Healthcare workers have always held challenging roles. However, in the last few years, it has been increasingly difficult. Understaffing, challenging work conditions, and more have plagued the industry, not to mention a brutal pandemic. Luckily, thanks to technological advancements, it’s possible to address those problems in a variety of ways.

 

Fixing these issues isn’t as simple as throwing around money and technological jargon. Instead, it’s necessary to first identify each issue and then look at how specific technological platforms or tools could help fix – or at least mitigate – the problems.

 

There are six major issues facing healthcare workers today. Thankfully, artificial intelligence and data science can help to address these issues and improve working conditions for healthcare workers.

The top issues in nursing

 

Nurses are essentially the front lines in our healthcare system, and most of them have the battle scars to prove it. Many struggle with the physical and mental toll of long shifts, overnight, weekends, holidays work shifts and overnight work. Studies show that these kinds of schedules and working conditions can lead to burnout, fatigue, and other negative health outcomes.

 

For instance, nurses who work long hours or overnight shifts often experience disrupted sleep patterns, which can cause them to feel drowsy, irritable, or forgetful. This can cause decreased job performance and a higher risk of making errors, which can be dangerous for both nurses and patients. Disrupted sleep patterns can also cause long-term health problems, including heart problems, obesity, and diabetes. The International Agency for Research into Cancer (IARC) has even listed night shift work as a probable carcinogen.

 

Nurses often deal with critically ill patients and have to make quick decisions that can impact patient outcomes. During any given shift, nurses can be running from one crashing patient to the other. This, of course, can cause heightened stress levels among nurses and other healthcare practitioners. Studies have also found that nurses are at greater risk of PTSD, especially as a result of the pandemic.

 

Also, many healthcare facilities are understaffed, which can lead to nurses being overworked and unable to provide adequate care to their patients. Research demonstrated that this chronic issue was made acute during the pandemic, especially in rural or low-income areas throughout the United States.

 

Of, course, During the pandemic, healthcare workers were lauded as heroes. In 2020, healthcare workers received moments of silence, flyovers, and occasionally a 10% discount in certain stores. But they also bore the brunt of misguided attempts to target healthcare workers for grievances that members of the public had about the lockdown, masking requirements, and erroneous rumors that the pandemic was fake.

 

But even beyond the impact of the pandemic, and despite the important role that nurses play in the healthcare system, they often receive limited recognition and respect, which naturally can lead to job dissatisfaction. Nurses report that recognition goes a long way, but that they rarely receive that recognition.

 

Aside from the lack of general societal recognition, nurses frequently don’t earn what they’re due, either. Nurses are often paid less than other healthcare professionals, and may not receive adequate benefits, such as paid time off and retirement plans. Finally, many nurses feel that their career prospects are limited, with few opportunities for advancement or growth within the field. Black nurses in particular face discrimination and a lack of mentorship.

 

How AI and data can help

 

That’s a long laundry list of issues. How on earth can concepts as seemingly vague as “AI” or “data science” help mitigate these serious challenges that have long been embedded in healthcare?

 

One thing is clear: technology can’t replace nurses. Nurses perform vital functions that simply cannot be done by technology, no matter how intelligent. But AI can analyze, optimize, and orchestrate information in a way humans with spreadsheets find difficult and frustrating.

 

Staffing Matrices

 

Artificial intelligence can not administer a patient’s IV medications or provide psychological comfort to patients. What technology can do, though, is optimize staff schedules. In an ideal world, nursing staff would be deployed based on the perfect mix of availability and patient acuity.

 

But right now, staff members are often deployed based on a Charge Nurse’s subjective assessment, or on inconsistently applied vacation request rules. This leads to overall nurse dissatisfaction and units not being supported adequately.

 

In contrast, AI can be used to automate nurse scheduling, taking into account nurse availability, workload, patient needs, and past experience to create an optimized schedule. This can help reduce the administrative burden on nurse managers and ensure that nurses are assigned to the right unit at the right time.

 

Artificial intelligence can do more than just filling out a spreadsheet, though. AI’s strength is in pattern recognition and prediction. By looking at historical data, AI can predict future patient acuity and determine optimal staffing levels in real-time.

 

High stress

 

If you’re a nurse manager, it’s important for you to know how your nurses are holding up at any given point. How many patients are they dealing with? How many of those patients require additional attention? How many equipment alarms have they dealt with? How many of those alarms were false positives? What interventions did a nurse have to complete, and how complex were they?

 

All these factors make up a nurse’s cognitive load, which affects how stressed they are. It’s no secret that nurses who work with critically ill patients can suffer from extreme stress and anxiety. Unfortunately, stressful, fast-paced environments are inherent in Critical Care. AI helps lessen stress.

 

Toward this end, there are many environmental factors that facilities can track to get a sense of the stress levels that nurses are under. Patient mortality rate, patient acuity, and even unit noise levels are all proven approaches to measuring job intensity and the resulting stress.

 

However, this data isn’t all in one place but rather spread widely among a variety of different machines, portals, and systems. A human could aggregate all this data, stick it into an Excel spreadsheet, and analyze it, but most hospitals just don’t have the staff to spare for this, and certainly not in real-time.

 

Again, we go back to AI, which can aggregate this information in a meaningful way, identifying when nurses need to catch a much-needed respite. Humans can also aggregate this information, and indeed some nurses have managed to keep up just with spreadsheets. But most nurses are too busy helping patients to take the time to update whether an alarm they attended to was real or a false positive. Machine learning models are capable of ingesting all this information and producing recommendations as circumstances change and based on how each nurse is doing.

 

Understaffing

 

Despite the fervent wishes of most hospital leadership, it’s not possible to simply generate more nurses overnight. There are organizations out there doing their best to attract and recruit people into nursing programs, but forecasts indicate that nursing shortages will last for years to come. Although it is vital to continue to educate and recruit more qualified people into the nursing profession, we still need to look at how we can deliver care in more appropriately efficient ways, using all of the available tools.

 

This 2019 study is just one of many describing how doctors and nurses feel that a large portion of shift time is wasted on non-essential or non-medical tasks. Now, you could ask every single staff member in a hospital about their opinions on time wasting, collate that data manually, and reissue schedules to avoid that issue. But there’s a reason most hospitals don’t do this – it’s time-consuming and inefficient.

 

AI technology can aid in identifying and eliminating this wasted time by better tracking this information in the electronic health records and in the medical devices that healthcare professionals use. With advanced analytics, this information can generate measures of work efficiency that can inform staffing schedules, identify optimal care windows, and enable benchmarking.

 

Lack of recognition

 

Nurses are leaving the profession in droves because they feel a lack of support and recognition. This directly impacts increased costs to hospitals, which can create understaffing conditions.

 

This is an area where AI can have a real, direct impact. Not everything can be measured on paper, but there are many aspects of work that can be tracked, highlighted, and most importantly, rewarded. AI aggregation can generate data available to better highlight and recognize the real work that nurses do.

 

For example, a nurse manager might happen to see that a particular nurse has been working long hours, taking on heavier, more complex assignments, and dealing with a greater proportion of environmental noise than usual. That kind of work might be missed in the general hubbub of hospital activity. A data-driven platform could specifically highlight overworked nurses, letting management celebrate their achievements – and give them a well-deserved break.

 

Inadequate pay and benefits

 

Hospital labor costs have significantly increased during COVID and post-pandemic. Yet nurses do not feel they earn a fair wage. So where’s the money going?

 

It’s all part of a vicious cycle. Nurses are exhausted and underrecognized. They burn out. Hospitals are forced to hire traveling nurses at a much higher price point. There’s less in the budget for the remaining nurse salaries. Those traveling nurses are also underpaid and overworked. They burn out and leave. The vicious cycle continues.

 

AI can’t manufacture dollar bills to reward nurses, but it can help prevent attrition. Any time that a nurse manager uses data to spot an individual at risk of attrition and prevents that person from burning out, the hospital saves money, which can then be used to properly remunerate permanent nurses.

 

Limited growth opportunities

 

Nurses are often resistant to new technology and techniques, but not because they are not tech-savvy.. It’s simply that they’re already working with enough complex, technical equipment and technology and don’t exactly relish the idea of adding more software to manage.

 

Thankfully, AI-powered technology is easy to understand. New dashboards that have the look and feel of an app on an iPhone, can help nurses make better use of new technology. Instead of being asked to take in vast quantities of new information, AI-powered learning can help nurses interact with new tools and techniques with intelligent highlights, and smarter lessons while teaching new skills.

 

Final thoughts

 

It’s clear that nurses do some of the most important, yet most undervalued work in our society. There’s no direct replacement for the work of the registered nurse. The good news is artificial intelligence can support nurses and hospitals like never before with data collection around patient acuity, scheduling, and workload. It can analyze that data in real-time, ensuring nurses are utilized in a way that is most optimal both for the hospitals and the nurses.