Incorporating local information and prior expert knowledge to evidence-informed mental health system research

We discuss the problems in limiting the evidence base to randomised controlled trials (RCTs) alone in the evaluation of complex and innovative mental care services and systems. The classical evidence-based care (EBC) approach is characterised by an 'aversion to complexity' which could be identified by four axioms: (a) the experimental method is regarded as the gold standard of EBC, (b) observational data are included in the same dimension of evidence as experimental data, and therefore observational studies are rated in a lower grade than RCT, (c) the use of classical statistics is based on algebraic formalism as the reference techniques for data analysis and (d) expert knowledge is regarded as a source of bias and it is excluded from the data processing. We suggest a new method, the Expert-based Cooperative Analysis (EbCA), as a general framework suitable for research in very complex medical problems aimed at reducing uncertainty and increasing the strength of local decision-making. We present here a case study of its applicability in the analysis of mental health systems. The technical efficiency of the small health areas of Andalusia (Spain) has been studied using Data Envelopment Analysis, Bayesian networks and EbCA. The incorporation of prior expert knowledge, local data and modelling of natural phenomena are critical to ground priority setting and policy formation combined with the traditional evidence-based approach.