Artificial intelligence (AI) and automation are being used more in healthcare, including crisis intervention services. Many healthcare groups in the United States want to use AI to help with front-office tasks, answering phones, and crisis support. It is important to know how different groups of people feel about these technologies. This matters a lot for medical office managers, clinic owners, and IT staff who decide whether to use new technology in places where trust and sensitive communication are very important.
A recent study by Jennifer S Ma looked at what people think about using AI and automation in crisis intervention like Lifeline in Australia. Though the study was not in the U.S., its results are still useful for American health administrators because of similar healthcare challenges and diverse patients.
Crisis intervention needs quick, caring, and personal help for people in distress. Usually, trained human counselors provide this service, which helps comfort patients by connecting emotionally. Adding AI and automation to these services makes some users worried.
The study involved 1,300 community members and 553 people seeking help in crisis situations. About one-third of these people did not want technology to collect personal information during these moments. Also, half said they might stop using a crisis support service if it switched to automation instead of human interaction.
The main reason for this is that people want to talk to real humans who can understand feelings and give personalized answers. They worry automation might take the place of human counselors, lowering the quality and warmth of care.
This shows that healthcare managers in the U.S. need to find a careful balance. They have to use AI to save resources but keep personal contact so patients trust the service.
The study found that age is an important factor in how people feel about automation in crisis support. Older adults, who may be used to traditional services or less comfortable with digital tech, are less likely to support AI and automation in these sensitive areas.
Older people were about 50-66% more likely than younger people to oppose automation. This result was clear from the data. This is important for U.S. healthcare since the country has many older patients. Clinics with many elderly patients may face more pushback against automated systems, especially in behavioral health or crisis lines that need strong trust.
Other factors like income level, education, and experience with technology might also affect how comfortable people feel with AI, but the study mainly looked at age. Medical managers should think about these factors when planning AI use to make sure services are fair for everyone.
One key finding was that almost half the people surveyed said they might not use crisis support if automation was used. This means that automation might lower patient use instead of helping it, at least at first.
In the U.S., where patient satisfaction and easy access matter a lot, this should warn health managers. They must talk clearly to patients about how automation will work with human helpers and not replace them.
Health IT staff should work with communications teams to make materials that reassure patients about the continued personal help, even when AI handles tasks like call routing or data gathering.
As U.S. healthcare uses more AI for front-office jobs and crisis response, possible benefits include faster service, shorter wait times, and better handling of many calls. Still, it is important to think about how work flows change and how patients feel about these changes.
Front office workers often handle appointment scheduling, screening calls, and gathering information. Using AI to manage some of these tasks can make work easier, cut human mistakes, and keep communication steady.
Patients who call crisis lines want quick and correct answers. Automation can sort calls, find urgent needs from answers, and connect patients quickly to the right human helper. This kind of system helps efficiency but does not remove humans completely.
Automation can help by collecting and saving important patient details during calls. This saves staff time and helps human responders have needed background when they talk to patients. Still, some patients, especially older ones, worry about automated data collection. They feel uneasy about privacy and less personal care.
Health administrators should clearly explain how data is kept safe and used to make services better. Patients need to know automation is there to help clinicians, not replace them.
Using AI well in crisis help depends on making sure workflows improve instead of causing problems. Staff should get training to use AI tools well, and communication must be clear about changes.
U.S. clinics should adjust automation plans to fit different patient groups. Older patients might want to talk to a live person right away, while younger, tech-smart users may like starting with self-service.
The study shows that using technology in care like crisis calls needs careful communication. Many users don’t want to accept automation because they fear humans will be fully replaced.
Sharing positive stories from patients and staff who use AI-supported services may help people feel better about these changes.
Managers and clinic owners thinking about using AI in phone answering and crisis services should consider their patients’ ages and attitudes. Doing surveys with their own patients can give useful information like the study found.
Older people and those not confident with technology might need extra help, such as the choice to reach a live person directly or clear explanations about how AI works to help them. Practices can also add automation slowly and get feedback along the way.
IT managers should choose AI systems that can be changed and are clear to users. Features like quick transfer to human counselors and easy access to privacy details are important.
Systems like those from Simbo AI automate front-office phone jobs. They can reduce wait times, handle more calls, and improve communication, but still keep the important human part.
Adding AI and automation into crisis help and healthcare communication has benefits. But people’s acceptance depends a lot on their age and background, which affects if they will keep using services.
Medical workers in the U.S. need to pay close attention to what patients think. Instead of using automation just because it is new, it should be used in ways that help human helpers and keep patient trust.
Clear messages that people will stay involved in crisis support are very important. Only when patients trust the system will AI improve access, speed, and health results.
By carefully using AI tools that fit their patients, U.S. healthcare providers can make crisis help and front-office services better while respecting patient wishes and keeping care quality.
The study explores consumer perspectives on the use of artificial intelligence (AI) and automation in crisis support services, particularly examining acceptability and anticipated service use if such technologies were implemented.
Older age was identified as a predictor for being less likely to endorse technology and automation in Lifeline’s crisis support services.
One-third of participants from both community and help-seeker samples did not support the collection of information about service users through technology and automation.
Approximately half of the participants reported they would be less likely to use Lifeline’s crisis support services if automation was introduced.
The most common reason for reluctance was the desire to speak to a real person, with concerns that human counselors would be replaced by automated systems.
Lifeline plans to always have a real person providing crisis support, despite the potential introduction of new technologies and automation.
Incorporating technology requires careful messaging to reassure users that the human connection will continue, addressing fears about losing personal interaction.
The study used a mixed methods approach, involving computer-assisted telephone interviews and web-based surveys to collect data from a representative sample.
The study engaged a nationally representative community sample of 1300 participants and a help-seeker sample of 553 individuals.
The research included quantitative descriptive analysis, binary logistic regression models, and qualitative thematic analysis to address various research objectives.
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