Articles

Leadership Equity Through a Full Lens

Healthcare systems are navigating a period defined by workforce shortages, clinician burnout, and shifting patient expectations. In cities like Chicago, these challenges are compounded by long-standing inequities in both health outcomes and leadership representation. Conventional leadership models, prioritizing productivity, financial outcomes, and operational control have proven insufficient in addressing these layered challenges. Emerging evidence suggests that leadership approaches grounded in relational trust, psychological safety, and workforce well-being are more effective in sustaining high-performing systems. The Care: Full Leadership Theory offers a redefinition: leadership not as authority or efficiency alone, but as the intentional practice of care, toward people, systems, and outcomes. Within this framework, equity becomes inseparable from leadership itself, as inequitable systems are often sustained by leadership models that fail to center human experience.

Theoretical Foundation: Care as Leadership Currency

At the core of Care: Full Leadership is a fundamental shift: care is the currency through which trust, engagement, and sustainable excellence are built. Leadership effectiveness is therefore measured not only by outcomes, but by the conditions created for people to thrive. This framework is operationalized through three interdependent precepts:

1.     Compassion as Strategy

Compassion is reframed as a strategic lever rather than a peripheral value. Leaders embed empathy into decision-making, recognizing that workforce engagement and patient experience are directly tied to leadership behavior.

2.     Human-Centered Decision-Making

Policies and operational decisions are evaluated based on their impact on people: clinicians, staff, patients, and communities, rather than efficiency alone.

3.     Healing the Healers

Leadership accountability extends to the well-being of the workforce. Addressing burnout, moral injury, and psychological safety becomes central to organizational sustainability.

Leadership Equity Through a Care: Full Lens 

Reframing Equity in Leadership 

Leadership equity has traditionally been measured through representation. While representation remains critical, recent literature emphasizes that equity must also encompass access to influence, decision-making authority, and supportive environments. According to Adesina et al. (2025), inequities in leadership are sustained not only by structural barriers but also by organizational cultures that marginalize certain voices and experiences. These dynamics are intensified for individuals navigating intersecting identities. The Care: Full Leadership framework expands this understanding by asserting that inequity persists where care is unevenly distributed.

Compassion as Strategy: Addressing Inequitable Leadership Pathways 

Traditional advancement pathways in healthcare leadership often rely on informal networks, sponsorship, and subjective evaluations mechanisms that can perpetuate inequity. Positioning compassion as strategy requires leaders to:

• Recognize and mitigate bias in promotion and evaluation processes
• Ensure equitable access to mentorship and sponsorship
• Evaluate leadership potential through inclusive and holistic criteria

Organizations incorporating structured, bias-aware leadership development processes can improve advancement outcomes for underrepresented groups. Compassion, in this context, is not passive; it is an active, strategic commitment to fairness, recognition, and opportunity.

Human-Centered Decision-Making: Redesigning Leadership Systems

Healthcare organizations frequently prioritize throughput, compliance, and cost containment, often at the expense of workforce experience. This dynamic disproportionately affects individuals in roles with less organizational power. Human-centered decision-making shifts this paradigm by:

• Integrating frontline perspectives into executive decisions
• Designing policies that account for differential impact across roles and identities
• Aligning operational goals with workforce well-being and patient dignity

Hill et al. (2025), found that leadership models incorporating participatory decision-making were associated with improved retention and engagement, particularly among historically marginalized groups.

In healthcare systems, where workforce diversity is high but leadership diversity remains limited, this approach offers a pathway to more equitable leadership ecosystems.

Healing the Healers: Equity in Workforce Experience

Burnout and moral injury are not experienced uniformly across the workforce.  Dzau et al. (2020) highlights that clinicians from underrepresented backgrounds often face additional stressors, including discrimination, isolation, and limited advancement opportunities. The “healing the healers” precept addresses these disparities by positioning workforce well-being as a leadership responsibility. Operationalizing this principle includes:

• Establishing psychologically safe environments where all staff can speak without fear
• Implementing structured peer support and debriefing practices
• Aligning incentives to reward supportive and inclusive leadership behaviors

Importantly, healing is both an individual and systemic process. Without addressing the organizational conditions that drive burnout, individual resilience efforts remain insufficient.

Implications for Executive Leadership

The integration of Care: Full Leadership into executive practice has several implications:

1. Redefining Leadership Metrics

Organizations must expand performance metrics to include:

• Workforce well-being and engagement
• Psychological safety
• Equity in advancement and leadership experience

2. Embedding Care into Governance

Boards and executive teams should:

• Hold leaders accountable for workforce outcomes
• Integrate care-based metrics into strategic planning
• Ensure transparency in leadership pathways

3. Aligning Incentives with Care

Compensation and recognition structures should reward:

• Compassionate leadership behaviors
• Team development and mentorship
• Contributions to equitable workplace environments

4. Scaling Human-Centered Systems

Leaders must redesign systems to ensure that care is embedded in:

• Decision-making processes
• Organizational culture
• Leadership development pipelines

The future of healthcare leadership depends on the ability to integrate equity with organizational strategy. The Care: Full Leadership Theory offers a necessary evolution, positioning care as the central currency through which trust, engagement, and sustainable performance are achieved. Advancing leadership equity requires more than increasing representation; it demands a reconfiguration of how leadership is defined, practiced, and evaluated. By embedding compassion as strategy, prioritizing human-centered decision-making, and committing to healing the healers, healthcare organizations can build leadership systems that are not only more equitable, but more effective. In this model, care is not an adjunct to leadership, it is its foundation.

Author: Jamia Thomas, MSN, NE-BC

References

Adesina, I., Joham, A. E., Hamad, N., Pincha Baduge, M. S. S., Garth, B., Nguyen, T. V., & Boyle, J. (2025). Intersectionality in healthcare leadership: A scoping review on the career experiences of racially and ethnically minoritized women health professionals. International Journal for Equity in Health, 24(1), 245.

Dzau, V. J., Kirch, D., & Nasca, T. (2020). Preventing a parallel pandemic: A national strategy to protect clinicians well-being. New England Journal of Medicine, 383(6), 513-515.

Hill, K. A., Austin, A. W., & Enders, F. T. (2025). Workforce interventions to improve representation in U.S. healthcare leadership. SAGE Journals, 12, 1-17. https://doi.org/10.1177/23821205251333034

When Algorithms Shape Access: Designing AI for Equity in Healthcare

When Algorithms Shape Access: Designing AI for Equity in Healthcare

By Racheal Hernandez, MAS

Introduction

Artificial intelligence is transforming industries at breakneck speed, from healthcare and finance to hiring and law enforcement. One of the most pressing ethical questions about AI in healthcare isn’t technical, but structural: Will AI help reduce inequities in care delivery, or unintentionally reinforce them?

Because AI is trained on massive datasets composed largely of existing human knowledge from books, articles, internet content, and social media its outputs are only as objective as the inputs. And much of that data reflects a historical bias shaped by a dominant cultural lens, often white, Western, and male.

For those working in diversity, equity, and inclusion (DEI), the implications are profound. If left unchecked, AI could not only mirror but reinforce systemic inequities. So how do we navigate the risks and unlock the potential of AI as a tool for equity rather than exclusion?

Bias In, Bias Out

At its core, AI works by learning from patterns. If those patterns reflect biased hiring practices, underrepresentation of minority voices in media, or criminal justice disparities, the AI system learns and perpetuates those patterns (Buolamwini & Gebru, 2018).

Healthcare algorithms are a stark example. For instance, an influential study found that an AI system used to allocate healthcare resources was less likely to refer black patients for additional care than white patients with the same medical conditions. This disparity occurred because the algorithm used healthcare cost as a proxy for health needs which reflects the systemic underinvestment in black communities rather than actual need (Obermeyer et al., 2019).

Who Writes the Rules?

Most AI systems are designed by a narrow demographic of technologists. According to a 2020 study by the AI Now Institute, over 80% of AI researchers are men, and the overwhelming majority are white (West et al., 2019).

When design teams lack lived experience with racism, ableism, or gender bias, they may fail to anticipate harmful impacts. What’s seen as “neutral” is often just a reflection of dominant norms.

The Myth of Objectivity

One of the greatest dangers in AI is the illusion of objectivity. When a system makes a recommendation on a job applicant or a loan approval it may seem more trustworthy because it's data-driven. But if the underlying data reflects decades of exclusion or prejudice, then the algorithm simply automates injustice (Noble, 2018).

Steps Toward Equity-Driven AI

While the risks are real, there are also opportunities. With intentional design, community input, and transparent oversight, AI can be made more equitable. Here’s how:

1. Diverse Data Curation
Ensure that training data includes a broad range of voices, experiences, and cultural perspectives. This includes language data, imagery, and behavioral models. Diverse data leads to more equitable algorithms.

2. Inclusive Design Teams
Recruit, hire, and empower technologists from marginalized communities. Inclusion at the table leads to better, fairer technology.

3. Audit for Bias
Conduct regular algorithmic audits to test for disparate impact. Independent, third-party reviews can identify risks that internal teams may miss.

4. Center Ethics in Development
Ethics should not be an afterthought. It must be embedded in the design process from ideation to deployment. Organizations should develop frameworks for responsible AI governance (Whittaker et al., 2018).

5. Listen to Affected Communities
Those most impacted by biased technology must have a voice in shaping and governing it. Participatory design models are critical.

6. Transparency and Accountability
Organizations must disclose how AI systems make decisions and allow users to challenge or appeal outcomes. Without transparency, trust breaks down.

Conclusion

AI is not inherently racist or inclusive but reflects the data, values, and choices of its creators. The question is not whether AI will shape society, but whose vision it will serve.

If we’re serious about equity, we must build technology that challenges historical bias rather than codifies it. This requires systemic change in how we train, hire, and hold accountable the institutions developing AI.

Used with intention, AI can help us identify and undo patterns of discrimination. But only if we lead with justice—not just code.

About the Author

Racheal Hernandez, MAS, is a Healthcare Administrator with over 20 years of experience in ambulatory care and healthcare leadership. Passionate about creating strong operational teams that deliver high-quality care.

References

• Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of Machine Learning Research, 81:1–15.
• Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.
• Raji, I. D., & Buolamwini, J. (2019). Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products. AAAI/ACM Conference on AI Ethics and Society.
• West, S. M., Whittaker, M., & Crawford, K. (2019). Discriminating Systems: Gender, Race, and Power in AI. AI Now Institute.
• Whittaker, M., et al. (2018). AI Now Report 2018. AI Now Institute.