
Temporary pandemic policies and payer-specific rules require careful date handling. | medium | telehealth_payment_architecture | ["item text mining", "basket matching", "policy timeline extraction"] | Shows whether virtual care is treated as equivalent care, a lower-cost substitute, or a tightly restricted exception. | Telehealth rule matrix and fee-parity ratio table. | Compare telehealth itemisation, parity, modality restrictions and post-pandemic persistence. | ["au_mbs", "us_cms_pfs", "ca_on_ohip", "uk_nhs_payment_scheme", "tw_nhi_services"] | design | Telehealth payment architecture and parity rules |
Capitation, quality payments and grants may sit outside item-level fee schedules. | medium | primary_care_longitudinal_incentives | ["taxonomy design", "basket pricing", "restriction coding"] | Indicates whether public payment systems encourage episodic visits or longitudinal population management. | Primary-care incentive typology and matched service basket. | Map how schedules pay for enrolment, chronic disease management, preventive care and care planning. | ["au_mbs", "us_cms_pfs", "uk_nhs_payment_scheme", "ca_bc_msp", "jp_mhlw_fee"] | design | Primary-care longitudinal incentive architecture |
Facility payment and professional payment are often not directly additive across systems. | high | hospital_outpatient_carveout | ["episode taxonomy", "facility/professional split coding", "case vignettes"] | Shows how bundling choices shape incentives for sites of care, device use and diagnostic ordering. | Bundling boundary map and cross-setting payment comparison. | Compare when services remain separately payable versus absorbed into outpatient or inpatient bundles. | ["us_cms_opps", "us_cms_asc", "us_cms_pfs", "au_ihacpa_nep", "uk_nhs_payment_scheme", "nl_nza_dbc"] | design | Hospital outpatient carve-out and bundling comparison |
Some terminologies are clinically essential but cannot be mirrored publicly. | low | terminology_coverage_completeness | ["dependency mapping", "licence classification", "gap analysis"] | Makes explicit where licensing or terminology access can block otherwise public reimbursement analysis. | Analysis-by-ontology dependency matrix and licence-risk register. | Identify which analyses require LOINC, RxNorm, ATC, ICD, SNOMED CT, HPO or other terminologies. | ["au_mbs", "au_pbs", "us_cms_clfs", "us_cms_asp", "uk_genomic_test_directory"] | prototype | Terminology coverage and ontology-dependency audit |
Listing dates, first-claim dates and source refresh dates may differ materially. | medium | coverage_to_utilisation_lag | ["event study", "changepoint detection", "interrupted time series"] | Separates nominal access from realised access and highlights implementation bottlenecks. | Event-study panel and diffusion-lag estimates. | Measure how long it takes for newly reimbursed services or medicines to diffuse after public listing. | ["au_mbs", "au_pbs", "au_aihw_mbs_pbs_stats", "us_cms_clfs", "us_cms_partd_puf", "uk_nhs_reference_costs"] | design | Coverage-to-utilisation lag after listing |
Scores reflect public accessibility, not necessarily health-system performance. | low | global_public_schedule_access_index | ["ordinal scoring", "metadata audit", "sensitivity analysis"] | Ranks where comparative reimbursement research is easiest, most reproducible and most licence-constrained. | Open reimbursement data maturity leaderboard. | Score each jurisdiction on machine-readability, licensing, historical versions, API access and utilisation linkage. | ["au_mbs", "us_cms_pfs", "tw_nhi_services", "br_sigtap", "za_upfs", "in_abpmjay_hbp", "oecd_health_stats"] | prototype | Global public schedule access and reproducibility index |
Rebates and confidential agreements limit interpretation of net prices. | high | medicine_substitution_and_price_ladders | ["ATC/RxNorm mapping", "PPP conversion", "price-index decomposition"] | Identifies where public policy accelerates price convergence and where published prices remain opaque. | Therapeutic-class price ladder and policy mechanism map. | Compare how systems group medicines, reward generics/biosimilars and revise prices after competition. | ["au_pbs", "us_cms_asp", "us_cms_partd_puf", "nz_pharmac", "se_tlv", "fi_kela_reimbursements", "jp_nhi_drug_prices"] | design | Medicine substitution, reference pricing and price ladder analysis |
null | high | null | null | null | null | null | null | design | Hospital-professional unbundling map |
null | high | null | null | null | null | null | null | design | Patient cost exposure and gap risk |
null | medium | null | null | null | null | null | null | design | Substitution and volume-control evaluation |
null | medium | null | null | null | null | null | null | design | Coverage-to-utilisation lag after listing |
null | high | null | null | null | null | null | null | design | Hospital outpatient carve-out and bundling comparison |
null | low | null | null | null | null | null | null | design | Ontology and mapping maturity score |
null | low | null | null | null | null | null | null | prototype | Terminology coverage and ontology-dependency audit |
null | high | null | null | null | null | null | null | design | Device and prosthesis price visibility comparison |
null | medium | null | null | null | null | null | null | design | Cognitive versus procedural reward index |
null | medium | null | null | null | null | null | null | design | Equity weighting and rurality adjustments |
null | medium | null | null | null | null | null | null | design | Medicine restriction-text strictness index |
null | high | null | null | null | null | null | null | design | Medicine substitution, reference pricing and price ladder analysis |
null | medium | null | null | null | null | null | null | design | Price revision velocity and indexation discipline |
null | medium | null | null | null | null | null | null | design | Published versus effective price opacity score |
null | medium | null | null | null | null | null | null | design | Telehealth payment architecture and parity rules |
null | high | null | null | null | null | null | null | design | AI diagnostics reimbursement readiness |
null | low | null | null | null | null | null | null | design | Coverage decision architecture comparison |
null | medium | null | null | null | null | null | null | design | Genomics coverage, price and diffusion atlas |
null | medium | null | null | null | null | null | null | design | Time from evidence/approval to reimbursement |
null | medium | null | null | null | null | null | null | design | Primary-care longitudinal incentive architecture |
null | high | null | null | null | null | null | null | design | Rare disease and ultra-orphan reimbursement pathways |
null | low | null | null | null | null | null | null | prototype | Global public schedule access and reproducibility index |
null | medium | null | null | null | null | null | null | design | Local discretion and postcode-lottery index |
null | medium | null | null | null | null | null | null | design | Local versus national coverage discretion map |
null | low | null | null | null | null | null | null | design | Schedule transparency benchmark |
Synthetic fixtures only until reviewed MBS/CMS/NHS source files are parsed. | null | recipe_genomics_price_coverage | null | null | null | null | null | null | null |
Requires careful professional/facility component separation and code-basket review. | null | recipe_cognitive_procedural_relativities | null | null | null | null | null | null | null |
Confidential rebates and managed-entry agreements limit direct effective-price comparison. | null | recipe_medicine_price_opacity | null | null | null | null | null | null | null |
Transparency scoring is a reproducible rubric, not a value judgement about policy quality. | null | recipe_source_transparency_atlas | null | null | null | null | null | null | null |
Requires source-specific interpretation of NCD/LCD, national schedule and regional commissioning concepts. | null | recipe_local_discretion | null | null | null | null | null | null | null |
null | null | track_runtime_mojo_python314 | null | null | null | null | null | null | Mojo-first runtime and Python 3.14 compatibility |
null | null | track_live_source_ingestion | null | null | null | null | null | null | Evidence-grade live source ingestion |
null | null | track_research_protocols_osf | null | null | null | null | null | null | OSF research protocol and report workflow |
null | null | track_publication_hf_spaces | null | null | null | null | null | null | Hugging Face dataset and Spaces publication |
null | null | track_data_packaging_standards | null | null | null | null | null | null | Research-data packaging standards |
null | null | track_mapping_workbench | null | null | null | null | null | null | Human-in-the-loop mapping workbench |
null | null | track_ci_cd_supply_chain | null | null | null | null | null | null | CI/CD and supply-chain hardening |
null | null | track_policy_demonstrators | null | null | null | null | null | null | First policy demonstrators |
null | null | track_data_quality_evidence | null | null | null | null | null | null | Data quality, source validation and evidence readiness |
null | null | track_public_product_citation_dashboard | null | null | null | null | null | null | Public product, citation and dashboard maturity |
null | null | track_historical_source_archival_reproducibility | null | null | null | null | null | null | Historical source archival and academic reproducibility |
null | null | track_osf_registration_record_quality | null | null | null | null | null | null | OSF registration record quality and protocol freeze |
null | null | track_evidence_adjudication_review | null | null | null | null | null | null | Evidence adjudication and accountable-review closure |
null | null | track_source_provenance_licence_release | null | null | null | null | null | null | Source provenance, licensing and historical release reproducibility |
null | null | track_release_record_archive_maturity | null | null | null | null | null | null | Citation, archive and public record maturity |
null | null | track_external_publication_archive_execution | null | null | null | null | null | null | External publication and archive execution |
null | null | ds_us_cms_mcd_downloads | null | null | null | null | null | null | null |
null | null | ds_us_hospital_price_transparency | null | null | null | null | null | null | null |
null | null | ds_us_transparency_in_coverage | null | null | null | null | null | null | null |
null | null | ds_us_open_payments | null | null | null | null | null | null | null |
null | null | ds_oecd_health_statistics | null | null | null | null | null | null | null |
null | null | ds_who_ghed | null | null | null | null | null | null | null |
null | null | ds_world_bank_wdi_health | null | null | null | null | null | null | null |
null | null | ds_brazil_sigtap | null | null | null | null | null | null | null |
null | null | ds_chile_fonasa_arancel | null | null | null | null | null | null | null |
null | null | ds_colombia_cups | null | null | null | null | null | null | null |
null | null | ds_korea_hira | null | null | null | null | null | null | null |
null | null | ds_thailand_nhso | null | null | null | null | null | null | null |
null | null | ds_singapore_moh_benchmarks | null | null | null | null | null | null | null |
null | null | ds_aihw_health_expenditure | null | null | null | null | null | null | null |
null | null | ds_ihme_gbd | null | null | null | null | null | null | null |
null | null | ingest_au_mbs_1989_2010_previous_downloads_page | null | null | null | null | null | null | null |
null | null | ingest_au_mbs_2010_2019_downloads_page | null | null | null | null | null | null | null |
null | null | ingest_au_mbs_20260701_txt_pair | null | null | null | null | null | null | null |
null | null | ingest_au_mbs_20260701_xml | null | null | null | null | null | null | null |
null | null | ingest_au_mbs_seed_fixture | null | null | null | null | null | null | null |
null | null | ingest_uk_genomic_directory_seed_fixture | null | null | null | null | null | null | null |
null | null | ingest_uk_genomic_test_directory_rare_v9 | null | null | null | null | null | null | null |
null | null | ingest_us_cms_clfs_26clabq3_ama_zip | null | null | null | null | null | null | null |
null | null | ingest_us_cms_clfs_seed_fixture | null | null | null | null | null | null | null |
null | null | ingest_au_pbs_api_v3_current_month | null | null | null | null | null | null | null |
null | null | ingest_au_pbs_seed_fixture | null | null | null | null | null | null | null |
null | null | ingest_us_cms_asp_july_2026_payment_limit | null | null | null | null | null | null | null |
null | null | ingest_us_cms_asp_seed_fixture | null | null | null | null | null | null | null |
null | null | ingest_us_cms_pfs_2026_revision_c_carrier | null | null | null | null | null | null | null |
End of preview.
Reimbursement Atlas Seed Data
This dataset contains design-stage metadata for public reimbursement schedules and planned policy analyses. It does not contain restricted ontology source data, proprietary code-system descriptors or confidential pricing.
The repository code and documentation are Apache-2.0. Dataset rows retain source-specific licensing and attribution requirements; this card does not grant Apache-2.0 rights to underlying MBS, PBS, CMS, ontology, or other third-party data. Publish only manifest rows with confirmed redistribution permission.
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