Clinical Verification of Homeopathic Symptoms: Experience and Statistics

Successful homeopathic prescriptions are not based on diagnosis only. The population with a certain diagnosis or disease is divided in subgroups, each subgroup responding to another homeopathic medicine. The necessity to divide ‘diagnosis-populations’ into subgroups is also becoming relevant in conventional medicine and as such studied in pharmacogenetics (Swen et al., 2007). Homeopathic doctors use symptoms (personal traits) to recognize subgroups in the same way as pharmacogenetic tests. Clinical and pharmacogenetic tests render probabilities; the probability of a diagnosis is sequentially updated after each test that indicates the diagnosis. This updating process is effectuated in Bayesian statistics. It is possible to describe the homeopathic prescription as a Bayesian algorithm.

In this chapter, some possible ways to assess homeopathic symptoms with scientific instruments are shown. First, Bayesian statistics is introduced. In the methods section, the various methods used in three projects are presented, the first is long-term recording of all repertorisations and prescriptions in one practice and its retrospective analysis, the second is retrospective evaluation of successful cases of a group of Dutch doctors by consensus meetings, and the third is multi-centered prospective assessment of six homeopathic symptoms. Some aspects of the discussion are placed under the results of the respective methods; the more general aspects of the discussion are placed after the results.