Artif Intell Med - Computational intelligence for the Balanced Scorecard: studying performance trends of hemodialysis clinics.

Tópicos

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{ detect(2391) sensit(1101) algorithm(908) }
{ clinic(1479) use(1117) guidelin(835) }
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Resumo

JECTIVES: The Balanced Scorecard (BSC) is a general, widely employed instrument for enterprise performance monitoring based on the periodic assessment of strategic Key Performance Indicators that are scored against preset targets. The BSC is currently employed as an effective management support tool within Fresenius Medical Care (FME) and is routinely analyzed via standard statistical methods. More recently, the application of computational intelligence techniques (namely, self-organizing maps) to BSC data has been proposed as a way to enhance the quantity and quality of information that can be extracted from it. In this work, additional methods are presented to analyze the evolution of clinic performance over time.METHODS: Performance evolution is studied at the single-clinic level by computing two complementary indexes that measure the proportion of time spent within performance clusters and improving/worsening trends. Self-organizing maps are used in conjunction with these indexes to identify the specific drivers of the observed performance. The performance evolution for groups of clinics is modeled under a probabilistic framework by resorting to Markov chain properties. These allow a study of the probability of transitioning between performance clusters as time progresses for the identification of the performance level that is expected to become dominant over time.RESULTS: We show the potential of the proposed methods through illustrative results derived from the analysis of BSC data of 109 FME clinics in three countries. We were able to identify the performance drivers for specific groups of clinics and to distinguish between countries whose performances are likely to improve from those where a decline in performance might be expected. According to the stationary distribution of the Markov chain, the expected trend is best in Turkey (where the highest performance cluster has the highest probability, P=0.46), followed by Portugal (where the second best performance cluster dominates, with P=0.50), and finally Italy (where the second best performance cluster has P=0.34).CONCLUSION: These results highlight the ability of the proposed methods to extract insights about performance trends that cannot be easily extrapolated using standard analyses and that are valuable in directing management strategies within a continuous quality improvement policy.

Resumo Limpo

jectiv balanc scorecard bsc general wide employ instrument enterpris perform monitor base period assess strateg key perform indic score preset target bsc current employ effect manag support tool within fresenius medic care fme routin analyz via standard statist method recent applic comput intellig techniqu name selforgan map bsc data propos way enhanc quantiti qualiti inform can extract work addit method present analyz evolut clinic perform timemethod perform evolut studi singleclin level comput two complementari index measur proport time spent within perform cluster improvingworsen trend selforgan map use conjunct index identifi specif driver observ perform perform evolut group clinic model probabilist framework resort markov chain properti allow studi probabl transit perform cluster time progress identif perform level expect becom domin timeresult show potenti propos method illustr result deriv analysi bsc data fme clinic three countri abl identifi perform driver specif group clinic distinguish countri whose perform like improv declin perform might expect accord stationari distribut markov chain expect trend best turkey highest perform cluster highest probabl p follow portug second best perform cluster domin p final itali second best perform cluster pconclus result highlight abil propos method extract insight perform trend easili extrapol use standard analys valuabl direct manag strategi within continu qualiti improv polici

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