When the doctor-patient interaction is viewed as a series of utterances, the temporal position of utterances becomes a central information in understanding the nature of interaction. Important concepts are interdependence and serial dependence which account for the fact that two partners influence each other in their talk and that each partner influences him/herself. Lag sequential analysis studies the associations between doctor and patient utterances in a two-way contingency table (lag one sequences) and is used for exploratory purposes. Log-linear modelling, based on multi-way contingency tables, is used as an extension of lag-sequential analysis to study longer sequences.
Markov chains test sequences in terms of processes with the aim to find predictive models and require a theory driven approach. Pattern recognition aims to discover regularities in the temporal evolution of the utterance sequences. Theory driven applications analyse manifest patterns in terms of their conditional probability distribution while empirically driven applications are used to detect “hidden” patterns. These different approaches to sequential data can be regarded as complementary tools to describe the doctor patient consultations at various levels of complexity.
Declaration of Interest: none.