The role of Artificial Intelligence in US healthcare delivery

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The integration of Artificial Intelligence (AI) into various industries has occurred in stages, with AI being particularly advanced in areas like finance, retail, and transportation. However, in the realm of healthcare delivery, AI is still in its early stages. Several factors contribute to AI adoption lagging in healthcare compared to other sectors. Early AI implementations were reliant on large amounts of structured data and discrete outcomes, while healthcare often deals with qualitative information and complex clinical decision-making, making AI implementation more challenging. Additionally, healthcare organizations may prioritize short-term financial gains over investing in long-term innovative technologies like AI, which could benefit the industry holistically.

Despite the challenges, the necessity of AI in improving healthcare delivery cannot be denied. With medical knowledge doubling at an exponential rate, AI has the potential to support clinical teams in providing the best possible care to patients. The COVID-19 pandemic has accelerated the digitalization and deployment of AI in healthcare, prompting discussions at management and board levels regarding its potential benefits. Moreover, AI adoption can help address the ongoing shortage of healthcare professionals, providing an additional benefit to the industry.

In healthcare delivery, AI primarily focuses on improving clinical judgment, including prognosis, diagnosis, treatment, clinician workflow, and expanding clinical expertise. Specialties like radiology, pathology, dermatology, and cardiology already utilize AI for image analysis. The potential of AI in healthcare extends beyond image analysis, with emerging use cases in nine domains of healthcare delivery, including reimbursement, clinical operations, quality, and safety.

Reimbursement, a critical aspect of financial health for healthcare organizations, has seen significant AI adoption. AI has simplified patient billing processes, reduced denials, and improved overall revenue-cycle management. Predictive analytics and natural language processing are being employed to tackle issues like prior authorization, reducing the need for manual intervention and enhancing patient experiences.

In clinical operations, AI is being used to optimize operating-room usage, predict surgical outcomes, and address clinician burnout. By streamlining scheduling, AI can increase access to care and reduce surgical wait times. Additionally, natural language processing is being piloted to alleviate the burden of updating electronic health records, enabling clinicians to spend more time with patients.

Quality and safety are domains where AI can have a substantial impact on nonfinancial factors. AI applications have been designed to reduce major adverse events and improve patient experience. For instance, AI algorithms can predict conditions like sepsis, allowing for timely interventions and significant reductions in mortality rates. AI is also employed to enhance patient experience through programs like Consumer Assessment of Healthcare Providers and Systems (CAHPS), aiming to understand and address patient dissatisfaction.

While AI has shown promise in various healthcare domains, most applications have not yet undergone randomized, controlled trials. Therefore, evidence may be lacking in some areas. Organizations are currently in the pilot phase, seeking to validate the benefits of AI adoption. Health systems are examining the potential of AI in reimbursement, clinical operations, and quality and safety, with AI adoption having varying degrees of success. As AI continues to evolve, it is expected to play a pivotal role in shaping the future of healthcare delivery, revolutionizing patient care and overall healthcare outcomes.

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