The focus of my career is clinical reasoning and decision making / priority setting.
My career involved working in clinical settings and on challenges facing healthcare and pharma, I have formal training in mathematical logic (modal and non-standard logics) and cognitive psychology (human reasoning and decision making).
Boundary Logic and Decision-Making Under Uncertainty
My postgraduate work focused on the application of mathematical logic to reasoning under uncertainty, specifically in domains where decisions cannot be meaningfully reduced to discrete choices. The central premise was that many real-world systems do not operate across well demarcated thresholds, but instead function within boundary regions or zones in which multiple actions remain possible, confidence can be low, and small changes in information, interpretation, or institutional context can materially alter outcomes.
At the time, this work sat largely in the domain of formal logic. Today, it maps directly onto the core challenges of modern artificial intelligence, clinical decision support, and regulatory intelligence.
From Discrete Decisions to Probability Fields
Contemporary AI systems are fundamentally probabilistic. However framed (Bayesian inference, deep learning confidence estimation, or causal modelling), they operate over continuous decision surfaces rather than binary truth states. Yet most human and institutional approaches still force these outputs into high versus low risk, approve versus reject, treat versus watchful waiting.
Boundary logic formalises the fact that the most consequential errors, biases, and institutional failures occur at the margin and near decision thresholds, and where evidence is incomplete, and human judgement, policy, or incentive structures begin to dominate the outcome.
Clinical and Regulatory Relevance
In healthcare, most predictive systems are evaluated on accuracy metrics, yet deployed through frameworks that rely on rules. Clinical pathways, triage protocols, and treatment guidelines often present as pass/fail decision ‘gates’, but are in practice driven by graded judgements of risk tolerance, evidence quality, clinician experience, and institutional norms.
Boundary logic provides a formal lens for modelling this. Rather than asking “What is the predicted risk?”, it asks “How close is this case to the point where different actions become equally defensible, and what factors, whether human, institutional, or technical, are likely to determine the final decision?”
This perspective underpins modern approaches to:
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Bias detection in clinical decision-making
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Regulatory and HTA simulation using gated evaluation frameworks
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Personalised adaptive learning systems that move away from population averages toward individual baselines
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Ethical reasoning that distinguish between rule-based compliance and principle-based judgement.
AI Safety
In current AI research, there is growing emphasis on uncertainty estimation, out-of-distribution detection, and epistemic humility or the ability of a system to recognise when it does not know. These are, in effect, operational forms of boundary logic. Indeed, for many applying clinical problems to LLMs, they worry that the LLM may not admit to not knowing. In that respect, LLMs do model human hubris.
Rather than optimising for confidence, such systems aim to reveal areas of indeterminacy or regions where model output, human judgement, and institutional policy intersect, and where the risk of error or misalignment is highest.
… So what does that mean?
This intellectual foundation informs the design of intelligent agents for clinical, pharmaceutical intelligence. The focus is on modelling the full decision topology, including:
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How decision makers respond to rising or falling risk over time
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How cognitive biases and heuristics influence actions near critical thresholds
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How institutional rules transform continuous information into discrete outcomes
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How ethics intervene when model confidence and human confidence diverge
The result is a class of systems designed to operate explicitly in the “grey zones” where most real-world harm, cost, and controversy tend to concentrate.
My brief CV
I have provided consultancy since 1997 and now I have evolved my company Cassis to focus on using AI to create decision support tools for healthcare, pharma/life sciences.
I was a founding director of Volv Global, Switzerland, which has a focus on AI and patient identification.
My experience at EDS and Kearney was excellent grounding for me.
I was a founding director of Eden Communications in the UK which launched the world’s first digital and interactive health TV channel, “Living Health”, in partnership with UK broadcasters. The channel received a number of awards for innovation.
I had direct healthcare experience at Hamilton Health Sciences, McMaster University in Canada as head of a department focused on improving the quality of clinical services. The work focused on clinical workflow, skill mix and patient experience. I tutored in the Faculty of Health Sciences in epidemiology. Amazing place to work!
As Senior Lecturer at HSMC, University of Birmingham, UK, I taught and published on health policy and management decision making and priority setting. I advised the European Commission and the Council of Europe and governments. I was an Associate Dean of Medicine, Deputy Director Public Service MBA and Director Masters in Health Quality Management (quantitative methods / operational research).
I did my doctorate at the University of Toronto and found my doctoral study of applied psychology consequential.
At McMaster University, my Masters was on the application of mathematical logic to reasoning under uncertainty, and the absence of discrete choices (boundary logic).