Artificial Intelligence (AI) is growing in the healthcare field in the United States. It aims to improve patient care, make operations run more smoothly, and lower healthcare costs. Since the COVID-19 pandemic, healthcare groups have seen AI as a way to handle uncertainty, help with decisions, and automate paperwork. But even with these benefits, AI use in healthcare faces big challenges. These include high costs, not enough skilled workers, and finding reliable partners. Medical practice leaders and IT managers need to understand these challenges to plan for using AI well.
This article looks at the main problems healthcare providers face when trying to use AI in both care and office tasks. It talks about AI’s role in changing healthcare work, human and technology challenges, and how these affect wider digital changes in U.S. healthcare.
To understand AI barriers, we first need to see why AI is becoming more important. The COVID-19 pandemic pushed healthcare groups to use digital tools faster. Many started investing more in AI to manage patients and daily work.
Greg Nelson from Intermountain Healthcare said the pandemic made people see that AI helps handle unstable situations and supports decisions. Intermountain built an AI Center of Excellence and tested about 80 AI projects. This shows they are working hard to use AI in care and office tasks.
A 2019 survey found that 51% of healthcare providers had started using an AI plan, up from 22% the year before. Also, 57% of healthcare financial leaders say they want to speed up automation spending. This is more than leaders in other industries. These numbers show that U.S. healthcare is focusing more on AI to save money, work better, and help patients.
But despite these hopeful signs, there are still big problems to solve before AI can be used widely and effectively.
One big problem stopping AI use is the cost. Healthcare systems often have tight budgets. They must balance money spent on technology while keeping care good and following rules. Buying AI tools costs a lot at first. This includes buying software, fixing computer systems, training staff, and keeping everything running.
Smaller clinics and offices have a harder time finding money for AI projects than big hospitals. Even big hospitals are careful about spending lots of money without clear proof that AI will pay off in the long run.
Cost problems don’t just stop hardware and software buying. They also make it hard to hire and keep experts who know how to use AI. This creates a cycle where not enough money means fewer experts, which makes AI projects harder to succeed.
A study by Moustafa Abdelwanis and team listed money problems among 16 main barriers to AI in healthcare. They said money limits affect computer upgrades, buying new tech, and hiring staff — all needed to make AI work well.
AI needs people who can watch over it, change it, and fit it into healthcare work smoothly. But many U.S. healthcare groups don’t have enough trained people who know both healthcare and AI tech.
Problems with people are called the Human-Organization-Technology framework by Abdelwanis and others. It includes lack of training and workers resisting AI use. Some healthcare workers don’t want to use AI because it feels new or might add to their work. Also, AI may need special experts to understand its results who are not common in care teams.
Smaller and medium healthcare groups have this problem more because their IT teams are small and busy. Teaching current workers to use AI well takes time and money that some places do not have.
Greg Nelson said AI should help human workers, not replace them. This means healthcare workers need to work with AI, not just follow it. Giving good education and chances to learn about AI is very important but still hard for many places.
Finding the right partners for AI is another hurdle. Many AI companies come from tech backgrounds but don’t know healthcare well. This can cause a mismatch between what AI offers and what clinics need.
Healthcare leaders want partners who are clear, fast in giving answers, and explain their results well. Mark Jackson from Piedmont Healthcare said quick access to insights is a key reason to choose AI partners. Healthcare groups want vendors who can quickly offer and improve AI for their specific care tasks, not just general AI.
Simbo AI is one company that focuses on automating phone work and answering services using AI. These tools help with busy tasks like managing calls, setting appointments, and answering patient questions. Working with companies like this who understand both healthcare and AI can lower risks and help staff accept new systems.
Jennifer Junis at OSF HealthCare said their prior investments in contact centers helped them start a COVID nurse hotline with AI chatbots quickly. This shows that smart, healthcare-ready partners help create fast solutions in emergencies and beyond.
On the other hand, working with unreliable partners or those who don’t understand healthcare can lead to AI systems that don’t work well, confuse staff, or cost more to fix.
How well AI tools fit with current healthcare work is a key factor for using AI. Bad fit can cause slowdowns, confusion, and unhappy staff.
Healthcare work is complex with many roles, steps, and rules. AI that automates office jobs like reminding about appointments, billing, or answering calls must fit well with this work to be helpful.
Abdelwanis and his team said that when AI doesn’t match workflows, it causes problems like staff frustration and delayed care. This means AI makers and healthcare groups must work closely to design and set up AI solutions.
Automating front-office work, like the phone services that Simbo AI offers, is a good example of AI helping workflows run better. Automating routine calls cuts staff work, lowers patient wait times, and lets humans focus on harder tasks.
Healthcare managers say improving efficiency and cutting costs are top priorities. AI automation can help keep income steady by handling appointments and patient communication well. Mistakes or delays in these areas can cause money loss.
Healthcare groups also use Robotic Process Automation (RPA). This tech uses AI to do repetitive, rule-based tasks like billing and claims. Together with AI analytics that predict needs, these tools help providers use resources better and prepare for patient care.
Apart from cost, talent, and partners, healthcare must handle other issues like data quality, ethics, and rules.
AI can be affected by biased or wrong data, which might hurt patient safety and care. Doctors want AI to be clear so they can understand how it makes suggestions before trusting it.
Healthcare also has strict rules that can slow down new ideas or make AI setups hard. Following laws like HIPAA and data safety rules needs constant watch and control.
Good training helps lower staff worries by teaching them how to work well with AI. Ongoing education and feedback can spot problems early and let healthcare groups fix them fast.
Healthcare leaders expect AI investments to make care cheaper, make workers stronger, and boost productivity over time. As healthcare changes to digital and remote care, AI will become more important in helping operations run smoothly and care stay good.
Still, the way forward takes careful work to fix money issues, find and train talent, and build trusted partnerships. Abdelwanis and others suggest a big-picture plan with careful checking, smart steps, and constant watching to keep AI working well.
Healthcare managers and IT staff must carefully check AI tools to see if they fit current work, offer good value, and deliver clear results. Investing in staff education, picking partners who know healthcare, and focusing on automation in operations can make the switch easier.
For healthcare leaders in the U.S., dealing with AI hurdles means more than getting new technology. It needs smart planning about how ready the organization is, costs, training needs, and vendor trust. By focusing on these parts, healthcare practices and hospitals can better use AI to improve care, cut costs, and run more smoothly in the future.
The COVID-19 pandemic has accelerated investment in AI and emphasized its value across healthcare organizations, with more than half of healthcare leaders expecting AI to drive innovation.
57% of healthcare CFOs plan to accelerate the adoption of automation and new ways of working in response to the pandemic.
84% of hospitals have audited their digital transformation state, focusing on software solutions that capture revenue and innovative analytics.
Intermountain Healthcare is developing an AI Center of Excellence to enable enterprise-wide innovation, highlighting the importance of practical AI applications.
OSF HealthCare leveraged pre-existing digital strategies and vendor relationships to quickly deploy AI tools like a COVID symptom-tracking chatbot.
AI is being applied primarily in administrative, clinical, financial, and operational areas to drive efficiencies and improve care.
Cost, access to talent, and the need for reliable partners are common barriers that hinder AI implementation in healthcare.
Intermountain Healthcare develops an ‘AI playbook’ to guide responsible decisions around AI investments, focusing on augmenting human intelligence.
Health systems look for partners with healthcare expertise, speed to insight, transparency, and the ability to explain outcomes.
Healthcare leaders believe technology investments will improve operations in the long run, enhancing cost structure, workforce resiliency, and productivity.
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