Using interpretive structural modeling (ISM) to detect and define initiatives that facilitate hemodynamic laboratory management

Abstract

Healthcare organizations are constantly changing—as are many companies in other business sectors—and the quest for ways to improve requires these organizations to examine continuously the processes involved in their daily activities. This study sought to analyze hemodynamic laboratories’ operating activities using problem structuring methods. The main aims were to understand underlying processes more fully and to delineate initiatives that can facilitate better management (e.g., the use of cutting-edge technology based on artificial intelligence). The data analysis focused on the particular case of the Laboratório de Hemodinâmica do Hospital de Santa Marta (LHHSM) (Santa Marta Hospital Hemodynamic Laboratory) and relied on interpretive structural modeling. The data were collected during a brainstorming session with a panel of experts in the selected area and combined with the key concepts identified by a literature review of hemodynamic laboratory management studies. The results provide a better understanding of the relationships between variables that influence the dynamics of these laboratories, thereby providing the LHHSM with the information needed to select appropriate improvement initiatives. A follow-up session with the LHHSM service director was held to consolidate the findings. This study's contributions and limitations were also defined.