About

➀ Read and Learn

The following course content

Introduction   

With the advent and advancements in technology, it only makes sense that healthcare would continue to evolve into using Artificial Intelligence (AI) to assist in understanding and caring for patients. But what are the pros and cons of using this technology for the nurse?    

In this course, we will describe AI and begin to understand how this ever-evolving strategy can be used in healthcare. We will also describe how it will impact the care delivery process of our patients. We will attempt to put a timeline on its integration in facilities, and its potential impact and outcomes for our patients.    

What is Artificial Intelligence (AI)?  

AI is an area in computer science that mimics thoughts and actions that would normally originate from humans (1). It functions to allow machine learning of tasks and algorithms imputed into a computer by staff and others (3). A technology writer defines AI in this way: “Artificial Intelligence encompasses the technique used to teach computers to learn, reason, perceive, infer communication, and make decisions similar to or better than humans” (6). 

In some facilities, nursing documentation software is already in place using the patient’s medical record to add information and document a patient’s visit and/or needs. The software is also useful for providers and other disciplines to access information in a timely fashion rather than scanning through a written medical record. This software can also aid nurses in documentation by selecting check boxes instead of writing notes, which allows for less human error and a better ability to read the medical record. AI is being incorporated in many different facilities and departments (6).  

In the early days of AI, simple algorithms were developed through imitating how humans use reasoning and deduction to solve problems (2). As scientists continued to develop AI, more complex algorithms are being produced. You may already use some form of AI, but research shows that at some point in time AI will enable nurses to experience new ways of performing nursing functions. One of its many functions is how information from AI can be helpful in identifying high risk patient discharges, and patients who may “slip through the cracks.” 

To simplify, AI “is the ability of a machine to emulate intelligent human behavior and, represents an umbrella term for machine learning (ML), computer vision, and natural language processing (NLP) technologies.” These three can be used individually or together which adds to the machine’s intelligence (2). There will always be a decision tree (or algorithm) for each piece of technology.  

Timeline of AI Use in Healthcare  

Using AI in healthcare is very costly and complicated, so it is important that institutions make informed decisions on how it will best be used. Short- and long-term goals must be developed to ensure the best, most accurate devices are purchased.  

The first step in developing appropriate AI is to engage the stakeholders and first-line users into a multidisciplinary team (7). This team should include all persons involved as end users of the technology and software. Staff would include healthcare staff, doctors, nurses, administrators, ancillary and financial staff, and even patients (7). Once the team is determined, certain questions must be asked, such as “what is working in the facility,” “what is not working,” and “why.” Once established and agreed upon, the questions that must be asked is “what problems can AI solve” and “how long will it take to develop.” 

Nurses are extremely important stakeholders as they are the direct patient caregivers. It is imperative that other members of the team understand how nurses perform the care and document findings, vital signs, and treatments. This will ensure that the appropriate AI machines are considered. To that end, timelines will be different in every facility. A long-term goal of AI might be to have robots perform vital signs and documentation, which in some facilities are already in use.     

One article describes timelines for implementation in the following manner: (7)  

AI today: AI machines cannot reason the same way as humans. They can only translate patterns from data sets. Facilities are beginning to adopt AI to automate high volume repetitive tasks, which are very time consuming. However, significant progress is being made in the areas of precision diagnostics, such as with radiotherapy and diabetic retinopathy.  

In the next five to ten years: AI will make significant progress developing efficient powerful algorithms and healthcare systems will move to becoming co-innovators with technology partners.  

In the long term (over ten years): Systems will become more intelligent and allow healthcare to shift from one-size-fits-all to preventive, personalized, data driven disease management, which will improve patient outcomes (7). 

Impact on Nursing Practice  

AI is used in clinical decision processes as machines can gather information from many sources to integrate with electronic medical records giving nurses more accurate, up to date information about their patients (1). It is imperative that direct care nurses be involved in the process of choosing which algorithms and machines work best for their practice in the venue they are employed. For example, AI may be used to perform some tasks in difficult situations, as we experienced during the COVID-19 pandemic, when donning and doffing personal protective equipment was necessary. If nurses were able to speak with the patient virtually, it might have been more efficient, giving the nurse more time to care for all patients and use personal protective equipment (PPE) less often (1).   

One study developed certain algorithms within machine learning using initial nursing assessments to predict survival or mortality in critical care patients. The study showed there is a high level of predictability for outcomes (1).    

High Quality Medical Care is Expanded  

AI can enable high quality medical care by reducing rates of errors and providing increased personalization and monitoring. This tool can also reduce healthcare costs and allows the nurse more time to perform direct patient care (8).  

Medical Record Improvement  

Since the use of Electronic Medical Records (EMRs), many disciplines are now digitalizing results of testing, radiology, and any other results or information. AI can enable more organization of EMRs which will then allow staff to see medical history and testing results in a timelier manner. This will provide easier access to patient information and enable providers to treat a patient more quickly (8).      

Quality of Service Improvements  

“AI can be used to improve the quality of service in terms of efficiency, safety, and access to healthcare through collaboration coordination and communication between healthcare disciplines” (8). To that end, AI can help reduce the workload for all disciplines in health care.  

Impact on Patient Outcomes  

AI promises to increase patient population health by providing doctors with health histories, progress updates, and other patient information to help fill gaps in care and identify high risk patients (8). Providers can access information more quickly, allowing them to focus on each patient’s needs in a timelier fashion.  This may allow providers more time with each patient to identify and recommend actions, stratify patients for the best outcomes, and improve quality of care (8).  

Patients will benefit when nurses are able to spend more time at the bedside or in the provider’s office, gathering information in the electronic medical record quicker, rather than surfing through written medical records. This will assist the nurse to know the patient’s history and perform more quality education and treatments for the patient as the information is right in front of them on the computer. Electronic medical records can also assist the nurse in determining the acuity level of each of their patients. This process will help nurses triage their patients to determine who to care for first. “These solutions are streamlining diagnostic and treatment processes, enabling focus on quality care, and offering innovative solutions to relieve an overburdened system” (8). This can accomplish better outcomes and quality of care.  

Implementation Strategies  

Aside from documentation in medical records, we have already seen some implementation of AI.  Many facilities are now using AI to assist surgeons using robotic-assisted surgery (3). As mentioned previously, it is important to understand a facility’s needs and constraints. One might ask “what is the problem, why is it a problem, to whom does it matter, and why is it not getting the attention it needs” (7). Once this information is taken into consideration, it will be necessary to determine which problems AI should solve (7). After making this determination, it will be necessary to understand if the appropriate data sets are available or can be built and later evaluated for providing the solutions the provider or facility needs (7).  The more AI develops to justify its use in healthcare, the sooner it can be implemented in clinical and financial applications, which can save nursing time and healthcare costs (9). 

Special Considerations  

The goal of AI is to lower costs and provide safer, higher quality patient care. “Powerful computing capabilities; large amounts of data; and the development of techniques such as clinical, operational, and behavior analytics can be combined and leveraged for personalized and population-based care” (2). While there are many positives to using AI in healthcare, there are also concerns. There could be vulnerability related to research and education. If the data that exists in a machine is biased or falsely imputed, it may be difficult to ascertain the truth of what was documented (4). If researchers are falsely claiming their results and mishandling information to gain federal funding, again the information may be rushed and therefore skewed, causing issues for the hospital, physicians, nurses, and patients (4).    

To illustrate this, one study found that clinical decision algorithms prioritized treatment for patients who were Caucasian and not as ill over patients who were African Americans and more ill. While situations like these do occur, AI remains useful for all patients disregarding race, gender, or other differences. (8). An example is self-driving cars. The concern is that there is a potential for a less than perfect success rate. If a self-driving car decides to swerve left or right, it may cause an accident. This suggests that inappropriate information was somehow implanted in the machine (5).   

When it comes to patient care, it is extremely important that we know the machines are 100% accurate before we are totally comfortable using them (5). Another concern is when an error occurs when replacing humans with AI technology. In this case, the concern lies in the uncertainty of legal accountability and responsibility. It is important to remember, AI can also present problems as algorithms may not be valid for standardizing care. 

Future of AI  

As AI technology continues to advance, healthcare will see a future that is more precise, predictable, personal, and portable (7). The challenge will be in how healthcare systems adapt to the changes brought on by AI and its machines. We believe that AI will give time back to nurses who are consistently working on understaffed units, allowing them to spend more time with patients and families (7). In essence, AI could transform healthcare by “leveraging a globally democratized set of data assets comprising the highest levels of human knowledge to work at the limits of science to deliver a common high standard of care, wherever and whenever it is delivered and by whom (7). 

The past ten years of computer science has been helping facilities digitalize medical records and billing and reimbursement (7). The next ten years will focus on the value and insight gained from these digital assets by attempting to translate this information so that they provide better clinical outcomes. It is also important that the workforce is educated in a comfortable and supportive environment so that they are not intimidated by AI. In the future, as nurses, we may see robots performing such skills as taking and recording vital signs and performing some minor nursing treatments (7). 

Conclusion

AI is fast becoming part of the healthcare system in the United States and other countries. We have already seen its use in electronic medical records, in our homes controlling our thermostats, and in electric cars. The future of AI is exploding as new machines, software, and algorithms are created. AI is also learning how to speak other languages and translate them. We now have voice and facial recognition on our smart phones, computers, and tablets. We can also talk to our phones and ask them to perform tasks such as dialing a phone number, looking up information on the internet, and translating language.  

As stated previously, nurses have been overworked and understaffed for many years. This has caused burnout and led many nurses to look for work in other venues. Wouldn’t it be nice if we were given the time back to do an even better job of caring for our patients? My wish is to have AI perform easy functions to allow nurses the ability to spend more time caring for the whole patient rather than just the illness.  

➁ Complete Survey

Give us your thoughts and feedback

➂ Click Complete

To receive your certificate