Título Integrating clinicians, knowledge and data: Expert-based cooperative analysis in healthcare decision support
Autores Gibert K., GARCÍA ALONSO, CARLOS, Salvador-Carulla L., GARCÍA ALONSO, CARLOS
Publicación externa No
Medio Health Res. Policy Syst.
Alcance Review
Naturaleza Científica
Cuartil SJR 1
Impacto SJR 0.89700
Ámbito Internacional
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957189392&doi=10.1186%2f1478-4505-8-28&partnerID=40&md5=d20a46f5f0413f1ef51451c4257e8ac7
Fecha de publicacion 01/01/2010
ISI 000208219500028
Scopus Id 2-s2.0-77957189392
DOI 10.1186/1478-4505-8-28
Abstract Background: Decision support in health systems is a highly difficult task, due to the inherent complexity of the process and structures involved.Method: This paper introduces a new hybrid methodology Expert-based Cooperative Analysis (EbCA), which incorporates explicit prior expert knowledge in data analysis methods, and elicits implicit or tacit expert knowledge (IK) to improve decision support in healthcare systems. EbCA has been applied to two different case studies, showing its usability and versatility: 1) Bench-marking of small mental health areas based on technical efficiency estimated by EbCA-Data Envelopment Analysis (EbCA-DEA), and 2) Case-mix of schizophrenia based on functional dependency using Clustering Based on Rules (ClBR). In both cases comparisons towards classical procedures using qualitative explicit prior knowledge were made. Bayesian predictive validity measures were used for comparison with expert panels results. Overall agreement was tested by Intraclass Correlation Coefficient in case "1" and kappa in both cases.Results: EbCA is a new methodology composed by 6 steps:. 1) Data collection and data preparation; 2) acquisition of "Prior Expert Knowledge" (PEK) and design of the "Prior Knowledge Base" (PKB); 3) PKB-guided analysis; 4) support-interpretation tools to evaluate results and detect inconsistencies (here Implicit Knowledg -IK- might be elicited); 5) incorporation of elicited IK in PKB and repeat till a satisfactory solution; 6) post-processing results for decision support. EbCA has been useful for incorporating PEK in two different analysis methods (DEA and Clustering), applied respectively to assess technical efficiency of small mental health areas and for case-mix of schizophrenia based on functional dependency. Differences in results obtained with classical approaches were mainly related to the IK which could be elicited by using EbCA and had major implications for the decision making in both cases.Discussion: This paper presents EbCA and shows the convenience of completing classical data analysis with PEK as a mean to extract relevant knowledge in complex health domains. One of the major benefits of EbCA is iterative elicitation of IK.. Both explicit and tacit or implicit expert knowledge are critical to guide the scientific analysis of very complex decisional problems as those found in health system research. © 2010 Gibert et al; licensee BioMed Central Ltd.
Palabras clave Bayes theorem; case mix; comparative study; correlation coefficient; data analysis; decision making; decision support system; health care; information processing; knowledge; mental health; methodology
Miembros de la Universidad Loyola

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