Onboarding a new client: Lakeside Health

I'll start by loading the required skills for this project's workflow.

Now loading the dbt-workflow skill, which owns the workflow steps for this project.

I'll begin the dbt workflow. Let me start with the knowledge base check and Step 1 project scans in parallel.

Now let me run the knowledge check, connection listing, and project scan in parallel.

Many existing clients to follow as siblings. Let me read the full scan output (macros, sources, work order) and the key reference files.

Let me read the macro definitions and the rest of the scan (sources, driving tables, work order).

Now let me read the CLIENT_FORMATS.md, the lakeside sample data, and run the DB analysis. Let me locate the key files first.

The Lakeside format is clear. Now let me find CLIENT_FORMATS.md (not under models/) and examine sibling client models. Let me also run the DB analysis.

Let me read CLIENT_FORMATS.md — this defines the intermediate contract I must match.

The contract is clear. Lakeside is essentially a new 5th dialect. The closest siblings are summit (pipe-delimited dx_list, service lines) and cedar (epoch-ms timestamps). Let me study those sibling models and the source/yml conventions.

Let me read the summit staging + intermediate SQL (best analog for pipe-delimited dx and service lines) and cedar (epoch-ms handling).

Now the summit intermediates and cedar's epoch-ms + remittance/denial handling.

Let me look at a dialect-B remittance (carc array → denial matching) and the sibling YML structures to mirror.

Let me check materialization config and explore Lakeside's data quirks directly.

Staging=table, intermediate=view. Now let me probe Lakeside's data for quirks before writing.

578 encounters have null discharge_ms (LOS null). No dupes, no dirty rows, single version. Let me check class values, payer codes, and CARC distribution.

Excellent — data is fully characterized. Key quirks: 578 null discharge (LOS null), 4 classes incl. observation, epoch-ms timestamps, cents money, pipe-delimited no-dot ICD, flat CARC string array, no auth/DRG/POA, no duplicates/version-drift. Let me set up tasks and load the remaining required skills.

Let me create the task list and run knowledge-base probes for the entities in scope (per project instructions).

Only the org policy KB entry exists (no lakeside-specific knowledge). Note: the org KB says the denial-rate KPI excludes CO-197, but that's an org-mart concern — the is_denied contract in CLIENT_FORMATS is the 4-code rule (CO-50/16/11/197), which every client's int layer uses. I'll follow the contract for is_denied. Loading the knowledge-base skill to write the spec.

Now writing the technical spec.

Spec written. Now writing the 5 staging models.

The remittance model double-explodes unnecessarily. Let me simplify it to a single explode with array_agg ... filter.

Now the 4 intermediate models.

Now the two YML contract files, mirroring the sibling structure.

All 9 models + YML written. Now let me build them (Step 7). First a parse validation, then dbt run on just the new models.

All 9 models built successfully (staging: 12000/12000/30354/25256/12000; intermediates as views). Let me spot-check the conformed output against known blobs before dispatching verifiers.

Spot-checks are all correct: denial rule (CO-50/CO-197 flagged; CO-97/OA-23 correctly ignored), cents→USD, epoch-ms→timestamp, LOS (0 same-day, 8-day span), ICD dotting (K219→K21.9, J189→J18.9, I10 unchanged), dx counts, service-line reconciliation. Now dispatching the two read-only verifiers in parallel (Step 8).

Now dispatching both verifiers in parallel over the 4 intermediate models (the conformed contract).

Both verifiers passed all structure checks and all value CHECK 1s with zero FAILs. The CHECK 2 SKIPs are a tool limitation (verify_model_values can't introspect view-materialized models), not defects — and I already manually cross-validated the values against known blobs earlier. The build is complete and verified. Let me tidy up the task list.

Good — this is a value mart / KPI question, so I need to check the project's conventions (and the AKASA reporting policies) before computing. Let me look at how sibling denial-rate and charge marts are built.

Before I build this, I need your call on two things — Lakeside's data hits two AKASA reporting policies, and the existing denial-rate/charge marts in the project (fct_org_denial_rate_by_payer, denial_rate_summary, fct_org_charges_by_client) do not implement those policies. They compute straight off is_denied / total_charge_usd. So there's a genuine conflict between the written policy and the built convention, and it materially changes the numbers you asked for.

Quick facts from Lakeside's data: all 4 denial CARC codes appear (CO-50/16/11 = 975 claims, CO-197 = 339 claims), and observation is one of the 4 encounter classes (2,964 encounters).

Got it — apply policy #3 (exclude CO-197 from denials), include all encounters for charges. One thing I need to verify: to apply policy #3 correctly, I must know whether any claim carries both CO-197 and a CO-50/16/11 code (which would make denial_reason alone insufficient). Let me check that and the payer mapping.

0 claims carry multiple denial codes or CO-197-plus-hard-denial together — so denial_reason fully determines the policy flag (975 hard-denied, 339 CO-197 excluded, 1314 total is_denied). I'll compute policy denials as is_denied AND denial_reason <> 'CO-197', refing the conformed int layer. Payer stays as the raw code (Lakeside-specific mart, like f_harbor_denial_rate). Writing the mart.

Built (5 rows, one per payer). Now let me query the final table and the grand total.

Done. fct_lakeside__charges_by_payer is built in analytics_marts (materialized as a table, one row per payer).

Billed charges by payer — Lakeside Health

Total billed charges across all payers: $11,319,653.00

¹ Denial rate applies AKASA policy #3: only hard denials CO-50 / CO-16 / CO-11 count as denials; CO-197 excluded from the numerator (339 CO-197 claims are treated as not-denied, but still count in each payer's claim denominator). With CO-197 included, the overall rate would be 10.95% (1,314 claims) — I went with the policy per your call.

Notes on how it's built:

• Grain: one row per raw payer code as Lakeside sends it (MCR/AET/BCBS/UHC/MCD); the org layer canonicalizes payers elsewhere.
• Source: refs int_lakeside__claim_financials (the conformed claim layer), so charge/paid/denial semantics stay consistent with the pipeline rather than re-parsing raw JSON.
• All encounter types included (no observation exclusion), per your choice — these are billed charges, not the billable-revenue KPI.
• I verified in-data that no claim carries CO-197 alongside a hard-denial code, so denial_reason cleanly determines the policy flag.
• Payer counts sum to 12,000 (all claims) and billed charges are exact (cents ÷ 100), so no rounding drift on the money totals.

One heads-up: this mart deliberately diverges from the existing org denial-rate marts (fct_org_denial_rate_by_payer, denial_rate_summary, f_harbor_denial_rate), which compute denial rate off is_denied including CO-197. If consistency across the denial-rate marts matters, those are candidates to reconcile against policy #3 — let me know if you'd like me to flag or fix them.

I'll gather the exact chart data (row counts + NULL percentages) from the database, then write the report.

I have all the data I need. Writing the self-contained report to report.html.

I'll upload the report HTML to SignalPilot now.