The scale of the problem
In the U.S. demand for qualified staff is growing. 2.3 million new healthcare workers will be needed by 2025. 75% of Americans now live with chronic conditions like dementia. Employment in age-related care will grow 41% by 2026 because of this.
Over the pond, the workforce challenges in the UK’s NHS (National Health Service) now present a greater threat to health than its funding challenges. There is a shortfall of nearly 100,000 workers which is expected to reach 250,000 by 2030.
Suffering the consequences
Without the right staff to care for patients, everyone’s health will suffer. Waiting lists will increase and non-critical care won’t be fulfilled. Workers who are overworked will quickly burn-out, further worsening the skills shortage. A worryingly high number of doctors and nurses are now leaving their jobs early.
Which will waste resources and funding. For the NHS, in particular, there’s a risk that £20.5 billion ($26.8 billion) in frontline funding won’t be spent because of a lack of people to implement the strategy.
Solving the staffing crisis
AI can improve healthcare staffing in several ways. First, as a way of mapping existing talent and skills - to identify skills gaps and predict future demand. It can assist with alternative options of resourcing, by assessing contractors for example. By surfacing critical insights and data, short and long-term workforce planning will be more informed.
It can also address biases in hiring and promotion processes. Through machine learning, hiring managers can now search for the best candidates from a vetted pool of healthcare professionals. Based on qualitative indicators such as skills and experience, instead of gut instinct.
Plus, with AI automating time-intensive tasks such as screening CVs, HR and procurement teams can focus on interviewing and hiring candidates. People who have been automatically matched to relevant opportunities based on their aptitude. Ensuring the best workers are placed in each role - boosting skills utilization and productivity.
Ultimately improving the quality of people hired, ensuring they are fit for each opportunity and that it aligns with their career goals. As, if work is of interest, people are more likely to complete it well. AI will also reduce time-to-hire so professionals can be caring for patients in a shorter time-frame.
There are many applications of AI that will prove useful to healthcare leaders. Traditional ways of staffing are not working and the industry is flatlining because of it. If alternative ways of resourcing are not found, such as using AI, then patients and workers will suffer. Starting a downhill spiral that’s not good for anyone’s health.