AI can accelerate revenue capture, reduce denials, and improve coder productivity—but only when quality assurance (QA), payer policy compliance, and safe automation are built into the core. At Ember, we recommend anchoring your evaluation around ten non-negotiable questions. Below, you’ll find those questions along with the signals of a strong answer—and how Ember approaches each area.
What to look for: A written QA plan spanning hypercare through steady state: double-coding samples, drift monitoring, regression tests, reviewer qualifications, and thresholds that trigger corrective action.
Ember approach: Structured QA with defined audit cadence (weekly in hypercare, then monthly), double-coding workflows, regression test suites, and documented exit criteria from pilot to scale.
What to look for: Clear definitions for precision, recall, F1; inter-rater reliability against certified SME reviews; and outcome metrics tied to revenue integrity (denials, DNFB days, late charge capture).
Ember approach: Metric packs by service line and code family, severity stratification, and side-by-side SME concordance so changes are measurable—not anecdotal.
What to look for: Transparent dashboards, exportable evidence, and a cadence for reviews with action registers.
Ember approach: Shared QA workspaces with drill-downs to case-level rationale, scheduled readouts, and closed-loop tracking on corrective actions.
What to look for: Named, certified SMEs (e.g., CCS, CPC) embedded through discovery, rules tuning, and sign-off gates.
Ember approach: Certified SMEs define inclusion/exclusion criteria, validate code sets, and co-own sign-offs at each implementation milestone.
What to look for: Tranche planning that starts with low-risk, high-volume cohorts; documented criteria like documentation completeness, payer mix, historical denial patterns, and code complexity.
Ember approach: Tranche rollout playbooks per service line with explicit eligibility rules and stop-loss thresholds.
What to look for: Confidence thresholds by code family, concordance checks with SME rules, and human-in-the-loop fallbacks when signals conflict.
Ember approach: Guardrailed auto-apply powered by confidence policies, multi-signal validation, and automatic routing to review when ambiguity is detected.
What to look for: CCI and payer policies applied before commit, not after; clear update propagation and conflict resolution.
Ember approach: Native CCI edit checks and payer policy reviews held inside the decision flow, with versioned policy libraries and update logs.
What to look for: Deterministic exception routing, edit snapshots, suggested fixes, and end-to-end audit trails.
Ember approach: Auto-route to review with embedded rationale, remediation guidance, and full case auditability.
What to look for: MLOps discipline—versioned models, change logs, champion-challenger/A-B tests, rollback procedures, and measured impact on downstream outcomes.
Ember approach: Continuous improvement loops with controlled rollouts, outcome tracking, and rapid rollback if thresholds regress.
What to look for: A published code-set calendar (CPT annual; ICD-10 updates), automated ingestion, regression testing on representative cohorts, and customer sign-off.
Ember approach: Code-set governance with scheduled updates, targeted regression suites, and clear communication of changes and expected impact.
Signals to trust
Red flags
Ember helps revenue cycle teams operationalize safe automation with embedded QA, payer-policy intelligence, and transparent analytics. If you’re evaluating AI for RCM, start with the ten questions above—and ask every vendor to provide evidence.
