Technology Insights Clinical Staffing

Making Us More Human: Applying Artificial Intelligence to Your Clinical Resourcing and Talent Acquisition Strategy

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Steve Matas, SVP of Strategic Resourcing
Steve Matas, SVP of Strategic Resourcing

Since the high-profile release of ChatGPT, we’ve all been inundated by stories about the inevitable rise of artificial intelligence (AI) and the potential doom and gloom of employing these tools too widely and too quickly. Headlines that portend AI applications will become sentient muddy the waters of reasonable discussion, and warnings from people like Geoffrey Hinton — the former computer scientist and ‘godfather of AI’ who quit Google to focus on the technology’s potential risks — further add to the perception that artificial intelligence and machine learning (ML) applications will grow beyond our control, replacing human jobs and eliminating entire industries. This is not only wrong and somewhat ridiculous, but it also ignores the tremendous opportunities AI can create to make our jobs and our lives more productive, more efficient, and (ironically) more human.

AI is not getting rid of the human; it is making them more human

Although there are still several valid concerns around privacy and security with artificial intelligence utilization, there are nonetheless abundant opportunities to apply AI to almost every industry, including clinical resourcing. For these professionals, AI applications can free up resources from burdensome and repetitive tasks to enable people to focus on innovation and human-centric activities that require compassion, collaboration, and reasoning (things a machine can never learn or emulate). The idea is simple: strategically applied AI and ML applications can provide faster outcomes while reducing (not replacing) human effort. For the person-to-person based resourcing industry, specifically, there are four areas in which artificial intelligence can help deliver on this promise:


  • Talent acquisition: AI can be used to automate the process of sourcing and screening candidate cover letters and resumes, helping recruiters to find qualified candidates more quickly and efficiently. Natural Language Processing (NLP) techniques can also be used to assess candidate skills, experience, and qualifications, enabling recruiters to focus on the most promising candidates for open positions.
  • Candidate matching: AI algorithms can be developed to match candidates from various online platforms and professional networks with job openings based on their skills, experience, and other relevant criteria, saving recruiters valuable time that can be better spent on networking and other more high-value activities while simultaneously removing bias. Additionally, machine learning algorithms can quickly analyze candidate profiles, job descriptions, and historical data to match candidates with relevant job opportunities.
  • Predictive analytics: AI algorithms can also analyze historical hiring data to identify patterns and predict candidate success based on factors such as qualifications, skills, and performance metrics. This helps recruiters make more informed decisions, prioritize candidates, and minimizes potential biases in the selection process.
  • Performance management: AI can be used to monitor employee performance and provide feedback to help employees improve their skills and performance. Similarly, AI can analyze employee data and patterns to identify factors contributing to turnover, enabling leaders to develop proactive retention strategies for long-term team cohesion.

The examples above show some of the ways in which AI can support and enhance the clinical resourcing industry, but it is important to remember that all technologies are merely tools to augment human decision-making, not replace it entirely. Successful recruitment and candidate placement will always require human oversight and intervention for maintaining ethical practices and incorporating important context that is beyond a machine’s capability.

Proceed with caution, enthusiastically

Human oversight is not only necessary for users of AI applications, but also critical for organizational leaders as they must assess the risks and benefits of applying artificial intelligence and machine learning tools into the workplace. The long-term effects of AI on life science business processes and operations are still largely unknown, and IT Leaders must proceed cautiously as they navigate regulatory uncertainty, the potential for data breaches, and the numerous ways in which bad actors can exploit systems for nefarious purposes before implementing any AI/ML applications into their organization.

Technology and HR leaders should determine who in the organization is using AI tools currently, and for what purpose. Leaders will also want to determine how best to protect enterprise data if/when employees are utilizing artificial intelligence and how then to manage security risks of the underlying technology. The bottom line is that it is incumbent upon leaders to have a balanced discussion of the potential benefits and risks of these technologies in advance of their use. Artificial intelligence is undoubtedly here; we must now learn how to use it properly and securely.

As discussed, there are numerous opportunities to apply artificial intelligence to several industries — including clinical resourcing — that promise to save time and make human work more productive. By freeing up employee time through automated assistance, AI applications have the potential to make us more human in the transactional world in which we live — a future state worth striving for, not one to be afraid of.

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