The drivers behind Artificial Intelligence (AI) and advanced data analytics promise a healthcare revolution. Yet, my experience across technology, policy, and care delivery reveals a crucial gap: while health tech moves at lightning speed, policy is struggling to ensure this innovation is safe, equitable, and effective.
True innovation must move past fast products and buzzwords and tackle the root causes of health problems, reduce administrative burden, and improve clinical decision-making.
The Dual Reality of AI: Real Impact, Real Harm
AI is undeniably starting to make a real impact in some ways, delivering better insights at the preventive and intervention levels. However, we are far from reaching a "do no harm" standard. The potential for real harm is evident, especially in sensitive areas like mental and behavioral health, where unregulated direct-to-consumer tools (often marketed as "wellness" or a "chat bot") lack the organizational scrutiny and safety standards of professional tools.
Regulation must prioritize policies that actively prevent harm, discrimination, and fear, while simultaneously offering clear, positive health impacts.
Policy Lag is Slowing Down Progress
Health technology's speed is often met with policy friction:
● Fragmented Compliance: Standards are often scattered and inconsistent, even within local sub-sectors like primary care. This slow, confusing process unnecessarily impedes adoption of beneficial new technologies.
● Misaligned Incentives: Payors often focus on short-term reimbursement (one-year windows), clashing with the long-term view needed to prove real patient outcomes and Return on Investment (ROI) that local clinical practices require.
● Data Bias: While the goal is equity, the lack of attention to managing data biases in algorithms misses a critical opportunity to address long-standing healthcare disparities.
The Path to Precision for Equity
If policy is to catch up, the focus must be ensuring ‘do no harm’ is paramount, even for tools marketed as non-clinical. A simple disclaimer is not enough.
To drive equitable care, policymakers and health systems must leverage AI for Precision for Equity:
- Individualized Insight: Use data to disaggregate health factors by demographics and social determinants, recognizing that different people have different needs.
- Population Focus: AI can identify high-risk groups, allowing local systems to prioritize and focus resources for both preventive and individualized care.
- Administrative Support: Automate, streamline, and optimize repetitive tasks to increase time for patient-focused care.
- Improved Decision-Making: Strategies like objective risk scoring, image pre-screening, detection tools, language translation, and accessibility tools can strengthen clinical care.
Scaling these solutions is currently hampered by fragmented IT systems (siloed EHRs), the difficulty in proving long-term ROI, and the challenge of securing multi-level organizational buy-in. We also must also address financial barriers for community healthcare nonprofits and education serving disadvantaged groups, as innovation tied solely to reimbursement excludes the very populations in need.
Preparing for the Future of Oversight
Looking ahead, we need a shift from fixed to >>> lifecycle oversight, allowing technologies to safely evolve while maintaining governance for safety and equity. We must train future healthcare leaders on computational ethics standards, moving away from rigid compliance models. Innovators must prioritize PROVING clinical or preventive value, not just novelty, while also looking at potential unintended negative consequences of their inventions.
Relevance for the Long Island Health Collaborative
For the Long Island Health Collaborative, these gaps have immediate implications. Our diverse communities face unique health challenges driven by social determinants and the priorities identified in our community needs assessment.
● Actionable Next Step: The Collaborative can advocate for and pilot programs that embed Equity-by-Design into local health tech adoption. By focusing resources on primary care and community-based organizations (and insisting on greater clarity and consistency in compliance from regulators) we can ensure AI-driven innovations do not harm our community and effectively address the needs of all Long Islanders.