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|Citació: ||Gibert, C.; García, C.; Salvador, L. Integrating clinicians, knowledge and data: expert-based cooperative analysis in healthcare decision support. "Health research policy and systems", 16 Octubre 2010, vol. 8, núm. 28, p. 1-16.|
|Títol: ||Integrating clinicians, knowledge and data: expert-based cooperative analysis in healthcare decision support|
|Autor: ||Gibert Oliveras, Karina ; García Alonso, Carlos; Salvador Carulla, Luis|
|Tipus de document: ||Article|
|Resum: ||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.|
|Versió de l'editor: ||http://www.health-policy-systems.com/content/8/1/28|
|Apareix a les col·leccions:||Departament d'Estadística i Investigació Operativa. Articles de revista|
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