Healthcare Claims Fraud Screening
Turn Medicare and Medicaid claims into ranked, auditable investigation referrals.
The evidence shows deterministic detection, a citation gate, human SIU review, no autonomous publishing, and data stored outside the install path; LOAD-CLAIMS also requests read-only access. However, the skill handles highly sensitive medical claims and identity data while requesting connection strings, credentials, or FHIR bearer tokens without sufficient detail on credential isolation, field minimization, access auditing, encryption, deletion, or rollback, so points are deducted.
The documentation provides a fairly complete procedure, directory conventions, stage snapshots, and some recovery guidance, and separates detection, adjudication, and synthesis. Static evidence does not establish availability of the key scripts, Workflow and Write tools, or external dependencies in the target environment; unified diagnostics for network failures, missing tables, tool failures, and abnormal inputs are also limited, so the score remains conservative under the static cap.
The audience, corpus input, quarter, line of business, and outputs are reasonably clear, and the non-fit boundaries for real-time editing and final fraud determinations are stated. Trigger exclusions, payer data-quality boundaries, Chinese-language operation, and mainland-China network reachability are not specified; core reference fetching depends on CMS, OIG, and other overseas services, which may limit access for FollowSkills users.
The documentation is well organized and includes quickstart material, claim loading, reference data, architecture, limitations, licensing notes, and extension guidance; official organizational provenance is supplied. The skill lacks clear versioning, changelog, named maintenance responsibility, or an update commitment, while license metadata is unknown and dependency/tool prerequisites are incomplete, so points are deducted.
The intended task and output format are clear, and deterministic screening, cited referral packets, dashboards, and XLSX outputs could be directly useful; the documentation also requires dollar and rule claims to trace to the detection stage. Because this is a static review with no representative run results, sample outputs, or real-data coverage evidence, end-to-end completeness and boundary quality cannot be confirmed, so the score is capped at 7.
The materials describe deterministic detectors, a citation gate, stage snapshots, cohort data for recomputation, and repository CI-related files. They do not provide executed results for this skill's key paths, proof of committed test coverage, or independent review records, so the score is limited to the strongest level supported by static evidence.
- Do not treat the output as a fraud determination; SIU or compliance investigators must review it.
- Before supplying a claims database, connection string, or FHIR credential, confirm read-only access, credential handling, data minimization, and access auditing.
- Reference fetching depends on CMS, OIG, and state-policy sites; unreachable overseas networks, URL changes, or bot protection may produce incomplete results.
- Before running, verify the quarter's reference database, DuckDB schema, Node.js, unzip, pdftotext, Workflow, and Write tools, and retain failure logs.
What it does & when to use it
This skill screens a payer’s Medicare or Medicaid claims corpus against public sources including NCCI MUE, OIG LEIE, CMS enrollment, and PFS. It runs a deterministic detection layer first, then applies model-based adjudication and provider-level narrative synthesis for SIU or program-integrity review. Findings are framed as indicators consistent with a scheme rather than proof of fraud, and cited dollar or rule allegations must trace back to independently recomputed detection results. Adoption fits teams with normalized claims data, quarter-specific reference data, and an environment that can provide local filesystem, shell, network, and workflow support.
Reads a canonical six-table corpus.duckdb plus a rule quarter and line of business; fetches and caches public reference and enrichment data when needed; runs deterministic detectors and a citation gate; adjudicates judgment-required findings by confirming, downgrading, or dismissing them; synthesizes provider narratives; and produces detection, adjudication, and final JSON snapshots, provider HTML packets, an HTML dashboard, and referrals.xlsx.
- An SIU team needs a recurring Medicare claims sweep for fraud, waste, and abuse indicators.
- A Medicaid program-integrity group needs ranked referrals from anomalous billing patterns.
- A payer needs findings checked against NCCI MUE, LEIE, CMS enrollment, and PFS rules with reproducible citations.
- Investigators need provider-organized review packets with source excerpts and cited rule allegations.
Pros & cons
- Separates deterministic calculations from model adjudication and narrative synthesis, with citations gated against recomputation.
- Supports Medicare and Medicaid and references NCCI MUE, OIG LEIE, CMS enrollment, PFS, and local enrichment data.
- Produces ranked referrals, provider review packets, an HTML dashboard, and an XLSX export.
- Uses cautious SIU language and does not treat pattern matches as established fraud.
- Requires a payer claims corpus.duckdb in the specified six-table schema.
- Initial or new-quarter reference seeding requires network access and depends on Node.js, unzip, and pdftotext.
- The Workflow tool and /workflows experience are Claude Code-specific and require adaptation elsewhere.
- The supplied materials show no test suite, performance benchmarks, or supported corpus-size limits.
How to install
Add the repository to the Claude Code plugin marketplace and install the healthcare plugin: /plugin marketplace add anthropics/healthcare, followed by /plugin install healthcare@healthcare. The README documents installation for the full healthcare collection, not a separate installation command for this skill.
How to use
Provide a payer claims corpus.duckdb with the documented canonical six-table schema, a reference quarter, and either medicare or medicaid. Example trigger: “Run a fraud sweep on this Medicaid claims corpus using the 2026q3 rules and generate investigation referrals.” The client must provide the documented Workflow, shell, network, and local filesystem capabilities.