WellCure Pitch Deck Notes

WellCure Pitch Deck Notes (Comprehensive Summary)

The clinical problem

  • Postoperative period is a critical recovery phase with high risk for deterioration.
  • Key statistics (source: Rowland BA, Motamedi V, Michard F, et al. Br J Anaesth. 2024;132:519–27):
    • >50\% of fatalities could be avoided after surgery with better monitoring.
    • Up to 30\% of surgical patients face serious complications.
  • Clinicians currently rely on spot checks; there is no tool for continuous, real-time predictive monitoring.
  • Early perioperative risk identification can prevent most serious adverse outcomes.
  • Context: This problem is framed as a readiness/need for continuous monitoring with predictive capabilities.

The impact (market and human impact)

  • Global/postoperative landscape:
    • 50{,}000{,}000\,\text{patients} worldwide suffer serious post-surgical complications each year.
  • Mortality risk linked to delayed detection:
    • Up to 33\% increase in mortality with delayed detection of post-surgical deterioration.
  • Economic impact in the United States:
    • Annual cost of post-surgical complications: €43{,}000{,}000{,}000\$-range? or more clearly: \$43-\$73\,\text{B} per year.
  • These figures underscore a large-scale opportunity for continuous monitoring and early intervention tools.
  • Relevance to healthcare systems: significant potential savings in hospital stays and ICU utilization when deterioration is detected earlier.

The solution (WellCure / Callisia approach)

  • Callisia’s solution aims to address the clinical problem with:
    • Wireless continuous monitoring
    • Predictive models for early risk detection
  • Outcome focus: reduce time to detection, preempt crises, and minimize unnecessary ICU transfers.

Technology and product architecture

  • Software layer:
    • AI-based platform for predictive analytics and alerting.
    • Source cited: Slight, Sarah P. et al. ROI study on continuous monitoring systems (Critical Care Medicine 2014) [context for ROI of continuous monitoring].
  • Hardware layer:
    • LEAF smartband equipped with biosensors.
    • Provides continuous vital signs data capture to feed AI models.
  • Data and systems integration:
    • RAW high-frequency data to support AI algorithms.
    • Data capture rates:
    • ECG up to 500\,\text{Hz}
    • PPG at 100\,\text{Hz}
    • Health indicators monitored include:
    • Cardiovascular health and autonomic nervous system activity
    • Stress levels
    • Respiratory and cardiovascular health
    • Arrhythmia detection
    • Blood volume variations and vascular health
  • AI/ML capabilities:
    • Real-time AI alerts for immediate anomaly detection
    • Ability to identify trends and potential crises before symptoms appear
    • AI-based personalized alerts tailored to each patient, as opposed to generic thresholds
  • Data infrastructure:
    • RAW data is ingested and stored; feature extraction performed upstream
    • Streaming data pipelines: Apache Kafka (high-frequency data) and related processing
  • User interface:
    • Dashboard for clinicians (web/mobile/desktop)
    • Graphical visualization of AI model outputs and patient risk
  • In-house hardware development:
    • Clinical-grade biosensor hardware with a personalized board

Predictive models and value proposition

  • Predictive capabilities vs traditional monitoring:
    • Continuous monitoring (24/7 vitals) vs periodic checks by staff
    • Real-time AI alerts vs delayed manual intervention
    • Early crisis prediction (trends before symptoms) vs reactive care
    • Reduced ICU transfers through preemptive intervention vs late detection leading to ICU transfer
    • AI-based personalized alerts vs generic thresholds
  • Synthetic data and model development:
    • Predictive models include synthetic data simulations inspired by research on patient deterioration to augment real data.

Comparative advantages (Callisia vs traditional monitoring)

  • Real-time monitoring and alerts:
    • Callisia: ✔ Continuous 24/7 tracking of patient vitals
    • Traditional: ✖ Periodic checks by staff
  • Immediate anomaly detection:
    • Callisia: ✔ Real-time AI Alerts
    • Traditional: ✖ Delayed detection
  • Early crisis prediction:
    • Callisia: ✔ Predicts crises early (before symptoms appear)
    • Traditional: ✖ Reacts when symptoms worsen
  • ICU transfer reductions:
    • Callisia: ✔ Reduces ICU transfers via preemptive intervention
    • Traditional: ✖ Late detection leads to ICU transfers
  • Personalization of alerts:
    • Callisia: ✔ AI-based personalized alerts
    • Traditional: ✖ Generic thresholds for all patients
  • Note: The comparison is framed as synthetic vs traditional monitoring, emphasizing proactive care enabled by AI.

Technology stack and data workflow (WellCure / Callisia)

  • Storage, processing, and GUI components
  • Data pipeline:
    • Raw data -> DB -> Streaming via Apache Kafka (High Frequency) -> Extracted features -> AI models
  • Multi-LEAF devices (devices on patient)
  • General database integration for patient data
  • GUI: Dashboard, AI models visualization accessible on Web, Mobile, Desktop

Hardware and clinical-grade sensors

  • In-house built personalized hardware device for clinical use
  • Clinical-grade biosensor ensemble on a personalized board
  • Emphasis on safety, interoperability, and clinician usability

Competitive landscape and market metrics

  • Competitive map indicators (IT context):
    • TAM: 4.7\text{ B} (EU)
    • SAM: 468\text{ M€} (IT)
    • SOM: 4\text{ M€} (IT)
    • Beach Head: 700{,}000 (IT)
  • Source for market sizing: Future Market Insights (continuous wireless patient monitoring market)

Maturity, personalization, and usability

  • Maturity: Callisia positions as a mature, validated technology with continuous monitoring capabilities.
  • Personalization: Emphasizes customization and tailoring to a wide range of user needs.
  • User friendliness: Focus on ease of use for clinicians and patients.
  • Customization: High degree of adaptability to different clinical workflows and settings.

Team and governance

  • Core team and co-founders:
    • Dianel Ago – CEO, Data & AI
    • Alessio Ancillai – CFO, Finance & risk management
    • Simone Murazzo – CTO, Software development
    • Jonathan Montomoli – MD, PhD
    • Floriano Bonfigli – Dott. Ing. MSc, President
    • Prof.ssa Claudia Diamantini – CdA, DII - UNIVPM
    • Prof. Domenico Potena – CRO; R&D, DII - UNIVPM
    • Ing. Fernando Negozi – Vice-President, CdA, Eletica SRL
    • Rifat Seferi – CoFounder, CdA, LIS SRL
  • Board and advisory roles reflect collaboration with university and industry partners.

Roadmap (timeline highlights)

  • 2023: MVP ready; EU/USA Series A fundraising target of €20M; Potential exit; LEAF hardware development; Company registration. Pre-seed milestones include €130k (✅).
  • 2024: LEAF hardware development continues; company registration completed; further pre-seed activities.
  • 2025: Pilot projects; development of predictive models.
  • 2026: Seed round targeted (€5M value) and funding needs (€1M specified).
  • 2028–2030: Longer-term milestones referenced but not detailed in the source text.

Collaborative ecosystem and partnerships

  • AIMED-EU & partner networks for advancing AI-driven care across Europe.
  • Notable collaborators listed (illustrative): Vidavo, Steinbeis Europa Zentrum, Aristotile, University of Marche, University of Thessaloniki, Ivano-Frankivsk National Medical University, IRCCS INRCA, E EUINNOVA, ARIF/AR… (note: the source text includes a dense list with acronyms and affiliations; these represent a broad European innovation ecosystem participation)
  • The collaboration underscores regulatory, cybersecurity, and innovation support for deploying AI-enabled medical devices in Europe.

Contact and branding

  • Brand site: www.callisia.health
  • Contact email: tech@callisia.health

Notable sources and claims to verify

  • Rowland BA, Motamedi V, Michard F, et al. Br J Anaesth. 2024;132:519–27 – cited for clinical risk statistics.
  • Slight, Sarah P. et al. The return on investment of implementing a continuous monitoring system in general medical-surgical units. Critical Care Medicine 2014 – cited for ROI context of continuous monitoring.
  • Market sizing source: Future Market Insights report on wireless patient monitoring (cited for TAM/SAM/SOM context).

Quick reference (key numbers in LaTeX)

  • Postoperative fatalities potentially avoidable: >50\%
  • Serious post-surgical complications: 30\%
  • Global post-surgical complications: 50{,}000{,}000
  • Mortality increase with delayed detection: 33\%
  • U.S. post-surgical complication costs: €43{,}000{,}000{,}000\text{ to }€73{,}000{,}000{,}000 \text{ or }\$43-\$73\,\text{B}
  • ICU stay cost reduction: -€2{,}000/\text{day} per patient
  • Hospital stay cost reduction: -€700/\text{day} per patient
  • Annual savings (mid-sized hospital): \$6.8\,\text{M}
  • Data rates: f{ECG} \le 500\,\text{Hz}; f{PPG} = 100\,\text{Hz}
  • Population and market sizing (EU): TAM 4.7\text{ B}; SAM 468\text{ M€}; SOM 4\text{ M€}; Beach Head 700{,}000
  • Roadmap financials (selected): Pre-seed €130{,}000; Series A €20{,}000{,}000; Seed €5{,}000{,}000; Post-seed needs €1{,}000{,}000