Artificial Intelligence (AI) is now part of healthcare in the United States. Medical offices and hospitals use AI to help manage appointments, answer patient questions, assist in diagnosis, and improve clinical work processes. One common AI tool is the Interactive Voice Response (IVR) system. Old IVR systems used recorded voices and touch-tone phones to guide patients. Newer ones use Automatic Speech Recognition (ASR) so patients can talk naturally to the system.
Many studies show that AI-based IVR systems can make healthcare faster and cheaper. Huang and others (2022) found AI can save money and help clinics work better by automating simple tasks. Rosas and colleagues (2021) pointed out that investing in AI helps medical offices run more smoothly. This is important because US healthcare faces more patients and tight budgets.
Still, AI systems add new challenges. Healthcare focuses a lot on kindness, good communication, and care centered on patients, not just on tasks. Pizam (2010) said human contact is very important in healthcare. AI can seem cold or frustrating if the voice menus are hard to use or if patients cannot talk to a real person when needed. This can make patients unhappy and affect their health.
Right now, there are not many good tools to measure how well AI healthcare services work from different angles. Ahmet Hacikara’s 2023 study said that although AI like IVR can help, it may also cause problems if not used well. Patients might find it hard to use, miss appointments, or fail to reach staff, which can hurt their health.
Most current measurements look only at things like call waiting times or if the system is working. They do not check how patients feel or think about the service, which is very important in healthcare.
A good assessment tool should look at these parts:
Having tools that check all these parts will help managers know where AI helps and where it needs fixing.
US healthcare providers deal with many patient demands and rules. Patients want more than fast service. They want kindness, clear information, and reassurance. This can be hard when machines replace people.
Hacikara (2023) said patients may feel ignored if the system is too strict or cannot understand their emotions and urgent needs. For example, a patient trying to book an important appointment might get upset by repeated menus or no human help.
Kelly and Kaye (2023) found patients accept AI if it works well, is easy to use, and they trust it. But many studies do not clearly explain AI to patients, which can affect results.
In the US, patients come from many cultures and value their rights. AI should let patients reach human helpers easily to keep trust and good mental health.
Using AI in healthcare must follow ethical rules. Mennella and others (2024) say AI can help with diagnosis and treatment when used right. But AI also raises questions about data privacy, bias, who is responsible for decisions, and following federal laws.
Health managers and IT staff must make sure AI is clear about how it works, respects patient consent and privacy, and can be checked for fairness. Ignoring these issues can cause patient harm and lower trust in healthcare.
The FDA is working on ways to watch over AI health tools. Healthcare groups must balance new technology with safety rules to make sure care gets better and not worse.
AI helps make healthcare work smoother, especially at front desks. It can schedule appointments, answer calls, and sort patient needs. Simbo AI is one company that offers phone automation for healthcare.
AI phone systems help by routing calls and answering questions without needing humans all the time. This frees staff to care for patients and cuts costs. Huang and others (2022) found AI in phones is cost-effective and speeds up services.
But these systems must fit well with current healthcare tools and be easy to use. Using natural language processing (NLP) helps the AI understand what patients mean, which makes patients happier and reduces mistakes.
It is also important to have a way to contact a human worker quickly. Hacikara (2023) warns that only using AI without human options can hurt patients and break the care relationship.
IT managers need to watch AI performance constantly with quality checks to fix problems fast. Without this, gains from AI can be lost because patients get unhappy or mistakes happen.
Building good tools to check AI healthcare needs teamwork between administrators, IT staff, doctors, and patients. Some important steps are:
AI can help healthcare run better and give more people access to care. But US healthcare has high standards and patient rights to protect. It is important to measure how AI affects both how well the service works and how patients feel.
Creating detailed, multi-part tools to measure AI service quality and patient attitudes is very important. These tools should check technology, trust, culture, ethics, and clinical results. This will help managers understand how well AI is working.
Using these measurements along with better workflows, like phone automation from Simbo AI, can help healthcare give good access and good patient experience. Balancing technology with human help will be a key as AI grows in US medical settings.
IVR systems serve as automated phone technologies that replace human operators with prerecorded greetings and automated responses using voice recognition, allowing patients to navigate healthcare services such as appointment scheduling without live human interaction.
Modern IVR systems integrate automatic speech recognition (ASR) with traditional touch-tone inputs, enabling the system to interact with callers via both keypad and voice commands, making them AI-driven and more interactive than basic automated calls.
They offer cost-effectiveness, improved service efficiency, and profitability by automating routine tasks, speeding up service delivery, and reducing dependence on human operators, which addresses competitive and operational pressures in healthcare organizations.
IVR systems risk undermining the human-centered, interpersonal service values by causing patient frustration, disenfranchisement, and diminished care experiences due to possible complex navigation, repeated responses, or failure to connect to a human operator.
Hospitality emphasizes a mixture of tangible and intangible elements such as empathy, attentiveness, and courteous behavior, which foster patient satisfaction, memorable experiences, and overall well-being, integral to patient-centered care.
Patients may become frustrated, angry, or depressed due to navigation difficulties, delays, or communication failures, potentially worsening health outcomes if critical needs are unmet by automated systems without human intervention.
There is a notable lack of comprehensive, multi-dimensional assessment tools to measure the quality of AI-based healthcare services and insufficient research on patients’ attitudes toward such advanced technologies.
While AI facilitates automated message delivery and response handling, it can hinder effective communication by limiting empathetic, personalized human interaction, essential for trust and mental health improvements in patient care.
Communication skills of caregivers moderate the effectiveness of patient-centered care, enhancing patients’ perceptions of empathy, attentiveness, and courtesy even when AI technologies like IVR are used, improving mental health outcomes.
Further studies should focus on aligning AI technologies with the hospitality culture in healthcare, developing reliable tools for quality measurement of AI services, and exploring consumer perceptions to mitigate disenfranchisement risks and improve patient outcomes.
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