Medicall AI – Intelligent Call Center for Healthcare

Note: All numbers are modeled or derived from pilot‑like synthetic scenarios for illustration only. Robust evidence requires field studies.

Summary

Medicall AI combines real‑time ASR, domain‑specific language understanding, safe triage with Human‑in‑the‑Loop (HITL), and agentic orchestration of clinical and administrative workflows. Modeling suggests 35–45% of contacts shift to self‑service or scheduled callbacks, keeping utilization (ρ) stable and reducing Average Speed of Answer (ASA) by 60–75%. Simulated multi‑channel reminders cut no‑shows by 3–6 percentage points while cost per contact drops 20–35%. With GDPR‑by‑design and deep HL7 FHIR integration, Medicall AI becomes the "nervous system" of ambulatory care.

1) Introduction

Demand growth, staffing shortages, and seasonal peaks cause long queues, missed calls, and repeated capture errors. Medicall AI addresses this with 24/7 availability, safe triage, structured data capture, and end‑to‑end system integration to improve access, safety, experience, and productivity.

Objectives

Reduce ASA and abandonment via self‑service, intelligent routing, and callback scheduling; increase FCR with domain NLU; reduce no‑shows with adaptive reminders; enforce safety through layered triage and HITL; ensure GDPR‑grade compliance.

2) Related Work

Classical IVR and generic voice bots lack guardrails for high‑risk domains. Medicall AI uses medically trained NLU, terminological coding (SNOMED CT/ICD‑10‑GM/OPS), and rule‑based triage for auditability.

3) System Architecture

Components: telecom gateway (SIP/WebRTC), real‑time ASR (target < 250 ms), medical NLU, dialog/policy manager, triage/safety engine, agentic orchestration, HL7 FHIR integration, security/privacy, monitoring/analytics.

Latency targets: ASR ≤ 250 ms; NLU+policy ≤ 120 ms; triage/rules ≤ 80 ms; FHIR I/O ≈ 100–150 ms; end‑to‑end < 600 ms.

4) Methods

Streaming ASR

Transducer/CTC with VAD and rescoring, supporting 8 kHz μ‑law and 16 kHz; robustness via noise reduction, accent adaptation, augmentation; metrics: WER, RTF < 1, endpointing delay < 300 ms.

Medical NLU

Intents via cross‑encoder; slots via sequence tagging plus terminology mapping; resolution with rule‑ and vector‑semantic disambiguation; HITL for sensitive fields; calibration via Platt/Isotonic.

Dialog Policy & Guardrails

Provable rules with HITL thresholds; safety kernel with allow/deny lists and domain constraints.

Safe Triage

Red flags (e.g., chest pain, stroke signs) with immediate escalation; risk model (logistic regression/GBTs); goal: high sensitivity with acceptable specificity.

Agentic Orchestration

Task graph: intake → triage → eligibility → slot search → booking → confirmation → reminder → follow‑up; transactions via saga and idempotency keys; outbox/event sourcing.

HL7 FHIR Integration

Resources: Patient, Appointment, Schedule, Slot, Communication, Coverage; security via OAuth2/SMART on FHIR and RBAC/ABAC; data quality via validation/normalization.

5) Queueing and Capacity Modeling

M/M/c with Priorities

Given arrival rate (λ), service rate (μ) and c servers. Without the system, utilization ρ approaches 1. With self‑service s and callbacks k: λ' = λ·(1−s−k). Example: λ=120/h, μ=40/h, c=3 ⇒ ρ=1. With s=0.35, k=0.25: λ'=48 ⇒ ρ'=0.4; ASA/abandonment drop ≈ 70%.

6) Security, Privacy & Compliance

Data minimization; pseudonymization; TLS 1.3 in transit, AES‑256 at rest; RBAC/ABAC; audit trails; DPIA and incident response ≤ 72 h; terminology mappings and retention/deletion policies.

7) Evaluation Design

KPIs: service (ASA, AHT, FCR, abandonment), quality (WER, intent/slot F1, triage sens/spec), economics (cost/contact, utilization, no‑show, ROI), experience (CSAT, NPS, PES). Study designs: A/B or stepped‑wedge cluster RCT; Difference‑in‑Differences.

8) Results (Simulation)

| Szenario | ASA (s) | Abandon (%) | FCR (%) | No‑Show (%) | Cost/Contact (€) | Jahres‑ROI |
| --- | ---: | ---: | ---: | ---: | ---: | ---: |
| Basis (ohne System) | 180 | 22 | 58 | 12 | 4,10 | — |
| Medicall AI (S=35 %, K=25 %) | 55 | 8 | 78 | 9 | 2,95 | 1,6× |
| Medicall AI + adaptive Reminder | 50 | 7 | 80 | 7 | 2,85 | 1,9× |

ROI formula: (Benefit − Cost) / Cost. Example arithmetic yields ~1.7× annual ROI for a typical practice.

9) Reliability, SRE & TCO

SLOs: 99.9% availability, P95 < 800 ms, booking errors < 0.5%; autoscaling by call rate/queue lag; observability with OpenTelemetry; TCO optimization via edge inference, FHIR caching, audio compression, and efficient models for low‑risk paths.

10) Limits & Risks

Quality drop at 8 kHz μ‑law; NLU bias; legacy integration risks; legal/ethical constraints. Mitigations: 16 kHz, fairness monitoring, adapters/canaries/rollback, transparency and handoff to humans at any time.

11) Rollout Roadmap

1) Process discovery → 2) Integration phase 1 (read‑only, KPIs) → 3) Limited pilot → 4) Domain expansion (auto‑booking, low‑risk triage with HITL) → 5) Formal evaluation (RCT/DiD, security/privacy audit) → 6) Scale.

Conclusion

Medicall AI unifies speech AI, domain understanding, safe triage, and robust orchestration into an auditable platform for ambulatory care. With GDPR‑by‑design and rigorous evaluation, it can improve access, safety, experience, and efficiency simultaneously.

Appendix A: KPI Definitions

ASA, AHT, FCR, abandonment, WER, CSAT/NPS/PES, ROI/cost‑per‑contact.

Appendix B: Safety/Triage Pseudocode

if red_flag_detected(utterance, slots):
    escalate_to_human(within_seconds=10)
    mark_case(priority="critical")
else:
    risk = triage_model.predict(slots)
    if risk > tau_high:
        escalate_to_human()
    elif tau_low < risk <= tau_high:
        proceed_with_booking_and_confirmation()
    else:
        provide_self_service_information()

Appendix C: Clinical Terminologies & Standards

ICD‑10‑GM/OPS for diagnoses/procedures; SNOMED CT for precise clinical concepts; HL7 FHIR for structured data objects (Patient, Appointment, Schedule, Slot, Communication, Coverage).