Will patients trust AI in healthcare?
As we witness the early transitioning to the ‘automated era’— whereby AI and machine learning technologies alleviate the burden of manual and menial work— it has become clear that its reception among its intended userbase will face a challenge of human resistance.
Away from heavy industries— factory floors, manufacturing, and supply-chains— AI is increasingly handling interactions between business and customer. It is, perhaps, the most challenging test of the technology’s ability to mimic human intelligence.
Whether it’s seeking reassurance in customer support, sound financial advice, or medical diagnoses, humans ability to make significant decisions with conviction innately requires a sense of trust. And this is something that, at present, can only be struck in the bond of genuine human interaction— not between man and machine.
Nowhere is this truer than in healthcare, where ‘bedside manner’— how a medical professional interacts and communicates with a patient— can make all the difference in the patient’s overall sense of wellbeing and in the emotional management of difficult, if not ultimately ‘life-or-death’ situations.
At the same time, the healthcare industry is one whose workforce is overburdened. Rising healthcare costs and resources spread ever more thinly is pressuring the sector to innovate and seek new solutions to the overall benefit of a never-ending stream of patients.
AI applications in healthcare
The pursuit of better healthcare has led to groundbreaking developments within the last few centuries, and this record has continued with the advent of AI technology.
Programs can now identify diseases with image analysis 1000 times faster than previously possible, or determine a patient’s predisposal to an illness based on their family’s medical history, suggesting clinical decisions as a result.
Robotic systems are in development that will learn from past operations, and assist surgeons with “minimally-invasive” surgery which could result in five times fewer complications and a 20 percent shorter hospital stay.
Advanced computational tools are also transforming pharmacy, changing the odds of drug discovery and even enhancement. Microsoft’s chatbot allows healthcare organizations to field requests from patients— as with other industries, AI technology has big potential in relieving the glut of admin work.
When it comes to direct patient interaction, however, AI technology could be struggling to kindle the sense of interpersonal trust built from years of professional healthcare experience. When it comes to human interactions so crucial to effective healthcare, the technology may have reached its limits.
Confidence in MedTech
Recent research into the way humans interact with automated MedTech tools found that patients’ confidence in their abilities and willingness to accept diagnoses, was significantly affected by their overall comfort with the technology.
The findings come as the medical field sees the increasing deployment of automated systems in the medical field, where intake is now frequently conducted through a kiosk, instead of by a receptionist.
“We investigated user acceptance of these ‘robot receptionists,’ along with automated nurses and doctors,” said the researchers. “In addition, we tested whether the form that these roles took—human-like, avatar or robot—made a difference in user acceptance.”
Ahead of the study, the volunteers provided researchers with preconceived beliefs about AI-driven technologies. That included ‘machine heuristics’, or the stereotypes held about machines and their capabilities. Patients were given a range of healthcare scenarios where the healthcare professional was presented as either a human, digital avatar or machine. They were then asked about the acceptance of the service they were given and the likelihood of using it again.
“We found that the higher people’s beliefs were in the machine heuristic, the more positive their attitude was toward the agent and the greater their intention was to use the service in the future,” the researchers explained. “We also found that ‘power usage’ predicted acceptance of digital healthcare providers. A power user (a person with advanced computer skills) is more likely to accept a robot doctor, for example, than a non-power user.”
The researchers said that technological ability and support of the machine heuristic resulted in the most positive attitude towards potential automation of healthcare provision. On the other hand, those lacking in these preconceptions would be less accepting of autonomous technology in healthcare.
But the researchers didn’t suggest that those more skeptical users should be prioritized for treatment by human doctors and nurses. Instead, the interfaces of automated healthcare facilities could simply be tailored for each patient, they said.
“Designers can direct resources toward improving features such as chat functionality instead of anthropomorphizing healthcare robots. In addition, increasing the number of power users and the general belief that machines are trustworthy may increase the adoption of automated services.”
Automated receptionists are, of course, a long way off from patients trusting robotic systems to perform life-changing surgery. But they are a signal of what’s to come with the ongoing permeation of automation technology in the sector.
As AI advances and becomes more accessible, the various applications and industries its deployed to will witness both striking benefits and a war of resistance— whether it’s fear of job losses, concerns over responsibility, or lack of trust in algorithm-based decisions.
In the medical industry— where technology and innovation is key to progression— the real challenge may be in convincing patients to ultimately put faith in data and silicon, equal to that they put into experienced, seasoned and, most importantly, human medical professionals.
The understanding that every individual has preconceived notions of the technology— which could affect their sense of wellbeing and recovery— must be present, and options supplied accordingly.
24 September 2020
16 September 2020
16 September 2020