OBJECTIVES: In the intensive care (IC), monitoring data are continuously being collected. Unfortunately, this data is often not interpretable for clinicians and therefore goes unused. While advanced analytics can process this data to support improvements in patient care, developers ideally invest in those analytics that lead to the greatest health benefits and/or cost reductions. We explored the potential cost-effectiveness of three IC analytics.

METHODS: We assessed the cost-effectiveness of three applications: the early identification of catheter-related bloodstream infection (CRBSI), improved nutrition monitoring and identification of high driving pressure (HDP) in ventilated patients. The literature was used to populate the decision trees and patient data (N=62) from a Greek IC was used for the HDP model . We performed univariate, multivariate, and probabilistic sensitivity analyses.

RESULTS: Early identification of CRBSI reduced mortality and length of stay thus increasing quality-adjusted life-years (QALYs) (6.70 vs. 6.58) and reduced costs (€8,184 vs. €9,869); probability of CRBSI and the costs per IC day were key influencers of the ICER and the probability of dominance was 90%. Improved nutrition monitoring could increase nutrition adherence (45% vs. 80%) and decrease costs by reducing length of stay (€9,768 vs. €9,036). Its impact on health is uncertain but it will not likely reduce health. Regarding identification of HDP, uncertainty about various input parameters meant great uncertainty surrounding impact on health and costs.

CONCLUSIONS: Developers should study the potential impact of the analytics they wish to develop before investing time and money. As an example, we illustrated that analytics for early identification of CRBSI and improved nutrition monitoring are worth further investment. However, the impact of identifying HDP will probably be limited despite them being considered clinically relevant. When the impact of the analytics is deemed sufficient, developers must consider the importance of access to the data needed to develop and validate their analytics.