Amber Nigam is CEO and cofounder of basys.ai, a Harvard-based company streamlining prior authorization for health plans with generative AI.
Imagine artificial intelligence calling your doctor on your behalf to assist you in real-time, analyzing biodata from remote sensors, sending updates to your physician, managing insurance approvals and even arranging for your prescription to be ready for pick-up at your local pharmacy.
While this might sound like science fiction, recent advancements in generative AI, especially the advent of ChatGPT, have brought us closer to making this a reality. In recent years, the healthcare industry has gradually digitized. It has seen a rising demand for technology to alleviate administrative burdens, as labor supply cannot keep up with the demand for healthcare services. From my perspective, healthcare is reaching a critical point that will make it the next major frontier for generative AI adoption. While responsible guardrails are necessary to mitigate AI risks, there is a pathway for sustainable implementation of generative AI in healthcare.
The Generative AI Revolution
Generative AI, propelled by deep learning and the advent of large language models, has undergone an astounding transformation in recent years. What was once a theoretical concept has rapidly evolved into a practical and powerful tool.
The journey began with ChatGPT’s debut in late 2022. Since then, new iterations of deep learning and machine learning technology have been hitting the scene at an astonishing pace. To put this into perspective, generative AI’s capabilities could surpass those of human experts in most fields within a decade, according to OpenAI CEO Sam Altman.
A Changing Landscape Of Healthcare
Traditionally, the healthcare industry has been marked by labor-intensive processes, administrative burdens and inefficiencies. Fortunately, technological advancements have begun to reshape the healthcare industry. For example, health insurers are better equipped to maintain responsible guardrails on cost and quality of care through electronic coverage approvals. Telemedicine has helped expand access to services. Despite these positive developments, the healthcare sector still faces numerous challenges, such as rising costs, labor shortages and physician burnout.
The healthcare industry’s readiness for AI integration has never been more evident. Several factors contribute to this receptiveness. First, there has been a significant increase in the availability of healthcare data. Patient records, clinical notes and medical imaging are now digitized, making them accessible for natural language processing and computer vision analysis. Second, regulatory changes, like the adoption of the Fast Healthcare Interoperability Resources standard, have facilitated data exchange and interoperability. Third, with 94% of healthcare organizations lacking an established generative AI strategy, as highlighted by Bain & Company, a strong incentive exists to adapt to compete with the advantages of generative AI-enabled competitors.
Identifying And Mitigating Risks
The integration of AI in healthcare presents an opportunity for innovation. However, this evolution is not without its risks. Algorithmic bias, a significant concern, can perpetuate inequalities in healthcare if not addressed. Additionally, the security of patient data is crucial, as breaches could compromise sensitive information. Another challenge lies in the lack of explainability in AI systems, which raises issues about transparency and trust.
To account for these risks, implementing responsible guardrails and guidelines is essential to govern the development and use of AI in healthcare. These guardrails may involve balancing automation with a human-in-the-loop approach and implementing self-learning mechanisms to ensure algorithms learn from their errors.
Upholding rigorous data standards to ensure accuracy and fairness is pivotal to diminishing biases and enhancing the security of patient data. Establishing training parameters that account for social determinants of health is important to maintain fairness and eliminate algorithmic bias. Compliance with data governance standards such as HIPAA is necessary to maintain data security as well.
Use Cases For Generative AI In Healthcare
Generative AI has demonstrated its potential across various healthcare applications.
• Personalized treatment: AI models can help predict patient health risks, which can enable proactive interventions and personalized care plans. Additionally, AI-driven algorithms can aid in optimizing resource allocation, reducing operational inefficiencies and forecasting healthcare costs, which helps empower organizations to make informed financial decisions for better budget planning and resource utilization.
• Patient engagement: Generative AI can engage with patients in natural language, assisting with appointment scheduling, medication reminders and answering medical queries. With some companies training their LLMs to have bedside manners and essentially function as electronic nurses, this use case could not only enhance patient satisfaction but also lighten the administrative load on healthcare providers.
• Operational processes: Operational processes such as prior authorization and utilization management can use generative AI to automate insurance approvals for medical procedures and prescriptions by analyzing health insurance policies, patient medical records and clinical guidelines. (Full disclosure: This is a service my company provides.) This can help ensure patients receive timely care while minimizing administrative burdens and costs.
Despite these use cases, algorithmic bias, data security and unexplainable AI are risks that must be addressed to sustainably implement AI in healthcare.
Toward The Future
With digitization of medical records, rising costs and administrative burdens, AI could help address some of the most pressing problems in healthcare. While challenges like data quality and bias require attention, responsible implementation of generative AI can overcome these roadblocks. From my perspective, personalized treatment, patient engagement and operational optimization are just the beginning, and key stakeholders across the healthcare industry stand poised to benefit from exploring the potential of generative AI.
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