The scientists placed sensors on people’s fingers to record pulse amplitude while they were in a driving simulator, as a measure of arousal. An algorithm used those recordings to learn to predict an average person’s pulse amplitude at each moment on the course. It then used those “fear” signals as a guide while learning to drive through the virtual world: If a human would be scared here, it might muse, “I’m doing something wrong.”Hutson, M. (2019). Scientists teach computers fear—to make them better drivers. Science magazine.
This makes intuitive sense; algorithms have no idea what humans fear, nor even what “fear” is. This project takes human flight-or-flight physiological data and uses it to train an autonomous driving algorithm to get a sense of what we feel when we face anxiety-producing situations. The system can use those fear signals to more quickly identify when they’re moving into dangerous territory, adjusting their behaviour to be less risky.
There are interesting potential use cases in healthcare; surgery, for example. When training algorithms on simulations or games, errors do not lead to high-stakes consequences. However, when trusting machines to make potentially life-threatening choices, we’d like them to be more circumspect and risk-averse. But one of the challenges is to get them to identify situations in which a human’s perception of risk is included in the decision-making process. Learning that cutting this artery will likely lead to death can be done by cutting that artery hundreds of times (in simulations) and noting the outcome. This gives us a process whereby the algorithm “senses” a fear response in a surgeon before cutting the artery, and possibly sending a signal indicating that they should slow down and call for help. This could help when deciding whether or not surgical machines should have greater autonomy when performing surgery because we could have mroe confidence that they’d ask for human intervention at appropriate times.
I’ve recently finished the analysis of the first round of the Delphi study that I’m conducting as part of my PhD. The aim of the study is to determine the personal and professional attributes that determine patient outcomes, as well as the challenges faced in clinical education. These results will serve to inform the development of the next round, in which clinical educators will suggest teaching strategies that could be used to develop these attributes, and overcome the challenges.
Participants from the first round had a wide range of clinical, supervision and teaching experience, as well as varied domain expertise. Several themes were identified, which are summarised below.
In terms of the knowledge and skills required of competent and capable therapists, respondents highlighted the following:
- They must have a wide range of technical and interpersonal skills, as well as a good knowledge base, and be prepared to continually develop in this area.
- Professionalism, clinical reasoning, critical analysis and understanding were all identified as being important, but responses contained little else to further explain what these concepts mean to them.
In terms of the personal and professional attributes and attitudes that impact on patient care and outcomes, respondents reported:
- A diverse range of personal values that they believe have relevance in terms of patient care
- These values were often expressed in terms of a relationship, either between teachers and students, or between students and patients
- Emotional awareness (of self and others) was highlighted
In terms of the challenges that students face throughout their training:
- Fear and anxiety, possibly as a result of poor confidence and a lack of knowledge and skills, leading to insecurity, confusion and uncertainty
- Lack of self-awareness as it relates to their capacity to make effective clinical decisions and reason their way through problems
- A disconnect between merely “providing a service” and “serving”
- They lack positive and supportive clinical learning environments, have poor role models and often aren’t given the time necessary to reflect on their experiences
- The clinical setting is complex and dynamic, a fact that students struggle with, especially when it comes to dealing with complexity and uncertainty inherent in clinical practice
- Students often “silo” knowledge and skills, and struggle to transfer between different contexts
- Students struggle with the “hidden culture” of the professional i.e. the language, values and norms that clinicians take for granted
These results are not significantly different from the literature in terms of the professional and personal attributes that healthcare professionals deem to be important for patient outcomes.
The second round of the Delphi is currently underway and will focus on the teaching strategies that could potentially be used to develop the attitudes and attributes highlighted in the first round.