AI and the future of work: 5 challenges healthcare faces

April 21, 2021

2020 radically changed the face of the American workplace — probably forever. While the full impact is not yet known, one thing is certain: AI and automation are quickly being adopted throughout all industries to increase productivity, lower costs and improve resiliency. In healthcare particularly, an industry traditionally slow to adopt emerging technologies, the need for intelligent automation was made abundantly clear as health systems faced unprecedented challenges. In fact, only 53% of hospitals had an AI strategy in 2019. By 2020, that number jumped to 90%.

But a strategic plan is not the same as implementation and widespread adoption. To advance the industry to the future and harness AI’s incredible benefits, healthcare leaders need to be fully aware of the challenges they will face in advancing to the future. It is a unique industry with unique concerns, but has the same need for innovation as every other industry. Healthcare leaders need to be ready to navigate and overcome these hurdles to find AI success and lasting impact for their organizations.

1. Overcoming employee hesitation and fear of automation

One of the greatest concerns voiced time and time again is the fear of artificial intelligence taking or eliminating jobs. But job elimination is not the goal of automation. Instead, the goal is to put humans back to human-centric work, using AI to augment our human workforces. Healthcare workers at all levels are facing burnout and overwork. AI can solve this. In fact, a McKinsey study on the future of work forecasts that while AI and automation can replace half of the activities across all jobs, only 5% of occupations can be fully automated. What AI can do is automate manual, repetitive data entry work and provide insights and recommendations that improve other workflows.

If healthcare leaders are unable to clearly communicate their goals with automation — not job reduction, but job enhancement — AI initiatives will inevitably face resistance that hamper expected outcomes. Organizations should develop and implement a comprehensive change management strategy that not only explains AI’s role within the organization, but also plans for employee change, such as creating new career ladders and new job descriptions. Buy-in is important at every level of the organization, so that every employee will help promote the technology’s use and further its impact.

2. AI’s ethical concerns and combatting biases

As artificial intelligence applications move beyond RPA into machine learning and neural networks, the potential for biases and other ethical concerns start to emerge. Bias can develop due to the data used to develop the algorithms, the design of the algorithm itself, or even in the practical application of the output. For example, if cost is a factor in determining a treatment plan, how do we know that the algorithm is still recommending the best course of treatment?

Every participant in healthcare AI needs to have responsibility for the ethical use and development of these solutions. AI developers need to address any potential for bias in their solutions and algorithms. Health systems need to be sure they understand any ethical concerns when choosing solutions or partners. And the end users need to be sure they know how to properly interpret outputs. As AI’s presence in the hospital continues to grow, ethics needs to remain a priority in each new use case so that it can be responsibly deployed. Nothing could shut down innovation like an ethical oversight.

3. Data security

Data security has been top of mind for healthcare IT professionals for years now, as cyberattacks have become more common and the cost of data breaches has grown. Artificial intelligence poses additional data security risks that need to be addressed not only for compliance and risk reduction, but also to maintain patient trust and privacy. AI algorithms themselves need to be secure, as does the data fed to them. Top security procedures need to be in place with all vendors, as well as monitoring practices that detect any potential threats or interference.

Luckily, AI itself is helping with healthcare data security. New privacy-preserving AI uses sophisticated mathematical models to protect and de-identify patient data, helping researchers and AI networks safely use data to develop their AI models.

4. Delivering tangible ROI

When investigating artificial intelligence technologies, it’s easy to get caught up in all the various options and potential applications. But to maintain momentum for AI and automation, health systems should first look for projects that can show tangible value, quickly. This can bolster enthusiasm and provide financial resources to reinvest in additional AI projects.

Currently, the best place to deliver fast ROI is in operational applications of AI that use intelligent RPA technology to automate burdensome administrative processes, such as claim status checks or 403(b) management. These are proven solutions that can improve a hospital’s bottom line while also improving employee efficiency.

5. Deploying at scale

To fully realize the benefits of artificial intelligence, organizations need to deploy artificial intelligence at scale throughout the enterprise. This is easier said than done. As health systems rush to implement new technologies and automate more workflows, the potential for siloed solutions, competing priorities, and problems with maintenance and governance all arise, threatening AI’s success.

Health systems need to start by planning for scale, creating an enterprise strategy and centralized governance system to oversee all AI projects. This will help manage resources by deploying people and dollars appropriately, and protect today’s quick-fix solutions from impeding tomorrow’s vision of an AI network working throughout the hospital.

By choosing AI as a Service (AIaaS), health systems can overcome these challenges and reap the benefits of AI sooner, propelling their organization into the future and becoming a leader in care.

AIaaS is quickly becoming the preferred method for purchasing and developing AI solutions. A healthcare-only AI as a Service provider understands the challenges of AI and helps healthcare leaders address them to ensure success at their organization. By choosing AIaaS, you get a partner who has the experience to successfully deploy and scale AI.

The future is coming. Artificial intelligence and automation are changing our hospitals and our workplaces. Is your health system ready? To learn more about how AI can be deployed throughout the enterprise to transform operations, visit the AI-Powered Hospital and see what AI can do for you.