{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_alaffia-health","slug":"alaffia-health","name":"Alaffia Health","type":"product","url":"https://www.alaffiahealth.com","page_url":"https://unfragile.ai/alaffia-health","categories":["data-analysis"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_alaffia-health__cap_0","uri":"capability://healthcare.autonomous.claim.anomaly.detection","name":"autonomous-claim-anomaly-detection","description":"Machine learning models automatically scan submitted claims against historical patterns and payer rules to identify underpayments, billing errors, and payment discrepancies without manual auditor review. Detects subtle anomalies that human auditors typically miss through pattern recognition across large claim volumes.","intents":["I need to find claims that were underpaid without manually reviewing thousands of records","I want to catch billing errors before they become revenue leakage","I need to identify patterns in claim denials and rejections automatically"],"best_for":["Revenue cycle teams at mid-to-large health systems","Healthcare organizations with high claim volumes (1000+ claims/month)","Systems struggling with manual audit backlogs"],"limitations":["Requires 6-12 months of historical claim data to train models effectively","Accuracy improves over time as model learns organization-specific patterns","May have false positives in early implementation phases"],"requires":["Historical claims data (6-12 months minimum)","Integration with claims management system","Epic or Cerner EHR environment (primary support)"],"input_types":["claims data","payer remittance advice","historical billing records"],"output_types":["anomaly flags","risk scores","claim-level alerts"],"categories":["healthcare","revenue-cycle","machine-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_alaffia-health__cap_1","uri":"capability://healthcare.revenue.leakage.quantification","name":"revenue-leakage-quantification","description":"Calculates and quantifies total revenue loss across claims, denials, and billing errors, providing financial impact metrics and recovery potential. Translates detected anomalies into dollar amounts to prioritize recovery efforts and demonstrate ROI.","intents":["I need to know how much money we're losing to billing errors and underpayments","I want to quantify the financial impact of payment discrepancies for executive reporting","I need to prioritize which claims to pursue recovery on based on dollar impact"],"best_for":["CFOs and revenue cycle directors needing financial impact visibility","Healthcare systems justifying investment in payment integrity tools","Organizations with complex multi-payer environments"],"limitations":["Accuracy depends on quality and completeness of claims data","Historical data gaps may underestimate actual leakage","Cannot predict future leakage without ongoing model updates"],"requires":["Claims data with associated payment amounts","Payer remittance data","Historical denial and adjustment records"],"input_types":["claims database","remittance advice","denial records"],"output_types":["financial reports","recovery opportunity rankings","ROI projections"],"categories":["healthcare","financial-analysis","reporting"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_alaffia-health__cap_2","uri":"capability://healthcare.denial.pattern.analysis","name":"denial-pattern-analysis","description":"Analyzes denial trends across payers, claim types, and diagnosis codes to identify root causes of payment rejections. Surfaces systematic issues like missing modifiers, coding errors, or payer-specific requirements that drive recurring denials.","intents":["I want to understand why we keep getting denied for certain claim types","I need to identify which payers are denying our claims at higher rates","I want to fix systemic coding or billing issues causing denial patterns"],"best_for":["Billing compliance teams","Revenue cycle analysts","Healthcare systems with high denial rates (>5%)"],"limitations":["Requires sufficient denial volume to identify statistically significant patterns","May not catch one-off denial reasons with low frequency","Payer-specific rules change frequently and require model updates"],"requires":["Denial records with reason codes","Associated claim details (codes, modifiers, payer)","6+ months of denial history"],"input_types":["denial records","claim metadata","payer correspondence"],"output_types":["denial trend reports","root cause analysis","remediation recommendations"],"categories":["healthcare","analytics","compliance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_alaffia-health__cap_3","uri":"capability://healthcare.real.time.payment.reconciliation","name":"real-time-payment-reconciliation","description":"Continuously matches incoming payments and remittance advice against submitted claims to identify discrepancies in real-time. Flags underpayments, missing payments, and posting errors immediately rather than waiting for manual monthly reconciliation.","intents":["I need to catch payment errors immediately instead of discovering them weeks later","I want to reconcile payments automatically without manual spreadsheet work","I need real-time visibility into which claims have been paid correctly"],"best_for":["Revenue cycle teams managing high claim volumes","Healthcare systems with multiple payers and payment streams","Organizations seeking to reduce accounts receivable aging"],"limitations":["Requires real-time or near-real-time data feeds from payers","Some payers provide remittance data with delays (5-10 days)","Cannot reconcile claims without corresponding remittance advice"],"requires":["Claims management system integration","Payer remittance data feeds","Bank payment feeds"],"input_types":["submitted claims","remittance advice","bank deposits"],"output_types":["reconciliation status","discrepancy alerts","payment variance reports"],"categories":["healthcare","accounting","automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_alaffia-health__cap_4","uri":"capability://healthcare.underpayment.recovery.prioritization","name":"underpayment-recovery-prioritization","description":"Ranks identified underpayments and billing errors by recovery potential, effort required, and likelihood of successful appeal. Helps teams focus recovery efforts on high-impact cases rather than pursuing every discrepancy equally.","intents":["I want to know which underpayments are worth pursuing vs. writing off","I need to prioritize my appeals team's workload based on dollar impact","I want to maximize recovery ROI by focusing on winnable cases"],"best_for":["Appeals and recovery teams with limited capacity","Healthcare systems with large underpayment backlogs","Organizations optimizing revenue cycle efficiency"],"limitations":["Prioritization accuracy depends on historical appeal success data","Payer-specific appeal rules may change and affect predictions","Cannot guarantee appeal success even for high-priority cases"],"requires":["Historical appeal outcomes data","Underpayment details with amounts","Payer contract terms and appeal policies"],"input_types":["underpayment records","appeal history","payer policies"],"output_types":["priority rankings","recovery probability scores","effort estimates"],"categories":["healthcare","revenue-cycle","optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_alaffia-health__cap_5","uri":"capability://healthcare.billing.error.detection","name":"billing-error-detection","description":"Identifies common billing errors such as incorrect modifiers, missing required fields, coding mistakes, and claim submission issues. Catches errors before claims are submitted or flags them after rejection to prevent revenue loss.","intents":["I want to catch billing errors before we submit claims to payers","I need to identify why claims are being rejected due to formatting or coding issues","I want to reduce claim rejection rates by fixing systematic billing errors"],"best_for":["Billing departments and coding teams","Healthcare systems with high claim rejection rates","Organizations implementing billing process improvements"],"limitations":["Requires knowledge of payer-specific billing requirements","Cannot catch errors that don't violate standard billing rules","Effectiveness depends on data quality of source systems"],"requires":["Claims data with detailed field-level information","Payer billing requirement rules","Coding standards and guidelines"],"input_types":["claim records","billing data","coding information"],"output_types":["error alerts","correction recommendations","validation reports"],"categories":["healthcare","compliance","quality-assurance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_alaffia-health__cap_6","uri":"capability://healthcare.payer.performance.benchmarking","name":"payer-performance-benchmarking","description":"Compares payment performance metrics across payers including payment rates, denial rates, average payment times, and underpayment frequency. Identifies underperforming payers and contract renegotiation opportunities.","intents":["I want to see which payers are paying us less than others for similar services","I need to identify payers with unusually high denial rates","I want data to support contract renegotiation discussions with payers"],"best_for":["Revenue cycle directors and CFOs","Healthcare systems managing multiple payer contracts","Organizations seeking to improve payer relationships and contracts"],"limitations":["Requires sufficient claim volume per payer for meaningful comparison","Cannot account for differences in claim mix or patient populations","Benchmarking data may be outdated if payer contracts change"],"requires":["Claims and payment data across multiple payers","Payer contract terms","Historical payment and denial records"],"input_types":["claims database","payment records","payer contracts"],"output_types":["payer performance reports","comparative analytics","contract insights"],"categories":["healthcare","analytics","business-intelligence"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_alaffia-health__cap_7","uri":"capability://healthcare.workflow.integrated.recovery.alerts","name":"workflow-integrated-recovery-alerts","description":"Delivers actionable alerts about identified payment discrepancies directly into existing revenue cycle workflows without requiring system changes or disrupting established processes. Integrates findings into teams' daily work rather than creating separate tools.","intents":["I want payment integrity alerts to appear in my existing billing system","I need to act on recovery opportunities without switching between multiple tools","I want to integrate AI findings into our current workflow without retraining staff"],"best_for":["Healthcare systems with established revenue cycle workflows","Organizations resistant to major system changes","Teams seeking minimal disruption during implementation"],"limitations":["Integration depth depends on target system capabilities","Limited integration with legacy or non-standard EHR systems","May require custom configuration for unique workflows"],"requires":["Integration with Epic or Cerner EHR","Claims management system access","Workflow customization capability"],"input_types":["workflow data","system integration APIs"],"output_types":["in-system alerts","task assignments","workflow notifications"],"categories":["healthcare","integration","workflow-automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_alaffia-health__cap_8","uri":"capability://healthcare.transparent.roi.reporting","name":"transparent-roi-reporting","description":"Provides clear, transparent reporting on financial returns from payment integrity efforts including recovery amounts, cost savings, and ROI metrics. Uses straightforward pricing model focused on value delivered rather than hidden per-transaction fees.","intents":["I need to justify the cost of this tool to our finance team with clear ROI","I want transparent pricing without surprise per-transaction charges","I need to track and report the financial impact of payment integrity efforts"],"best_for":["CFOs and finance teams evaluating payment integrity investments","Healthcare systems seeking transparent vendor relationships","Organizations comparing payment integrity vendors"],"limitations":["ROI realization timeline depends on claim volume and recovery success","Pricing model may not be competitive for very small organizations","Actual recovery amounts depend on organization's specific issues"],"requires":["Baseline financial data on current revenue leakage","Claims volume and payment data","Agreement on ROI metrics and measurement"],"input_types":["financial data","claims metrics"],"output_types":["ROI reports","cost-benefit analysis","financial dashboards"],"categories":["healthcare","financial-reporting","business-intelligence"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["Historical claims data (6-12 months minimum)","Integration with claims management system","Epic or Cerner EHR environment (primary support)","Claims data with associated payment amounts","Payer remittance data","Historical denial and adjustment records","Denial records with reason codes","Associated claim details (codes, modifiers, payer)","6+ months of denial history","Claims management system integration"],"failure_modes":["Requires 6-12 months of historical claim data to train models effectively","Accuracy improves over time as model learns organization-specific patterns","May have false positives in early implementation phases","Accuracy depends on quality and completeness of claims data","Historical data gaps may underestimate actual leakage","Cannot predict future leakage without ongoing model updates","Requires sufficient denial volume to identify statistically significant patterns","May not catch one-off denial reasons with low frequency","Payer-specific rules change frequently and require model updates","Requires real-time or near-real-time data feeds from payers","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.43333333333333335,"quality":0.81,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:29.132Z","last_scraped_at":"2026-04-05T13:23:42.537Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=alaffia-health","compare_url":"https://unfragile.ai/compare?artifact=alaffia-health"}},"signature":"KT7kKLYjkcolFipGaFHOoSZJQAmB7TGCkeF6Di3680kBPcZfsqJLXp+wApxgzZckCes2umoEPI9b9GCBgSYPDg==","signedAt":"2026-06-21T11:48:36.553Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/alaffia-health","artifact":"https://unfragile.ai/alaffia-health","verify":"https://unfragile.ai/api/v1/verify?slug=alaffia-health","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}