Cassis welcomes enquiries from investors interested in predictive decision support, To request a private demonstration or discuss a specific applications, contact mike_tremblay@skythunder.net.
My focus is productivity improvement in workflow to achieve a variety of objectives:
- improve, augment and support people in healthcare and pharmaceutical companies to reason better in decision making;
- Reduce rework and simplify complex challenges especially at the boundary of choice-making;
- Reduce the risk of medical errors by cognitive modelling of medical heuristics and ‘how clinicians think’;
- Reduce the risk of business errors by cognitive modelling of commercial reasoning.
| Illustrative Decision | Illustrative Prediction | Illustrative application |
|---|---|---|
| Health status deterioration | Change over the next 24–48 hours | COPD, heat risk |
| Avoiding harm | Increasing individual vulnerability | Medicines use |
| Medical errors | Probability that important evidence is being missed | Pain, Sepsis, Red flags |
| Recovery | Deviation from expected personal trajectory | Rehab, rare diseases |
| Reasoning and cognitive drift | Changes in decision quality | Cognitive drift, medical errors |
| Workflow optimisation | Reduce rework and avoidable delay | Care trajectory |
For those thinking of costs, my modelling assumes a value-base approach, designed to release net healthcare or commercial benefit. All require investment to bring them to a market ready commercialisation and clinical / regulatory standard. Experience suggests that a clinically credible healthcare/pharma -AI prediction platform typically requires approximately €/$/£ 750,000 and 6–9 months of focused development to reach MVP and pilot readiness. A demonstrator is about €/$/£ 250,000 over a couple of months. The costs of licenses or outright purchase can be discussed.
Clinical Applications: A reusable predictor approach
The clinical work focuses on a practical question:
Can we identify when an individual patient is becoming more vulnerable before harm, deterioration or diagnostic failure becomes clinically obvious?
Current healthcare systems are often good at recording events after they occur. They are less effective at predicting what is likely to happen next for a specific patient. Cassis addresses that gap by developing patient-specific predictors for defined clinical decisions, including deterioration, medication-related harm, falls, diagnostic error, rehabilitation and heat-related risk. The underlying capability is robust developmental methodology for turning complex clinical uncertainty into a defined prediction problem, with a a usable decision support resource for clinicians.
The problem
Preventable harm remains a major cost to healthcare systems, pharmaceutical companies, providers and patients. The problem is rarely a complete absence of information. More often, relevant information is:
- distributed across different sources;
- recognised too late;
- interpreted inconsistently;
- assessed against population averages rather than the patient’s own baseline;
- not converted into a forward-looking estimate of risk.
The objective is to estimate whether risk is changing within a defined future window, commonly the next 24 to 48 hours, while there is still time to review treatment, increase monitoring or intervene.
Pharma Applications: A reusable predictor approach
The work addresses a practical question:
Can we identify changes in clinical, commercial or market-access risk early enough to improve the decision?
Pharmaceutical organisations generate large volumes of clinical, safety, economic and market data. The difficulty is not usually a lack of information, but converting fragmented evidence into a forward-looking decision.
The problem
Important decisions are often based on:
- evidence distributed across several sources;
- retrospective analysis;
- population averages;
- inconsistent interpretation;
- uncertain assumptions;
- limited modelling of alternative decisions.
These can delay recognition of clinical risk, weaken pricing or reimbursement strategy, and reduce the ability to anticipate how regulators, HTA bodies, payers or clinicians may respond. The objective is to estimate how a defined outcome is likely to change before the decision is made.
Why Cassis is different
Cassis begins with the decision, not the AI technology. The aim is to improve judgement under uncertainty. This supports an important methodological shift comprising:
- individual prediction to replace population risk models;
- clinical foresight to replace retrospective detection;
- patient trajectory to capture continuity of data over time, not just isolated measurements;;
- generic alerts to decision-focused recommendations, with quantified risk banding to quantify uncertainty, not generic alerts.
- retrospective analysis to forward prediction;
- static evidence to changing trajectories;
- population averages to patient or market-specific estimates;
- generic dashboards to decision-focused outputs;
- single-point estimates to quantified uncertainty.
Reusable architecture
Although each predictor addresses a different problem, the development method is consistent. Cassis has developed:
- defined use cases;
- predictor logic and mathematical specifications;
- interactive HTML simulators;
- synthetic cohorts and scenarios;
- patient-specific and population-based models;
- decision outputs for clinical and commercial users, including clinician-facing decision outputs;
- risk visualisation;
- before-and-after intervention modelling;
- documentation for clinical, technical and commercial review.
The economic value lies in the reusable methodology so additional applications can be developed more efficiently.