Nursing CE Central
Artificial intelligence (AI) has massively gained notoriety with the popularization of tools like ChatGPT and Midjourney. Major industries are scrambling to figure out how to best put AI tools into practice. Healthcare first introduced artificial intelligence tools in the 1970s and these tools have continued to evolve.
Nursing CE Central predicted artificial intelligence as one of the major healthcare trends to watch out for in 2023, as a way to delegate tasks and allow care providers to spend more time with their patients. Many companies are taking notice and partnering with hospitals to integrate AI tools and virtual nurse platforms.
One popular artificial intelligence and virtual nurses in healthcare facilities is Care.AI, which is currently utilized by over 1500 facilities. Care.AI recently partner with Portneuf Regional Medical Center (PMC) and introduced its virtual nursing platform to PMC’s surgical unit in late July of this year. This new partnership provides a current example of how artificial intelligence is currently being implemented in healthcare facilities.
AI and virtual nurse platforms like Car.AI hope to support care teams and create better outcomes for patients. However, reception to artificial intelligence has been divided as many point out security risks and biases impacting the quality of care for patients.
For healthcare professionals like nurses, ensuring their work is efficient, accurate, and ethical is paramount for the patients and communities they serve.
Early artificial intelligence (AI) such as the first chatbot was developed as early as the 1960s. At the same time, medicine was digitizing data and developing the first web-based search engines like PubMed.
1976 brought the first application of AI in medicine with CASNET, a consultation program where a care team could apply information about a specific disease to individual patients and provide physicians with advice on patient management. CASNET was developed at Rutgers University and demonstrated for the first time to the Academy of Ophthalmology.
Some people may remember Watson, IBM’s open domain question answering system who competed on Jeopardy! in 2011. Watson utilized DeepQA, which generated probable answers from analyzing unstructured natural language data.
In 2017, Watson identified five new RNA-binding proteins being altered by amyotrophic lateral sclerosis (ALS). Researchers claim it would have taken years to individually examine the 1,500 RNA proteins, showcasing how important AI like Watson could be for medical research.
Artificial intelligence has only grown more advanced with the advent of Deep Learning (DL) which can detect lesions, create differential diagnoses, and compose automated medical reports. Research on artificial intelligence in medicine only continues to grow with over 12,000 publications citing deep learning, over 50,000 machine learning, and over 100,000 pieces of scientific healthcare literature with artificial intelligence.
In nursing, AI can be considered another clinical decision support tool. By pulling data from electronic health records, clinical practice guidelines, previous notes, and test results, AI can screen a great breadth of data to provide nurses with specific predictions/suggestions.
Another data source for AI in nursing is sensor-based technologies which can measure body movement, weight, movement, and environmental (temperature, light, sound, air quality) data. These sensors became highly popular during COVID-19 and the rise of telehealth; however these sensors may also be utilized in healthcare facilities to aid AI.
Portneuf Regional Medical Center (PMC) has stated that they are slowly rolling out Care.AI’s tools and virtual nursing platform in phases to ensure they’re being implemented in a meaningful way.
The virtual nurse platform provided by Car.AI is comprised of several technologies working in tandem: autonomous monitoring sensors, two-way audio/video conferencing, voice activation, multi-stream video calls, and automatic call transcription.
Care.AI markets its platform as having “a second nurse in the care suits” providing assistance to the in-person nurse by managing tasks like documentation, and routine monitoring.
The CEO of PMC, Jordan Herget made the following statement regarding integrating this virtual nurse platform during an ever-increasing nursing shortage:
“At Portneuf, we are deeply committed to supporting caregivers and helping our teams improve the quality of care we provide. Now more than ever, retaining passionate, highly skilled caregivers and improving the work environment for frontline team members is critical to the success of any hospital. By integrating this AI-powered technology into our care delivery model, we will relieve some of the administrative burden carried by nurses and ensure our team members are able to continue providing the best care for patients while also taking care of themselves.”
The virtual nurse platform was rolled out in the first week of August. These nurses take on duties including the patient admission process, obtaining a patient’s medical history, and documenting the visit.
PMC has not yet implemented the autonomous AI sensors, but they plan to do so by the end of this month. These sensors include:
Technologies like the one provided by Car.AI are innovative steps toward creating the most effective healthcare team but may introduce new challenges to nurses who are already overburdened.
The United Nations High Commissioner for Human Rights, Michelle Bachelet has stressed that AI used without proper safeguards poses “a serious risk to human rights” such as privacy, health, education, etc.
Security is one of the primary concerns with AI, and that issue is only more pronounced in healthcare settings where a patient’s information must be protected. For example, consent must be gained for personal information or biospecimens to be used in research, regardless of the data’s identifiability. Who in a healthcare facility is responsible for educating patients on the ramifications, both positive and negative, that may arise from sharing their data for AI learning?
Asking for consent is a best-case scenario with artificial intelligence, but one that is occurring less and less. Google is currently accused of training its AI systems with vast amounts of unauthorized personal information and copyrighted material. Several similar cases have been leveraged at Meta, Microsoft, and OpenAI.
These companies are accused of using protected information to train AI tools and profit from them. It is important to question how companies bringing AI tools to healthcare are gathering and protecting patients’ data.
Medical errors are another challenge AI may present in healthcare environments. It is very easy to trust AI technology as infallible and unbiased; however, these tools are taught using human data which is vulnerable to error.
Healthcare teams need to be aware of potential discrepancies in care and understand where they may be coming from. Multiple studies have found examples of these concerning AI errors contributing to patient outcomes. Nurses have the most direct role in a patient’s care and that role necessitates compassion and empathy. This puts nurses in a perfect position to work with healthcare facilities that are implementing AI tools to ensure that every patient receives the highest quality of care.
As virtual nurse platforms like Care.AI continue to grow in popularity nurses and other healthcare professionals will have to evolve to incorporate these new technologies. It is vital that nurses understand how the technology they use is being developed, the benefits it can offer them, the challenges it may introduce, and when they need to trust their human judgment.
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