Healthcare systems face challenges like increasing patient loads, rising costs, and staff shortages, which hinder consistent, high-quality care. Without integrating artificial intelligence (AI), these issues could worsen, leading to greater disparities in access and care quality. AI offers transformative potential by automating administrative tasks, supporting clinical workflows, and enabling predictive tools for conditions like sepsis and ICU mortality. Large language models (LLMs), for instance, can generate medical summaries rapidly and accurately.
Despite these benefits, AI implementation, particularly in critical care, remains limited due to concerns about bias, inadequate datasets, and lack of transparency. This AI hesitancy results in significant opportunity costs, including poorer patient outcomes and inefficiencies in resource use. Overcoming these barriers and advancing AI adoption is essential to improving healthcare delivery and addressing its growing challenges.
Opportunity cost in healthcare refers to the lost benefits when AI technologies are not adopted. In critical care, this includes missed chances for earlier sepsis detection, targeted follow-up care to reduce readmissions, and improved efficiency through AI tools like ambient technologies and LLMs. These innovations could enhance patient care by automating tasks, predicting patient deterioration, and allowing clinicians to focus more on direct care. LLMs have also shown the potential to offer empathy comparable to human clinicians. However, most AI models remain in the prototyping phase, especially in ICUs, resulting in lost opportunities to improve administrative and clinical efficiency, resource allocation, and patient satisfaction.
AI models in healthcare face ethical challenges, including perpetuating racial, socio-economic, and gender biases due to unrepresentative datasets and flawed algorithms. There is a need for robust bias detection, local validation, and ethical guardrails, such as guidance from the World Health Organization (WHO). Involving healthcare professionals in bias assessments can further enhance fairness and decision-making.
Failing to implement AI also raises ethical concerns, especially in critical care. Inaction wastes development investments and denies potential public benefits, such as improved patient outcomes and resource efficiency. Hospitals must balance addressing AI risks with the equally significant risks of inaction. Preparing for AI adoption requires technical infrastructure, professional training, and alignment with ethical principles like fairness and patient-centred care.
By addressing these challenges, healthcare institutions can responsibly integrate AI, moving beyond stalled ethical debates to realise its transformative potential for patient care and system efficiency.
To address the opportunity cost of AI hesitancy, healthcare systems should adopt a value-based approach to AI implementation, focusing on improving patient outcomes relative to costs. This aligns AI with goals such as enhancing efficiency, reducing the healthcare burden, improving job satisfaction, and delivering better patient care. A value-based model ensures AI is integrated into healthcare systems and incentivises providers to prioritise outcomes like improved quality of life and reduced mortality rates.
Central to this approach is assessing AI’s Return on Health (ROH), which measures the impact on health outcomes and helps determine whether an AI technology meets patient and institutional needs. ROH assessments should involve end-users, including healthcare practitioners and patients, throughout model development to build trust and ensure alignment with clinical workflows. Testing both the AI model and its integration into workflows is critical to successful implementation, fostering a patient-centred and efficient healthcare system.
AI has the potential to strengthen the patient-doctor relationship by providing earlier insights into conditions and treatment options, fostering active patient and family engagement. Value-based AI ensures effective, coordinated care that enhances health outcomes while reducing costs. However, not all AI models are equally effective, emphasising the need to carefully select and integrate innovations, guided by ethical standards.
In ICU setting, value-based AI could predict patient deterioration, optimise resources, and guide early interventions, improving care, survival rates, and reducing readmissions.
Transitioning AI from bytes to bedside is not just a technological goal but a moral obligation to improve care and equity.
Source: Intensive Care Medicine
Image Credit: iStock
