The Impact Of AI On Healthcare Staffing Shortages

AI and healthcare staffing shortages

Key Takeaways

  • The nationwide shortage of nurses means less individual attention for patients & more potential errors, leading to reduced quality of care.
  • To ease the strain on hospitals, AI/ML is being integrated into nursing workflows to assist with day-to-day tasks, reduce mistakes, and save hospitals money.
  • As the nursing shortage is predicted to grow more dire in coming years, the market for AI in healthcare is projected to grow to $1.85 trillion by 2030.

The Problem

The US healthcare system, always under immense levels of stress and strain, is struggling even more now. The primary problem is a glaring shortage of nurses available for work in hospitals in every region of the U.S. According to the US Chamber of Commerce, an average of 193,100 job openings for registered nurses is projected through the year 2032, with only 177,400 new nurses estimated to enter the workforce.

Such a large discrepancy between the needed number and the actual number of nurses available for hire is far from ideal. Lack of staffing leads to subpar patient treatment and reports show that a scarcity of nurses is correlated with higher morbidity and mortality rates, more frequent diagnostic errors, and overall much lower patient satisfaction scores.

The reason behind this widespread: nurses nationwide have been overworked for years, and subsequently feel burnt out by their jobs to such an extent that they leave the profession altogether. When a nurse leaves and a replacement is not found, the extra workload is diverted to the remaining staff, who then become more overextended than before. This negative feedback loop will only grow worse over time unless a worthwhile solution is proposed.

Solutions

As a result of burnout and nursing shortages, hospitals’ labor costs increased 258% from 2019 to 2022 according to data from the American Hospital Association. This is where artificial intelligence and machine learning (AI/ML) can be integrated as a solution. These systems have the potential to make workloads more manageable, thereby reducing burnout. Several success stories in the news already tout the benefits of using AI/ML, including reduced medical errors, increased hospital savings, and improved hospital operations.

AI’s Potential As A Solution

…nurses nationwide have been overworked for years, and subsequently feel burnt out by their jobs to such an extent that they leave the profession altogether.

Administrative Assistance

Integrating AI/ML into the nursing profession shows positive potential. AI is already being utilized to handle many nursing administrative tasks, helping to free up time for patient care and reduce the feeling of burnout. This new tech can handle many of the tedious tasks that most nurses would prefer to not do, which mostly includes chores like note-taking, staffing, and scheduling.

One specific example is from Mass General, Brigham, the parent organization behind two of the nation’s most prestigious medical institutions, and its handling of the COVID-19 outbreak. When the massive surge of patients hit early in the pandemic, both the hospital and its help hotlines quickly became overwhelmed with massive demand from the public. General Brigham created an AI chatbot, developed around the CDC’s screening questions, that could assist patients and answer their questions. So now, rather than nurses being glued to the flooded phone lines all day, they could tend to their in-hospital patients, and callers were no longer forced to wait on hold. The bot reportedly served over 40,000 patients in its first week of activity.

Remote Monitoring

Remote monitoring allows patients to be monitored outside of a traditional clinical setting using digital medical devices. Apple Watches, Fitbits, and other wearables equipped with pattern-recognizing sensors provide data that helps determine patients’ treatment plans. Heart rate and rhythms, sleep, stress levels, and oxygen levels are all markers that are monitored by the wearables. If any of these (or other) vital signs fall outside of their regular, observed patterns, the AI system can recognize these as potential signs of health deterioration that may otherwise go unnoticed.

This has been of large benefit to healthcare professionals and hospitals in a few ways:

  • AI can identify and help diagnose certain health problems quicker
  • Hospitalizations can be reduced with patients receiving diagnoses and treatment from the comfort of their home
  • Patient triage when a problem is detected can potentially be done more quickly and effectively without an in-person appointment
  • Reduced patient in-flows in hospitals saves time and money for both patients and the healthcare system
  • Treatment plans can be personalized based on the data provided by these AI-powered wearables

Optimized Operations

Another powerful healthtech success story comes from AmeriPro Health’s predictive system, which is aimed at helping hospitals optimize patient flow and management of available beds for inpatient admissions. At one partner hospital, overcrowded emergency rooms would lead to ambulances waiting in the hospital’s parking lot, delaying patient evaluation, treatment, and admission. The AmeriPro predictive model identified recurring bottlenecks that were causing this congestion and provided insights for decision-makers to adjust their strategies in staffing as well as allocation of equipment based on their peak utilization. As the data came in from the model, staff found ways to reduce costs, find new efficiencies in its patient discharge system, and predict the length of an incoming patient’s stay based on their symptoms.

Within months of integrating AmeriPro’s system, the hospital shaved an entire day off of patients’ average length of stay. The inefficiencies exposed by the AI system were subsequently corrected; the hospital adjusted its allocation of equipment to the most essential times of day and focused on discharging patients as early in the day as possible. The result was a significantly larger volume of patient inflow and, therefore, greater coverage for the community.

Financial Benefits

On top of helping staff with their day-to-day tasks and assisting hospitals with providing better services, AI also holds great potential as a financial asset for the healthcare system. If used correctly, AI can save hospitals significant amounts of time and money in the onboarding process for new nurses as well as daily staffing schedules.

How?

  • AI can quickly sift through massive amounts of data from applicants to determine who best fits any given criteria that hiring managers may be looking for.
  • Once hired, AI can be leveraged to personalize their training and development for onboarding. These learning platforms may provide individualized lessons based on learning styles, roles, and personality characteristics. Such a system can go a long way in improving employee morale and, in the long run, retention rates.

As a result of these features, the hiring process becomes much more efficient, requiring significantly less time and resources, and consequently money.

Matching Staffing & Scheduling

On the staffing side of operations, similarly to the AmeriPro system, AI is able to optimize hospital staffing and scheduling with fewer errors. To address the of issue high volumes of patient no-shows and cancellations within their system, Banner Health deployed MedChat AI, a tool that enhances patient triage and guided scheduling for non-clinical agents, ensuring accurate information capture and reducing employee burden and scheduling errors. Using MedChat to assist in their staffing, Banner Health saw a 35% labor productivity increase and saved $9 million annually in full-time employee costs.

HealthTech’s Future & Staffing

A study conducted by Accenture produced the following statistics:

  • 92% of clinicians believe that too much time spent on administrative tasks is a major contributor to burnout.
  • 93% of clinicians agree that applying automation to remedy time-intensive documentation processes will be beneficial.
  • 52% of clinicians believe AI can improve patient diagnosis.
  • 61% feel AI models enable better customer experiences.
  • 55% believe AI leads to faster decision-making.

As these numbers suggest, the vision for AI becoming a mainstay in hospitals nationwide is not just far-fetched, wishful thinking. At this point, countless different companies have found ways to integrate AI into daily activities in healthcare. A survey by the Health Management Academy reports that, as of 2023, 47.5% of all US hospital systems use AI in some capacity, with the remaining 52.5% responding that they are evaluating how AI can be integrated into their hospitals.

In addition, according to a study by Next Move Consulting, the total size of the HealthTech market is projected to grow from $95.6 million in 2021 to $1.85 trillion in 2030, so we are only now entering the prime years of the HealthTech ‘boom’. As AI in healthcare grows more advanced, we should expect that our national healthcare system will continually grow stronger. As more and more AI tools become fully integrated into the day-to-day operations of hospitals, all healthcare professionals should feel burnout’s grip loosen from around them as many tedious, inefficient tasks can now be handled by technology.

Challenges

Although the potential for HealthTech to become the centerpiece of healthcare systems worldwide undeniably exists, there are still barriers to its adoption.

One of the most common explanations hospitals give for delaying the use of AI is the same excuse anyone could give for being hesitant to change; an investment of time and effort will inevitably be required to integrate AI seamlessly into daily operations.

Healthcare professionals have to be trained on how to safely use AI technology and there will inevitably be a learning curve that comes with the newness of these tools. The sooner hospitals realize, however, that the benefits of HealthTech far outweigh the burden of learning to use it, the sooner the worldwide healthcare system can become significantly more efficient and provide better care for all.

Final Thoughts

The benefits that come with the widespread integration of AI into healthcare are endless; the nursing shortage that has cost hospitals sickening amounts of money can be alleviated, inefficiencies in staffing operations can be addressed, and burnout of professionals can be eased. Each of these outcomes, along with many others, feed into what is most important for both patients and the C Suite of healthcare systems: providing greater care while also saving money on unnecessary expenses.

Brooks Harlan
Brooks Harlan

Brooks is a marketing analytics intern at Langar Holdings. He is currently a student-athlete and pursuing his B.A. in Economics at Duke University and is heavily interested in the ways that human behavior/consumption can be modeled and predicted through data.

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