Application of a non-linear model to understand healthcare processes: using the functional resonance analysis method on a case study of the early detection of sepsis

Abstract

The use of non-linear models to understand complex processes in healthcare is not a fully adopted concept. Current patient safety research focuses on events by studying adverse events, typically trying to understand the root causes of failures. This article describes an attempt in a Danish hospital to create an understanding of how complex processes produce positive outcomes despite variability and unforeseen factors, using the functional resonance analysis method (FRAM) to describe a frequent activity in healthcare: early detection of sepsis. The model presents 40 activities performed by nurses, doctors, secretaries, health workers and laboratory technicians; and illustrates possible and actual variability in the process. The results reveal that the application of FRAM helped to gain a heightened understanding of a complex healthcare process. The FRAM provided new insights to staff by focusing on aspects that previously had not been central when working with the patient safety during sepsis detection. This included aspects such as becoming aware of the importance of asking the right questions during the referral process from a general practitioner, using experience and clinical judgement during early assessment of patients and the importance of having a good collegial relationship between doctors and nurses. The method helped reveal how the process is often able to succeed despite variability, and how aspects like experience and clinical judgement play a vital role in adapting to everyday conditions. This knowledge can enhance the understanding of how complex processes develop and be useful in supporting their management and improving patient safety.