Introduction

News of technological advancement is everywhere. Across news, social media and the internet at large, talks of new technology swell and collapse on readers, overwhelmed by the cascade of information. Even we've been talking about it — in our recent discussion on planning for next year and our articles on innovation and AI last year.

It can be easier just to turn your mind away from it, and focus on the tasks in front of you. It's easier to disengage and just let it float past you and maybe, maybe, it'll make an appearance down the line in your profession. Or maybe you can ignore it forever.

It's easy as a nurse to believe yourself invulnerable to technological change. The rate of change can be slow in healthcare and governance can take a long time to put in place. Meanwhile, you've got patients to care for or staff to lead and the less fiddling around with the damn EHR you can do, the better.

The reality, however, is that the nursing profession is changing. And faster than you'd believe. And it's due to technological change, which is already deeply embedded into so many processes.

The nursing profession shouldn't idly accept technological change passed on to it. This stance only leads to sluggish adoption, ineffective learning, and resistance. Rather, nursing leadership must actively engage in conversations about healthcare's technological evolution. Nurses are poised to be primary agents of change and play an active role in shaping the future of care delivery. They are not there just to applaud progress but also to be critical evaluators, screening new technologies for potential risks to staff adoption or patient care.

But to ascend to this role of technological change agents, nursing leadership must first understand upcoming innovations. By delving into the challenges and benefits of emerging tech, they can form informed opinions, make sound decisions and involve themselves in wider conversations. In the following sections, we'll investigate particular technologies that are shaping the nursing profession, exploring both the potentials they offer and the concerns they raise.

Clarifying Artificial Intelligence (AI) and Machine Learning (ML)

As discussed in our previous article around AI, it can be defined as follows, according to the Australian Government Department of Industry, Science, Energy and Resources:

"AI is a collection of interrelated technologies that can be used to solve problems autonomously and perform tasks to achieve defined objectives. AI is more than just the mathematical algorithms that enable a computer to learn from text, images or sounds. It is the ability for a computational system to sense its environment, learn, predict and take independent action to control virtual or physical infrastructure."

In short, it's about making machines more humanlike in their capabilities and the tasks they can complete. It's worth noting that ML is a separate, but related technology, defined as a branch of AI that utilises advanced algorithms to analyse large amounts of data and then make informed decisions.

Previously, we discussed how artificial intelligence was utilised to predict COVID-19 spread and development of new pharmaceuticals. These are all revolutionary widespread usages of AI within healthcare generally that will change how we approach potential future pandemics and how drugs are developed.

But how will AI influence a nurse on the ground? What skills will they need in the coming years to ensure they can handle these systems to improve patient care, rather than create interference and detriment?

AI and the Electronic Health Record (EHR)

The first of these could be around future advances in the EHR. The EHR stores plentiful amounts of data on a patient. This could be understood via ML and then engaged by a nurse, be it via text or a voice interface, to recall information quickly and efficiently.

Imagine:

• Confirming quickly, via voice, the allergies your particular patient may have, or the correct timing for medication, the same way ‘Hey Google’ or Siri is currently used.
• As a heftier task, the EHR could understand all of the content inside the system and make concise summaries, understanding the theme of a patient's journey, and communicate this to a nurse — even going so far as giving a warning for a high risk of sepsis.
• During discharge, AI could determine whether any steps are still outstanding and notify a nurse to action them.

This is a new level of fluidity of information transfer that doesn't take much to be seamlessly embedded into existing process.

The main fear, however, is that this depersonalises the patient experience. In the most extreme case, this could lead to reliance on technology over actually checking, speaking to and engaging with the patient. The data input into the system is also critical — trash in, trash out, as is said — and if there are errors in the data received via a system such as My Health Record then these problems are exacerbated by the technology.

A balanced approach needs to be taken to embedding AI systems into the EHR — one that looks to utilise the powers of these tools to get nurses up to speed with their patients' challenges faster, but is still patient-centred and focused on direct contact, communications and interaction with patients to validate information and provide them comfort and assurance.

AI and alarm management

Alarm fatigue is a common problem amongst nurses. This is when, due to the huge amount of alarms experienced every day (from 150-300 per bed per day for some hospitals, with the majority being false alarms), nurses become desensitised and can then potentially miss critical clinical alarms.

AI reasoning models can be developed which would allow effective prioritisation and arbitrage of these alarms. This would be done by algorithmically clustering particular alarms and then shrinking these into a singular alarm that can then be actioned. A study at the Royal Adelaide Hospital showed that the alarms were reduced by 99.3%, a significant reduction that should allow for more time caring for patients and less time managing false alarms.

The fear may be however, that the algorithm, like a human, can 'miss' alarms or simply deprioritise ones which could be life-threatening. Although validation of the system and algorithm would definitely need to be scrutinised and tested, it is highly likely that the AI system would do better at prioritisation than a human pressured by a cacophony of alarms from medical devices.

Zero User Interface (UI) solutions

As previously mentioned, voice, and touch, could be the future of how nurses engage with digital systems in the future, rather than fiddling on smartphones or heading back to their computer.

Natural Language Processing (NLP), the ability for machines to understand written or spoken text, has come a long way. Google, Siri and Alexa are all sitting comfortably in many peoples homes, but the challenge for healthcare is these tools would struggle to understand the complex medical jargon the on-the-ground nurses and physicians use. However, medically-oriented NLP tools are well on their way, with VEVA, the Vanderbilt EHR Voice Assistant, developed at Vanderbilt University Medical Centre, showing just how a nurse or physician could engage with an EHR via voice. You could use this to understand patient data or even just ‘converse’ to gain insights and solve patient issues.

Many may fear that the machines are just telling us what to do, but really they are really just structuring the often messy pool of EHR data into information that nurses and doctors can combine with their knowledge and practical wisdom to translate into better patient outcomes.

Conclusion

The world of nursing is changing fast, and a lot of it is thanks to technology. From AI to NLP, there are new tools popping up everywhere. The skills nurses need today are different from before. With roles like nursing informaticists on the rise, it's clear that there's a space for those who understand both nursing and technology.

But while these tools might make some jobs easier, they don't replace the heart and soul of nursing. We can't forget the personal touch. Machines can help, but they don't replace the care and understanding that a nurse brings.

Leaders, learning and development professionals, and nurse educators must shape this new era of nursing. Training must evolve to include not just the latest technology but also understanding how ethics and empathy intermingle with it.

If you’re desiring more knowledge around technology in healthcare to equip yourself and your teams for the future, head to our 2-day conference Ausmed Elevate ‘23. We’ll be hosting Aidan Roberts, CEO and Co-Founder of SimConverse, at the forefront of AI in healthcare, who’ll be talking about how we reimagine clinical education in the age of AI. Register now.

References

• AI vs. Machine Learning: How Do They Differ? | Google Cloud (2023). Available at: https://cloud.google.com/learn/artificial-intelligence-vs-machine-learning (Accessed: 15 August 2023).
• HIMSS21: How NLP Could Be a Game Changer for EHR Workflows, Technology Solutions That Drive Healthcare. Available at: https://healthtechmagazine.net/article/2021/08/himss21-how-nlp-could-be-game-changer-ehr-workflows (Accessed: 15 August 2023).
• Fernandes C, Miles S, Lucena C, Cowan D. Artificial Intelligence Technologies for Coping with Alarm Fatigue in Hospital Environments Because of Sensory Overload: Algorithm Development and Validation. J Med Internet Res 2019;21(11):e15406. URL: https://www.jmir.org/2019/11/e15406. DOI: 10.2196/15406