Computational intelligence for the Balanced Scorecard: studying performance trends of hemodialysis clinics

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.

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.

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).

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.