Analyzing the Anesthesia human-machine system
When anesthetized, you are in the hands of the nurses and machines. How does this system operate and are there any needs for improvements? A cognitive ergonomics project applied in real context, during a course at Chalmers, masters level, together with two other students.
This was a heavily user research-based project to learn how to use methods to extract needs and requirements in contexts involving complex systems and expert users. As students we were free to try out as many methods as possible.
I learned how to measure mental workload, understood more about mental models, attention, and different sensory modalities. I also gained practice in using systems theory to eventually identify solutions that could support the anesthesia nurses in their work.
As students, we were welcome to observe a couple of surgeries. This was an unique experience and I am impressed by the relaxed, yet focused atmosphere in the surgery rooms, and how the teams cooperated to ensure that every detail was under control.
During the surgeries we could ask questions and get a much better understanding of the different tasks than if we were to only use a regular interview.
- Contextual inquiry
- Observations (of brain surgeries in surgery room)
- Enhanced Cognitive Walkthrough (to identify potential use errors)
- Nasa TLX (evaluate task demand and mental workload)
- ECOM (analysis of decision making processes)
- ETTO principle (the trade-off between efficiency and thoroughness)
- ACTA (expert interviews, to understand difference in mental models between expert and novice)
- Hierarchical Task Analysis
- Use cases
- User profiles and personas
- User relations
- Emotion wheel
Defining the system
The first step was to define the system, its elements, connections and borders. There is a lot going on in the surgery room so this definition helped us to keep track of what was happening, and to keep our focus on the anesthesia system.
Analyzing the tasks
The next step was to identify the tasks, and the main goal of the system. We focused on three use cases throughout the analysis - preparation, patient start and monitoring, as these were the ones we could observe.
Measuring mental workload
Different methods were used to identify which tasks meant a higher mental workload for the nurses. Below are ratings generated by the Nasa-TLX method for the tasks “Preparation” (left) and “Patient start” (right). The emotional aspects of the tasks were also evaluated, showing that nurses experienced negative feelings when they felt unprepared for an event during the surgery.
Findings made it clear that all nurses were highly skilled in handling the ventilator. The most valuable knowledge comes with experience, and builds on what we referred to as “Physiological thinking”. Inexperienced nurses found it difficult to always be prepared for the unexpected, while an experienced nurse is more likely to predict what can go wrong during a procedure, as they had a more robust understanding of how the human body could react in certain situations.
Wireframing a solution
One of the most cognitive demanding tasks was the preparation phase. The nurse has to memorize the name of the patient, the age, weight and height as well as any diseases that can affect the surgery. This was often done by repeating the factors over and over again in their minds at the same time as they prepared the surgery room.
The proposed solution is a system that helps the nurse and the team to easily access these parameters, along with supporting functions where nurses can log the injected drugs and make notes to communicate between departments. The solution should support the operator with planning and retrieval of information before, during and after a surgery.
One of the key features would be to present relevant information and warnings based on the medical journal, medicine list, planned procedure and actions during the surgery. The system could give suggestions on what to think about during the surgery which both helps the experienced nurse to prepare mentally, and supports new nurses to improve their mental model referred to as physiological thinking.
In the future, a system built on AI and machine learning could help the nurses to become even better at identifying risks and avoiding unexpected events.
Changes on an organizational level?
To make the greeting of the patient less demanding, it was also suggested that the anesthesia team would get more time with the patient, before it is time to enter the surgery room. The suggested system could then be used to make notes about any circumstances that could affect the anesthesia, and the nurses would have a calm setting when meeting the patient for the first time.