In our increasingly complex technological landscape, where data drives decisions and digital health solutions transform patient care, the intersection of health informatics and occupational & environmental medicine (OEM) has never been more critical.
As a board-certified OEM physician and now board-eligible clinical informaticist (thanks to the American Board of Preventive Medicine’s Practice Pathway): I better see the synergies between healthcare, information technology, computer science, and human factors, the interplay between these disparate disciplines, and how can leverage my OEM skillsets to drive innovation.
Health informatics harnesses the power of electronic health records (EHRs) and data analytics to improve clinical decision-making, reduce medical errors, and enhance patient outcomes. The field encompasses everything from clinical informatics and public health informatics to bioinformatics and translational research applications. And now with the rapid advancement of AI and ML, there are unprecedented opportunities to scale data-driven healthcare insights to lead to better care delivery for all patients.
With respect to occupational and environmental health applications: health informatics provides the foundation for developing sophisticated workplace health monitoring and risk assessment programs. And OEM physicians possess a distinctive combination of clinical expertise and population health knowledge that makes them uniquely qualified to address complex workplace health challenges. Their training encompasses ten core competencies that extend far beyond traditional clinical practice, including expertise in OEM-related law and regulations, toxicology and exposure assessments, disability evaluations, and disaster response/emergency preparedness.
The Strategic Value of the OEM Physician with Health Informatics Training
The specialized knowledge of OEM physicians equipped with health informatics training can provide immense value to organizations seeking to manage health-related risks effectively. Their ability to conduct workplace health risk assessments, identify potential hazards, and develop comprehensive prevention strategies makes them indispensable consultants for companies across various industries. These physicians can assess workplace environments for physical, chemical, biological, and psychosocial hazards, providing evidence-based recommendations for risk mitigation. When combined with health informatics capabilities, OEM physician expertise becomes exponentially more powerful. Now with AI and ML, OEM specialists can conduct sophisticated population health surveillance, track exposure patterns, and identify emerging health trends across large worker populations. This technology-enhanced approach allows for real-time monitoring of workplace health indicators and early detection of occupational health disease states.
Integrating health informatics tools with health and safety fundamentals enables OEM physicians to move beyond traditional reactive approaches to embrace predictive and preventive strategies. AI and ML algorithms can analyze vast datasets to identify workers at risk for specific conditions, while electronic surveillance systems can detect early warning signs of occupational illnesses before they become severe. This proactive capability is particularly valuable for organizations seeking to minimize healthcare costs and optimize workforce productivity.
The Future of Risk Management
As organizations face increasingly complex health-related challenges, the integration of health informatics and OEM expertise represents a critical evolution in risk management approaches. The ability to combine clinical knowledge with sophisticated data analytics creates opportunities for more precise risk assessment, targeted interventions, and measurable outcomes.
This partnership between technology and specialized medical expertise enables organizations to move beyond compliance-focused approaches to embrace comprehensive health promotion strategies. By leveraging the unique knowledge base of OEM physicians enhanced by health informatics capabilities, start-ups, Fortune 500 companies, think tanks, and world organizations can develop more effective, evidence-based approaches to protecting human health in our rapidly changing world.
Use Case 1: Evidence-Based Care Optimization (ECO) for Occupational Health Decisions*
In a MedPageToday piece, my colleagues and I with the End Burnout Group introduced the concept of Evidence-Based Care Optimization (ECO) as a replacement for prior authorization systems that currently burden healthcare providers with administrative delays and inappropriate care denials. In the context of OEM practice, this informatics approach could transform how occupational health decisions are made and approved. ECO leverages existing digital tools to automatically certify clinical appropriateness using national clinical guidelines integrated with the EHR, separating clinical decision-making from insurance coverage determinations. For OEM practitioners, this could mean streamlined approval processes for workplace injury treatments, occupational disease management, and preventive health interventions, ultimately improving worker health outcomes while reducing healthcare costs.
Use Case 2: AI Trust Calibration Framework for OEM Clinical Decision Support
In a recent article my colleagues at West Virginia University and I have published, we highlight a systems thinking approach to understanding how clinicians develop trust in AI systems over time, offering a crucial framework for implementing AI-based clinical decision support in OEM practice. Through simulation-based analysis, this research demonstrates that clinician expertise, workload, diagnostic difficulty, and AI accuracy interact dynamically to influence trust trajectories, with expert clinicians maintaining more stable trust levels even under challenging conditions. For OEM informaticians, this framework offers evidence-based guidance for developing AI systems that support occupational health assessments, environmental exposure evaluations, and workplace risk stratification. This approach could inform the development of specialized training programs that help OEM professionals calibrate their trust appropriately, ensuring neither over-reliance nor under-utilization of AI tools in critical occupational health decisions.