NiftyBot - Enrich Healthcare Data with Advanced AI

Care teams drown in paperwork while risk hides in the chart.

NiftyBot reads notes, guidelines, and payer rules to flag risk, match trials, and draft payer-ready submissions. You move care forward without waiting weeks.

Complexity: Medium

Clinical Trial Match

Recruitment shouldn’t stall on manual matching. This call screens a diabetes patient against active GLP-1 adjunct trials and returns nearby fits. Coordinators get an outreach line they can send today.

REQUEST


{ "source": { "diagnosis": "Type 2 Diabetes", "a1c": 8.2, "medications": ["metformin"], "location": "Chicago, IL", "sponsor_interest": "Pfizer" }, "requests": [ { "field_name": "eligible_trials" }, { "field_name": "next_step_note", "instructions": "One sentence coordinator outreach" } ] }

RESPONSE


{ "enrichments": [ { "field_name": "eligible_trials", "value": ["GLP-1 add-on therapy Phase 3 within 20 miles", "Lifestyle + adherence coaching hybrid remote"], "confidence": 0.76, "method": "assessment", "reasoning": "Eligibility based on A1C range, current therapy, and site proximity." }, { "field_name": "next_step_note", "value": "You may qualify for a nearby study testing an add-on therapy; I can share details and next steps if interested.", "confidence": 0.78, "method": "reasoning", "reasoning": "Compliant language that maximizes response without overpromising." } ] }

Complexity: Low

Intake Risk Triage

Not every patient should wait in the same line. This call classifies readmission risk from intake signals and offers a nudge for next steps. High-risk patients get escalated earlier.

REQUEST


{ "source": { "provider": "Cleveland Clinic", "age": 68, "symptoms": ["shortness of breath", "ankle swelling", "fatigue"], "vitals": { "bp_systolic": 150, "hr": 94 } }, "requests": [ { "field_name": "readmit_risk", "field_type": "categorical", "instructions": "Return one of: Low, Medium, High" }, { "field_name": "care_plan_hint" } ] }

RESPONSE


{ "enrichments": [ { "field_name": "readmit_risk", "value": "High", "confidence": 0.82, "method": "classification", "reasoning": "Presentation consistent with decompensated heart failure risk profile." }, { "field_name": "care_plan_hint", "value": "Cardiology follow-up within 48 hours and daily weight tracking; review diuretic adherence.", "confidence": 0.75, "method": "reasoning", "reasoning": "Early specialty touch and self-monitoring reduce readmission likelihood." } ] }

Complexity: High

Prior Authorization Accelerator

Prior auth shouldn’t take weeks. This call drafts payer-ready justification, predicts approval likelihood, and lists missing documents. Clinics move care forward with fewer denials.

REQUEST


{ "source": { "payer": "UnitedHealthcare", "procedure_code": "CPT 33249", "diagnoses": ["I42.0", "I50.22"], "supporting_docs": ["echo report", "failed med therapy note"] }, "requests": [ { "field_name": "approval_likelihood", "field_type": "categorical", "instructions": "Return one of: Low, Medium, High" }, { "field_name": "justification_summary" }, { "field_name": "missing_items" } ] }

RESPONSE


{ "enrichments": [ { "field_name": "approval_likelihood", "value": "High", "confidence": 0.79, "method": "assessment", "reasoning": "Clinical profile aligns with guideline indications and includes failure of guideline-directed therapy." }, { "field_name": "justification_summary", "value": "Meets criteria for ICD implant based on reduced EF, symptomatic heart failure despite GDMT, and recent exacerbations.", "confidence": 0.81, "method": "reasoning", "reasoning": "Includes the minimum elements payers commonly require to approve." }, { "field_name": "missing_items", "value": ["BNP lab within 30 days", "NYHA class documented in clinic note"], "confidence": 0.77, "method": "assessment", "reasoning": "Frequently requested artifacts that reduce denials and resubmissions." } ] }