Sparky*
Sparky*
Sparky*
Forecasting

Clinical Decision Support Platform for Early Deterioration Detection

The challenge
Early signs of patient deterioration are spread across vitals, lab results, medication records, and notes. Fixed scores cause alarm fatigue, and when staff are stretched, warning signs are recognised late.

What we built
We built a clinical decision support platform that combines bedside data, labs, medication records, and clinician notes into one timeline per patient.

AI highlights changes in risk and explains the factors behind them, so clinicians trust the signal. Care teams review a prioritised patient list, validate key indicators, and document escalation in a structured workflow that also supports ward overview and quality improvement.

Business value

  • Deterioration recognised earlier
  • Less alarm fatigue for clinical staff
  • Faster, more consistent escalation
  • Decisions traceable to the evidence

Where else it applies
The same approach transfers to home care, care homes, rehabilitation centres, and remote patient monitoring. It belongs to our Prediction and Forecasting work: past data turned into estimates of what is likely to happen next, so teams plan ahead instead of reacting.

Task

Help clinicians spot patient deterioration early.

  • Strategy

    Unify patient signals into timelines and apply explainable risk models inside the triage workflow.

  • Design

    Secure clinical data platform with AI risk forecasting, NLP on notes, and clinician-facing review and escalation.

  • Client

    Healthcare providers and hospital networks in the EU

  • Tags

    anomaly-detection, clinical, Health, healthcare, nlp

Let’s build together

Got a deep-tech challenge?

Tell us the problem. We’ll show you the closest thing we have already built and how we would adapt it to your data.

Tell us what you’re working on
Back
Next Project
News Classification and Entity Extraction