AI in Healthcare: Will it threaten humans? The HR Perspective

Introduction

Artificial intelligence (AI) is transforming the healthcare sector, enhancing diagnostic accuracy, personalizing patient care, and streamlining administrative tasks. According to the latest data 40% of global companies report using AI in their business, and 82% of global companies are either using or exploring the use of AI in their organization. This marks a significant increase from 2017, when only 20% of companies had incorporated AI into their product offerings and business operations. At that time, 72% of these businesses believed AI would have an impact on their business within five years. As AI revolutionizes patient care, it also brings challenges that must be managed from an HR perspective. This blog explores how healthcare HR can balance technology and human interaction to ensure optimal patient outcomes.

AI and Talent Acquisition: Hiring Smarter with AI

AI-powered chatbots are transforming initial candidate screening in healthcare recruitment by automating the preliminary interaction process. These chatbots can handle routine inquiries and pre-screen candidates based on predefined criteria, significantly reducing the time and effort required by HR professionals. AI's role in identifying top talent has been bolstered by data analysis and predictive modeling. By analyzing large datasets, AI can uncover patterns and predict candidate success, aiding HR professionals in making more informed hiring decisions. AI also enhances candidate engagement by providing timely updates and personalized interactions, improving the overall candidate experience. 

However, the use of AI in recruitment raises ethical concerns, such as the potential for bias and fairness issues. Algorithms can inadvertently perpetuate existing biases if not carefully managed. It is crucial to apply human judgment alongside AI tools to ensure a fair and equitable hiring process. Incorporating AI into recruitment processes offers numerous advantages but must be balanced with human oversight to address ethical considerations and ensure the best outcomes for both candidates and employers.

Examples

• LinkedIn’s AI algorithms help recruiters identify high-potential candidates by analyzing skills and experience against job requirements (LinkedIn, 2023).
• Google uses AI to screen resumes and identify qualified candidates for open positions (Henkin, 2023)

AI and Employee Development: AI as a Learning Companion

The transition towards an AI-driven healthcare system underscores the importance of reskilling and upskilling programs for healthcare employees. Training and development are not only vital for equipping staff with new skills but also play a crucial role in enhancing job satisfaction and motivation. According to Herzberg's Two-Factor Theory, opportunities for personal growth and advancement can significantly impact job satisfaction (Herzberg, 1966). Additionally, the Self-Determination Theory suggests that skills development fulfills employees' needs for competence, leading to increased motivation and engagement (Deci & Ryan, 1985). By investing in continuous education and professional development, healthcare institutions can ensure their staff remains competent and adaptable, which in turn fosters a more motivated and satisfied workforce.

AI-powered learning platforms are revolutionizing employee development in healthcare by offering personalized training experiences tailored to individual needs. 

• Platforms, like Coursera for Business, use AI to recommend courses based on employees' skills, roles, and career goals, ensuring that learning is relevant and targeted (Coursera, 2023). 

AI also plays a crucial role in identifying training needs and performance gaps by analyzing performance data and predicting skill deficiencies. 

Examples

• IBM’s Watson Analytics provides insights into employee performance, helping organizations pinpoint areas where additional training is required (IBM, 2023). AI-powered performance management systems, such as those offered by SAP SuccessFactors, provide real-time feedback and coaching by continuously monitoring employee performance and offering actionable insights (SAP, 2023). 
• Google designed AI improvements to help employees focus on their most important tasks while collaborating securely and strengthening human connections (Daston, 2023).

Despite these advancements, human interaction remains essential in the learning process. While AI can offer valuable insights and support, the guidance and mentorship provided by human managers and coaches are critical for contextualizing feedback and fostering a supportive learning environment.

AI and Employee Well-being

AI-powered wearable devices are increasingly used to monitor employee health and well-being, offering real-time data on physical activity, sleep patterns, and vital signs. Companies like Fitbit and WHOOP provide wearables that help employees track their health metrics, promoting physical well-being and aligning with Herzberg's Two-Factor Theory, which highlights that health and safety are fundamental to job satisfaction (Herzberg, 1966).

AI plays a significant role in identifying early signs of burnout and stress by analyzing data trends from these wearables and employee surveys. 

Example

• Companies using AI tools like Microsoft’s Workplace Analytics can detect patterns of decreased productivity and increased absenteeism, signaling potential burnout before it becomes critical (Microsoft, 2023). 
• AI-powered mental health support tools, such as those offered by Woebot Health, provide employees with immediate access to mental health resources and support, helping address psychological well-being and stress management (Woebot, 2023).

AI-powered tools, like virtual assistants and chatbots, offer employees immediate support and access to resources for their inquiries or issues. These tools can enhance job satisfaction by streamlining tasks and making them more manageable. However, the absence of personal interaction may lead to feelings of isolation among employees.

Meanwhile, AI can take over repetitive, time-consuming tasks, freeing up employees to focus on more creative, strategic, and fulfilling work. This can reduce stress and enhance job satisfaction. However, the fear of job displacement due to automation can also create anxiety and insecurity among employees.

The use of AI to monitor employee well-being raises ethical concerns, particularly regarding privacy and data security. The continuous collection of personal health data can lead to concerns about surveillance and data misuse. It is essential for organizations to ensure transparency and obtain informed consent to address these privacy issues. A holistic approach to employee well-being should integrate non-AI interventions, such as employee assistance programs and wellness workshops, to complement AI tools and foster a comprehensive support system.

AI and Job Displacement: Can Robots Replace Humans?

The automation of routine tasks in healthcare, such as medical transcription and data entry, is significantly transforming the industry. AI-powered systems can now efficiently handle these repetitive tasks, allowing healthcare professionals to focus on more complex aspects of patient care. AI-powered diagnostic tools, such as those used in radiology, are enhancing diagnostic accuracy but also altering professional roles. 

Examples

• Mayo Clinic employs AI-powered tools to assist in diagnostic imaging. AI algorithms analyze medical images, such as X-rays and MRIs, to detect abnormalities with high accuracy, aiding radiologists in their work. 
• Mount Sinai Health System uses an AI platform called Deep Patient, which analyzes electronic health records to predict the onset of diseases such as diabetes and liver cancer.
• Tools like Google's DeepMind have demonstrated the ability to identify diseases from medical images with high precision, potentially impacting radiologists' traditional duties (Google Health, 2023).

While AI presents opportunities, it also raises concerns about job displacement, particularly for roles heavily dependent on data processing, such as radiologists and medical coders. The concept of job displacement versus job transformation highlights that while some positions may diminish, new roles will emerge. This shift necessitates robust reskilling and upskilling programs to help employees transition into new roles, aligning with the principles of job security and motivation (Herzberg, 1966). Human-AI collaboration offers potential benefits, such as enhanced diagnostic capabilities and improved patient outcomes, while still preserving essential human oversight and judgment. This balance can help maintain job satisfaction and security in the evolving healthcare field.

AI and HR Analytics: Making Informed Decisions

AI-powered HR analytics tools are revolutionizing decision-making in healthcare by identifying trends and patterns within employee data. Tools such as IBM’s Watson Talent use AI to analyze large datasets, revealing insights into employee behavior and organizational dynamics (IBM, 2023). This data-driven approach has led to significant improvements in organizational performance, including up to a 25% increase in business productivity and an 80% boost in recruiting efficiency (SHRM, 2022). AI also plays a crucial role in predicting employee turnover and performance. 

Examples

• Predictive analytics platforms like Workday can forecast potential attrition by analyzing factors such as job satisfaction and engagement levels, enabling proactive retention strategies (Workday, 2023). 
• Johnson & Johnson applies HR analytics to support diversity and inclusion initiatives. They analyze demographic data, hiring trends, and promotion rates to identify gaps and biases in their recruitment and career advancement processes (Johnson & Johnson, 2024).

Optimizing workforce planning and scheduling is another area where AI excels. By leveraging historical data and real-time inputs, AI tools can create efficient schedules that align with demand and employee availability, enhancing operational efficiency.

• Amazon utilizes AI to optimize various HR functions, including employee scheduling and performance management. By automating routine tasks and leveraging AI for data-driven insights, Amazon allows HR teams to concentrate on strategic activities such as employee development and improving workplace culture.

However, the effectiveness of AI-driven HR analytics depends on data quality and accuracy. Inaccurate or incomplete data can lead to flawed insights and decisions. Thus, HR professionals must develop strong data literacy skills to interpret and leverage these insights effectively, ensuring strategies are based on reliable information (Deloitte, 2023).

The Ethical Dilemma of AI

Algorithmic bias in AI systems poses a significant challenge in healthcare HR, impacting recruitment and promotion decisions. Bias can inadvertently perpetuate existing inequalities, leading to unfair treatment of candidates and employees. For example, a study by MIT and Stanford found that AI systems used in hiring can exhibit gender and racial biases if not carefully designed and monitored (Angwin et al., 2016). This can undermine employee satisfaction and motivation, as biased practices affect their job opportunities and career growth.

Data privacy and security are also critical ethical concerns in AI-driven HR practices. AI systems often require access to sensitive employee data, raising the risk of breaches and misuse. For instance, the misuse of employee data by AI systems can lead to privacy violations and legal repercussions, impacting employee trust and morale (GDPR, 2022).

Examples

• Mayo Clinic encountered issues related to data privacy when using AI to analyze employee performance and engagement.
• Amazon's AI recruitment tool, trained on biased historical hiring data, unfairly disadvantaged female candidates by penalizing resumes with female-oriented terms or experiences. This led to privacy and fairness concerns, highlighting the need for AI systems to be not only secure but also fair, with mechanisms to monitor and correct biases.

The use of AI for surveillance and monitoring can further exacerbate ethical dilemmas. Continuous monitoring might be perceived as intrusive, potentially diminishing employee morale and job satisfaction. Ethical guidelines and regulations are essential to ensure that AI applications in HR are used responsibly, protecting employee rights and fostering a fair work environment (OECD, 2021).

Overcoming Resistance of Implementing AI

Resistance to change is a common challenge in implementing AI in healthcare HR. Employees may fear job displacement or feel threatened by new technologies. For instance, a study by McKinsey highlights that 70% of change initiatives fail due to employee resistance (McKinsey, 2020). Addressing these concerns through effective communication and involving employees in the change process is crucial. Transparent communication helps in reducing uncertainty and building trust, which can enhance motivation and satisfaction.

Training and development play a pivotal role in preparing employees for AI-driven workplaces. Providing targeted training helps employees build the skills necessary to work with new technologies, aligning with the principles of change management which emphasize equipping staff to handle transitions (Kotter, 1996). 

Example

• IBM’s AI training programs have helped employees adapt to AI tools, significantly improving their confidence and job performance (IBM, 2023).

Leadership support is essential for successful AI implementation. Leaders who advocate for the change and model positive attitudes towards AI can influence employee perceptions and facilitate smoother transitions. Additionally, adopting a phased approach to AI implementation allows organizations to manage changes incrementally, addressing issues and adjusting strategies as needed, which minimizes disruption and resistance (Harvard Business Review, 2021).

Example

• Mayo Clinic faced resistance during the integration of AI tools for performance management, with concerns about surveillance and job displacement. The solution involved strong leadership support, transparent communication about AI benefits, and a phased implementation approach. Comprehensive training was also provided to help employees adapt to the new technology.

Future of HR with AI

The long-term impact of AI on the HR function in healthcare is transformative. AI is expected to automate routine tasks such as payroll processing, benefits administration, and initial candidate screenings, allowing HR professionals to focus on strategic initiatives like talent development and employee engagement. According to Gartner, by 2025, AI will automate up to 50% of routine HR tasks, significantly altering the HR landscape (Gartner, 2022).

New HR roles and responsibilities will emerge in an AI-driven world, including roles such as AI HR specialist and data-driven talent manager. These roles will focus on leveraging AI tools to enhance HR processes and improve decision-making. 

Examples

• Unilever uses AI to analyze candidate video interviews, streamlining the hiring process and improving efficiency (Unilever, 2021).
• HireVue's AI analyzes video interviews to assess candidates' responses, tone, and body language, streamlining hiring by providing insights into fit and performance. As AI evolves, these tools will offer more sophisticated insights and reduced biases through improved algorithms and diverse data.

The collaboration between HR and IT departments is crucial in managing AI initiatives. This partnership ensures that AI systems are implemented effectively and align with organizational goals. Developing AI literacy among HR professionals is essential to bridge the gap between HR and IT. Continuous learning and adaptation are necessary, as highlighted by the Society for Human Resource Management (SHRM), which emphasizes the importance of ongoing education in emerging technologies for HR professionals (SHRM, 2023).

References

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Coursera (2023) Coursera for Business. Available at: https://www.coursera.org/business (Accessed: 6 August 2024).

Deci, E.L. and Ryan, R.M. (1985) Intrinsic Motivation and Self-Determination in Human Behavior. New York: Plenum Press.

Daston, L. (2023) ‘AI improvements in Google: Focusing on important tasks,’ Daston Reports. Available at: https://www.daston.com/ai-improvements-google (Accessed: 6 August 2024).

GDPR (2022) General Data Protection Regulation. Available at: https://gdpr-info.eu/ (Accessed: 6 August 2024).

Google Health (2023) ‘DeepMind: Advancements in AI for medical imaging,’ Google Health. Available at: https://health.google.com/deepmind (Accessed: 8 August 2024).

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Henkin, M. (2023) ‘Google's AI resume screening technology,’ Forbes. Available at: https://www.forbes.com/google-ai-resume-screening (Accessed: 6 August 2024).

Herzberg, F. (1966) Work and the Nature of Man. Cleveland: World Publishing Company.

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Kotter, J.P. (1996) Leading Change. Boston: Harvard Business School Press.

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