This paper proposes an original approach for modelling medical expertise and simulating medical strategies. A knowledge-based system is used to model therapeutic strategies according to three axes: diagnostic, prescription and treatment effect. The diagnostic axis describes the ways of deciding whether an individual is eligible for treatment or not. The prescription axis models the ways of choosing an adequate drug for an individual or changing the current treatment if it is judged ineffective. Treatment effect concerns the effect of a drug at the individual level. This modelling is used for exploring different therapeutic strategies and quantifying their impact on the individual and population levels. We have developed a platform, based on a rule-based system, that was validated with a Use-case in Hypertension management. Classical and Alternative strategies have been simulated with the same Realistic virtual population. 20.000 individuals were considered and several parameters (e.g . optimal drug prescription, evolution of the cardiovascular risk) were calculated. The experiments showed the viability and relevance of the approach. Its strengths are numerous. Since the rules are the input of the system, they can be introduced and modified by non-programmers people, allowing prescribers to fully test their own rules. The platform is configurable in terms of modelled expertise and in terms of outputs to be measured. Empirical results concerning the superiority of the Alternative strategies have been produced.