"Humanness in Health" Insight Series Part 3: Humanely Bringing AI into the Workforce: Smart, Equitable, and Achievable for Public Health

As healthcare systems and public health agencies grapple with workforce shortages, high turnover, and growing community needs, artificial intelligence (AI) has emerged as a promising solution for modernizing workforce recruitment and retainment. From large hospital systems to small rural health departments, AI tools are helping organizations source, screen, hire, and train qualified staff more efficiently. But implementing AI in a human-centered field like healthcare requires careful attention to inclusivity, transparency, and accessibility—especially in underserved communities.

Digital equity is critical. Public health agencies and hospitals must ensure that AI systems are inclusive and accessible, particularly in rural or underserved communities where internet access and digital literacy often lag. Using AI to recruit workers in these areas can backfire if residents cannot access job platforms, understand chatbot instructions, or engage with digital onboarding systems. Successful implementation must include support for digital navigation, multilingual tools, and offline access to ensure no qualified candidate is left out due to technology gaps. 

As agencies work to rebuild and strengthen their teams, artificial intelligence (AI) offers a new, data-driven pathway for talent development that directly responds to these challenges. AI-powered solutions go beyond recruitment, playing a pivotal role in post-hiring retention and workforce optimization. Tools such as predictive analytics can flag employees at risk of disengagement or departure by analyzing trends in engagement surveys, absenteeism, workload distribution, and performance reviews. Platforms like Microsoft Viva Insights, Lattice, or Culture Amp integrate these capabilities to recommend targeted retention strategies, such as flexible schedules, coaching, or recognition programs. Natural Language Processing (NLP) and sentiment analysis applied to open-ended feedback can reveal hidden sources of frustration or unmet career aspirations, enabling human resource teams to act preemptively.

Moreover, AI enables continuous learning and mobility, which are key to retaining talent in a mission-driven but under-resourced field like public health. Personalized learning platforms like Coursera for Government, HealthStream, or LinkedIn Learning Hub use machine learning to curate individualized training paths and recommend opportunities for skill development aligned with evolving agency needs. AI can also support internal talent marketplaces, matching staff with stretch assignments, project-based work, or mentorship opportunities based on their skills, interests, and career goals—thus boosting engagement and preventing stagnation. When combined with AI-enhanced onboarding, wellness tools, and intelligent workforce planning, these technologies form an integrated strategy for workforce sustainability.

For many public health organizations operating under tight budgets, adopting AI might seem financially out of reach. To ease this financial burden, organizations can partner with academic institutions or tech non-profits to expand their AI capacity without heavy spending. Collaborations with public health schools or computer science departments often result in student-led projects that build custom recruitment solutions. Non-profits like DataKind or the AI for Good Foundation pair public organizations with volunteer data scientists who can design AI tools tailored to specific recruitment challenges.

Regional collaboration offers another scalable approach. Public sector recruitment collaboratives—where multiple local health departments share access to an AI-powered platform—can help small agencies pool resources, centralize applicant databases, and coordinate outreach. For example, several county health departments in a state could jointly invest in an AI-enhanced ATS, improving efficiency while keeping costs manageable.

As powerful as AI and automation have become in streamlining the recruitment process, especially in fast-paced sectors like healthcare and public health, hiring the right person remains an inherently human task. Matching skills to job descriptions is only part of the equation. The deeper work lies in understanding people—who they are, what drives them, how they’ve navigated challenges, and how they’ll connect with the

communities they serve. That kind of insight can’t be outsourced to an algorithm.

1. Recruitment Is About Trust, Especially in Health FieldsIn healthcare and public health, trust is everything. These roles demand emotional intelligence, empathy, and a strong understanding of cultural contexts—traits that aren’t easily captured in a resume or digital screening. A chatbot might handle FAQs or schedule interviews, but it can’t detect whether a candidate has the compassion and presence to support grieving families or build rapport in a multilingual clinic. For example, a community health worker who is deeply trusted in their neighborhood may not have a traditional healthcare résumé, but a human recruiter can recognize the value of their lived experience. AI, on the other hand, may filter them out. That’s why human involvement is essential, especially in mission-driven environments.

2. AI Doesn’t Understand Context—People DoMany healthcare professionals don’t follow a linear path. They may have paused their careers for caregiving, shifted from unrelated industries, or returned to work after illness. AI systems often flag these resumes as inconsistent, even though they may belong to resilient, highly capable individuals. Human recruiters ask the right questions, “What led to that gap?” or “What motivated this career change?” Organizations like Proximate Learning understand that context matters. Their approach involves listening, coaching, and advocating for candidates, especially those from nontraditional backgrounds—something AI alone

simply cannot do.

3. Equity Requires Human OversightWhile AI can support equitable hiring by anonymizing applications and reducing some forms of bias, it can also reinforce systemic inequities if trained on flawed or incomplete data. That’s why human oversight is critical to identify and correct unintended bias, ensure fairness, and uphold values of equity and inclusion. Proximate Learning embeds this thinking into its recruitment model, combining human-centered design with technological tools to ensure that AI serves people—not screens them out unjustly. Technology should assist the process, not act as the final gatekeeper.

4. Relationship Building Drives RetentionAI may help identify a qualified candidate, but it doesn’t build relationships—the number one driver of employee retention. Healthcare workers stay in jobs where they feel seen, supported, and connected. Human recruiters and onboarding teams play a critical role in mentoring new hires, offering guidance, and creating a sense of belonging. At Proximate, this relationship-building begins during recruitment and continues through placement and early employment. The warmth and trust fostered in these human interactions can make the difference between a short-term hire and a long-term team member.

5. Recruitment Is a Reflection of Organizational CultureEvery interaction in the hiring process signals what a healthcare organization values. A warm, thoughtful recruiter sends a very different message than a faceless automated email. Even candidates who aren’t selected remember how they were treated. Organizations that prioritize dignity, inclusion, and clear communication attract people who share those values. 

Proximate Learning helps healthcare and public health organizations build equitable, sustainable pipelines of local talent who are skilled, supported, and invested in the mission. This is particularly valuable in fields like healthcare and public health, where cultural relevance, trust, and commitment are just as important as credentials.

Main Takeaways:

• As public health agencies nationwide work to fill the workforce gaps, some organizations have turned to AI for assistance in hiring and employee retention. However, digital equity with these new tools must be practiced to not further exacerbate biases that may cause a wider rift in the hiring process to expand.

• One way to make roads for public health agencies to gain access to these tools for prolonged sustainability is partnership. Collaborations, both regional and nationally, with tech non-profits and academic institutions could lighten spending costs while fostering uplifting reciprocal relationships.

• Even with these tools, it may still be challenging to hire the right person for the position that fits them. So, agencies must work to understand the workforce, particularly the individual.

• Recruitment in healthcare needs empathy and trust that AI can’t solely evaluate within a wide range of candidates with varying skills. However, at Proximate Learning we strive to emphasize human oversight with these tools to ensure equitable, long-term workforce development in public health.