How AI and automation can help clinicians practice at top-of-license

Doctors and nurses want to be with patients. They want to be caregivers. The problem is that they are tasked with too many other things that make it difficult for them to work at top of license.

Top of license work has long been an aspiration of our clinical workforce. We want all of our clinicians’ education, training, and expertise to be put to use. We want to steer their work and workflows away from things where all of that training and expertise doesn’t add any value.

Advisory Board’s research on top-of-license work for nurses and care teams across the past decade has consistently said the same thing: there are competing and consistently escalating demands on clinicians that make it hard to practice at top of license. The reality is that we need clinicians practicing at top of license because care complexity continues to increase in terms of the number of conditions, comorbidities, medications, and providers a patient has.

"Fatigue rarely comes from the specific thing people are supposed to be or want to be doing, like patient care. It comes from all of the additional [things] we ask them to do, typically administrative, not top-of-license stuff."

- Health system executive

At the same time, the administrative burden on clinicians is enormous. “Too many bureaucratic tasks” is the top contributor to clinician burnout: nearly three out of five physicians say it is the biggest contributor to their feelings of burnout. That’s not a surprise: between one-third and one-half of physicians’ time is spent reviewing medical records and writing notes.

Unfortunately, most health care organizations see hiring more staff as the only way to address the complexity of care and to relieve the administrative burden. The reality of provider finances and labor markets is that they can’t add staff—they can’t afford it, or qualified candidates aren’t available, respectively. Nine out of every 10 nurses say that they have considered leaving the nursing workforce, mostly because of staffing constraints.

Advisory Board has literally written the book on care-team redesign, and that undertaking is an important step in understanding and advancing top-of-license practice. But given the increasing complexity of the issue, it’s not unreasonable to think that health care has reached a point where clinicians simply can’t practice at top of license without a new approach to the problem.

Artificial intelligence excels at repetitive tasks that humans usually perform as the connective tissue between silos of data and accountability. The solutions available incorporate many different kinds of technology—voice recognition, predictive classifiers, natural language processing, and computer vision are relatively common—which can then be layered on top of robotic process automation to create adaptive and resilient applications. These solutions can do repetitive tasks, but they can also learn and be taught to do them differently and better as needs change.

There’s a pervasive misconception in health care that any implementation of AI is effectively handing over processes to an unaccountable, inscrutable bot that will ignore outliers and boss clinicians and staff around. We’d never hire a person like that, so why would we hire a computer to be that way?

A clear understanding of both AI’s potential and its limitations can help health care leaders think about its uses in ways that will drive results. Health care has begun to embrace AI across the care enterprise to make business processes like revenue cycle and human resources more efficient. Those are worthy and necessary investments. But they do not help address the fundamental risk to both mission and margin that clinician burnout poses.

Artificial intelligence or automation—of any kind—in clinical care can be a sensitive subject. Neither providers nor patients want to lose the human connection of delivering and receiving care. The reality is that health care must deploy these technologies because, if for no other reason, clinicians are tired and must have more robust support.

Scroll down below to discover the three ways AI is already working to enable top-of-license practice:

1. Help clinicians add value to patient interactions.
2. Reduce clinical documentation demands.
3. Make accountability more than just scolding clinicians into compliance

To practice to the full-extent of one’s education and training—making the greatest contribution each individual can make—rather than spending time doing non-value-added work.

AI is a broad term, representing our intent to build humanlike intelligent entities for selected tasks. The goal is to use fields of science, mathematics and technology to mimic or replicate human intelligence with machines.

AI comprises components such as machine learning, neural networks, computer vision, natural language processing, sensors, human-AI interactions, planning and reasoning, and autonomy. ¹

AI-enabled process automation.

Intelligent automation combines adaptive elements of AI like predictive classifiers, natural language processing, and computer vision with execution-focused capabilities of RPA to perform repetitive, logic-based tasks.

It uses AI capabilities to take in and act on structured and unstructured data in a human-like manner and learns from historic data to improve accuracy and efficiency.