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With artificial intelligence (AI) becoming more prevalent by the day, worries about job loss due to the transformative technology are running rampant.
For example, about 74 percent of US workers agree or are on the fence about the adoption of AI tools leading to job loss, according to a survey of 3,000 employed Americans conducted by Checkr in 2023. However, this might be a knee-jerk reaction. Indeed, workplace and healthcare industry experts are predicting that AI will not lead to significant job loss – but instead will change the nature of the work being performed.
In fact, a 2023 study from consulting firm Forrester found that just 1.5 percent of jobs will be lost to generative AI in the United States while 6.9 percent of jobs will be influenced by the technology by 2030. As a result, workers “should be more focused on how to leverage the technology than on how to compete with it,” according to the study’s authors.
AI might even alleviate staff runover and burnout among health information (HI) professionals, according to a survey of HI professionals conducted by AHIMA and NORC at the University of Chicago. Respondents noted that certain AI/machine learning technologies alleviated staff burnout and overwork including autonomous coding (46 percent), administrative workflow assistance (46 percent), chatbots (32 percent), and healthcare utilization management (30 percent). The majority of respondents also associated these technologies with improved productivity.
Much of AI’s potential lies in its ability to perform repetitive tasks, which is particularly appealing in healthcare. A 2019 study from the Brookings Institution estimates that 40 percent of tasks performed by healthcare support staff and 33 percent of tasks performed by healthcare practitioners have the potential to be automated – freeing workers to zero in on more complex functions. In addition, according to a column published in 2023 by LinkedIn, this “liberation empowers individuals to engage in more stimulating and intellectually challenging work, enabling them to explore their true potential.”
Facing Change
Pranav Rajpurkar, an assistant professor in the department of biomedical informatics at Harvard University in Cambridge, MA, focuses his research on pushing the boundaries of AI in medicine and agrees that clinicians and HI professionals alike should zero in on how AI will transform their work roles for the better, instead of worrying about being displaced by the technology.
"As generalist medical AI systems take on more routine tasks, healthcare professionals will shift towards higher-level decision-making and patient care. They'll spend less time on administrative tasks and more time applying their expertise to complex cases that require nuanced judgment,” Rajpurkar says.
For HI professionals, the transformation could be significant, Rajpurkar says.
"Their focus will shift from managing discrete data points to overseeing complex, AI-driven information systems,” he says. “They'll need to ensure that these systems are properly integrated, that data quality is maintained, and that information flows seamlessly between AI systems and human users. Additionally, they'll be instrumental in developing governance frameworks for AI use, ensuring compliance with evolving regulations and managing the ethical implications of AI-driven decision-making in healthcare."
Clinicians will also need to pivot as AI comes into play. They will act as supervisors and partners to AI systems, interpreting and integrating AI-generated insights with their clinical expertise. Under this work model, clinicians can focus on the aspects of care that require human empathy, creativity, and ethical reasoning.
Relishing Revised Roles
While AI is poised to impact clinicians and HI professionals, coders, in particular, will need to gravitate toward higher-level tasks as AI becomes more prominent and handles many of the routine tasks that traditionally land on coders’ shoulders, says Conrad Coopersmith, general manager of coding automation at AGS Health, a revenue cycle technology and service company in Washington, DC.
Consider the following: AI can automatically assign codes without human intervention by the coder. To accomplish this, AI models analyze voluminous amounts of data to predictably learn from that data over time. The technology makes it possible to bypass human review and increase the speed of reimbursement because workers don’t have to touch the chart.
In addition, AI helps to reduce errors and denied claims, leading to higher levels of reimbursement.
These AI models have the potential to be deployed in all areas of medicine but are currently being leveraged in care settings that have high volumes of patients, large data sets, and established codes such as emergency departments and outpatient medical imaging.
With AI taking care of many basic tasks, the role of the coder will change to an exception-based audit function, Coopersmith says.
“Coders will be able to spend more time looking at and analyzing output versus putting it together. The role of the coder will naturally evolve to more of a focus on quality and audit versus the initial task of organizing information for coding itself,” he says.
Training to Succeed with AI at Your Side
HI professionals will need to educate themselves differently as their roles evolve in the age of AI.
"To prepare for these new roles, healthcare professionals should focus on developing skills that complement, rather than compete with, AI capabilities,” Rajpurkar says. “This includes honing critical thinking, ethical reasoning, and complex problem-solving skills. They should also strive to understand the basics of AI and machine learning, not to become AI engineers, but to be informed partners and supervisors of AI systems. Continual learning will be crucial, as will the ability to adapt quickly to new technologies and changing clinical paradigms. Interprofessional collaboration skills will also be vital, as generalist AI systems will increasingly bridge traditional disciplinary boundaries."
Coopersmith recommends that healthcare organizations and educational institutions prepare HI professionals to succeed by concentrating on real-world scenarios that include stakeholders from across the organization. “The best way to prepare a team to handle more complex higher-level tasks is to pick an actual use case, implement it, and then iterate on it,” he says.
In addition, Coopersmith contends that educational initiatives need to include change management as well. “We have to be able to show the health information professionals who are directly affected what's actually happening and how they can best transition from the current state to the future state,” he says.
John McCormack is a Riverside, IL-based freelance writer covering healthcare information technology, policy, and clinical care issues.
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