AI is helping make health care more accurate and efficient. It can also reduce the amount of work doctors and nurses do. One big benefit of AI is that it saves clinicians time on paperwork and looking at data. For example, The Permanente Medical Group (TPMG) uses AI medical scribes that save thousands of hours each year. This lets doctors spend more time with their patients.
But just saving time with AI does not always lead to better relationships between patients and doctors. In many US healthcare settings, there are both system problems and personal challenges for clinicians. These problems stop them from using the saved time well. Healthcare leaders must understand these issues to make sure AI really helps patient care. This article talks about these problems and how AI tools, like those from Simbo AI, may help.
AI should give doctors more time to talk and connect with patients. This could build trust and improve communication. But in many US clinics, the real situation works against this idea.
Even though AI saves time on paperwork, many clinics keep patient visits very short—usually 15 to 20 minutes. These times are set to meet business goals, not patient needs. As more people have multiple health problems, short visits may not be enough to cover all issues or build good relationships, even if doctors have more free time.
Doctors may feel pressured by clinic managers to see many patients quickly. This causes back-to-back appointments with little extra time for personal talk. Research by Bryan Sisk shows that such business models may lead AI to help doctors see more patients but not spend more time with each one. So, the chance to improve patient relationships using AI often does not happen.
AI gives doctors a lot of data, test results, and treatment ideas. This helps with accuracy but also adds to doctors’ workload in another way. They need more time to explain AI results to patients. Some patients may not trust AI because they don’t understand how it works. The “black box” nature of AI can make patients worried. Doctors then have to spend more time calming fears and clarifying information.
Meanwhile, the short visit times in US clinics make it hard for doctors to explain AI clearly and also build trust. Doctors may struggle to do both well with limited time.
Besides system problems, many clinicians face personal challenges. These challenges stop them from using AI-saved time well for building patient relationships.
Many doctors feel unsure about handling emotional or sensitive talks during visits. Medical training often teaches doctors to stay emotionally distant. This can make them less comfortable dealing with patient feelings, even if they have more time because of AI.
This discomfort affects how well doctors connect with patients. So, even if AI saves time on paperwork, the quality of doctor-patient talks may not improve much unless this issue is addressed.
Many healthcare workers are tired and stressed. This burnout hurts their ability to listen carefully and show empathy, which patients need to trust their doctors. AI tools like ambient scribes can help reduce paperwork, but burnout must also be managed to improve care.
For example, TPMG found that after using AI scribes, 82% of doctors felt happier at work and 84% said their communication with patients improved. AI can help doctors feel better about their jobs. Still, clinics need to support workers beyond just using technology.
Even when doctors have AI tools, they may lack training on how to talk about AI results with patients. It can be hard to explain complex AI outputs or calm patient worries about AI decisions. Good communication skills are needed to use AI well in care talks.
Education should focus more on building empathy, relationships, and patient-focused communication alongside using AI.
Using AI well means balancing efficiency with building good patient relationships. Clinic leaders need to connect technology, staff, and patient care goals carefully.
One area to improve patient care is the clinic’s front desk phone. Simbo AI makes AI tools that automate phone calls. These tools handle patient check-ins, appointment scheduling, and common questions. Automating these tasks saves staff time, lowers wait times, and prevents missed calls.
Smart phone automation helps patients reach the clinic faster. This builds trust even before patients see the doctor. It also frees front desk staff to focus more on personal help during visits.
AI medical scribes listen to doctor-patient talks and write notes automatically. This stops doctors from having to type or write during visits. TPMG found that using AI scribes reduced time spent on paperwork, shortened appointments, and cut extra work done outside office hours. They saved over 15,000 hours each year.
Doctors said AI scribes helped them talk better because they were less distracted by screens. Patients also noticed this. The saved time made doctors happier with their work, which helps them stay engaged with patients.
AI can also automate lab test orders, read scans faster, and organize patient information. These steps reduce repetitive work for doctors. When AI links well with EHR systems, it stops work delays and frees extra time for doctors to focus on patients.
Some doctors find that AI tools do not fit well with their current EHR software. Fixing AI-made notes may sometimes take longer than writing them by hand because of software issues.
Clinics should invest in AI tools that fit their workflow and train staff well. Choosing AI solutions that work smoothly with existing systems helps reduce problems and improve doctor use.
For doctors to use AI-saved time well, training and motivation matter a lot.
Medical schools and big healthcare systems are starting to check empathy and communication skills when accepting new students and during training. This helps prepare doctors to use extra time with patients better.
Continuing education should teach doctors how to explain AI results clearly and kindly. This can help reduce patient fears about AI decisions.
Wellness programs, good work-life balance, and reasonable workloads help reduce burnout. When doctors feel supported, they connect better with patients.
Collecting patient opinions about doctors’ communication and relationships can guide training. Clinics can improve skills over time by focusing on quality as they use more AI tools.
For clinic leaders in the US, adding AI is not just about installing new software. It needs careful planning that balances tech and patient care.
Leaders should think about changing appointment scheduling. They can let doctors use AI-saved time to talk more with patients rather than seeing more patients faster. This might mean renegotiating contracts or trying care models that reward building relationships.
Training doctors to communicate well with AI is important. Front desk staff should also learn to use automated phone systems like Simbo AI’s tools for smooth patient access.
IT managers should make sure AI tools fit well with EHR systems. Providing support and custom options helps doctors accept and use AI better.
Clinics should track data like how much time doctors spend on notes, appointment lengths, patient satisfaction, and doctor job happiness before and after AI starts. This helps clinics adjust schedules and training to make sure AI improves both efficiency and patient care.
AI is changing clinical care in the US. To get good results, clinics must fix both system and personal challenges that stop doctors from using AI-saved time to build better patient relationships. Using AI tools like Simbo AI’s phone automation together with good doctor training, schedule changes, and support can help clinics balance efficiency with care. This fits with the idea from Francis Peabody many years ago: good patient care depends on a personal connection between doctor and patient.
AI can off-load tedious administrative and data analysis tasks, potentially allowing clinicians more time to engage relationally with patients and provide personalized care, enhancing shared decision making and communication.
The key assumptions are that AI will off-load tedious work, clinicians will use the extra time for relationship building, and clinicians have the skills to engage meaningfully with patients using richer data.
AI could analyze vast clinical data faster and more accurately, reduce manual charting through voice recognition, and streamline ordering tests, thereby reducing clinicians’ administrative burden and allowing focus on patient interaction.
Structural barriers like stable visit lengths with increased complexity, business-driven pressures to see more patients, and personal barriers such as discomfort with emotional communication may limit time spent on relationship building.
More treatment options and data increase interpersonal demands, requiring clinicians to educate patients extensively, explain AI decisions (often opaque), and spend more time on shared decision making.
Lack of confidence in handling difficult conversations, avoidance of psychosocial topics, discomfort with emotional presence, and cultural or training emphasis on emotional detachment can hinder trust building.
Through selective medical school admissions emphasizing empathy, ongoing training in communication and relationship-building, addressing burnout, and providing feedback on interpersonal skills to maximize AI benefits.
Healthcare systems might increase patient volume to maximize efficiency gains, reducing individual visit times, which can diminish opportunities for meaningful patient-clinician engagement and trust formation.
Patients may distrust AI due to its ‘black-box’ nature, requiring clinicians to explain and vouch for AI recommendations to maintain confidence and trust in treatment decisions.
While AI can enhance care accuracy and efficiency, preserving the healing patient-clinician relationship through trust, respect, and personal connection remains critical; all stakeholders should intentionally maintain this balance in AI integration.
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