Artificial intelligence is a vast and rapidly evolving technology in all aspects, and AI's potential to transform healthcare is deep-seated. Adoption of AI in healthcare provider organizations is inevitable and will greatly enhance and speed up innovation in the customer and business domains.

AI has the potential to revolutionize healthcare, but its maturity is difficult to assess due to hype and skepticism. Healthcare relies on scientific rigor and has significant regulatory mechanisms. Clinical studies for AI will take years, and payers must approve reimbursement before it becomes widely available.

Let's deep dive into the topic, the points below will help you get a clear picture of the factors that impact the AI adoption on healthcare providers.

Regulatory Approval

AI is expected to impact healthcare by aiding in diagnosis, treatment, and monitoring. The FDA has approved nearly 350 AI applications for clinical use, mostly involving sensor data or radiological imaging. However, regulatory clearance doesn't guarantee optimal performance. Clinicians should ask relevant questions before adopting AI technology, as algorithms can vary across different imaging devices.

Clinical vs Administrative Applications:

AI can lower healthcare expenses and streamline processes. It's a feasible choice for healthcare institutions, especially in the US where 34% of healthcare costs are for administration.

Providers can use AI for internal administration without regulatory approval. Unlike clinical applications, economic returns from AI are more controllable. Errors from AI-based decisions in administrative applications are less problematic than those affecting a patient. However, relevant administrative applications have to comply with government-prescribed reimbursement processes if it is the payer.

Reimbursement and Return on Investment

AI-based innovations in healthcare may be more commonly reimbursed as the industry shifts towards value-based payment models. However, COVID-19 has slowed the progress towards value-based care. Once implemented, AI-powered decisions may be the best way to manage patient populations. Currently, clinical uses of AI are mostly experimental, lack regulatory approval, and provide little ROI for providers. AI development is mostly supported by large, research-focused, and relatively wealthy healthcare providers.

Data and EHR Integration

Healthcare data in the US is fragmented, scattered, and challenging to integrate into AI models. Hospitals and medical practices have their own EHR data, while payers typically keep only claims data. Poor interoperability limits AI's potential to determine comparative effectiveness and identify risk factors. Despite these challenges, the potential value of AI may drive better data standards and integration over time.

Clinician Education and Workflow

Clinicians need to learn about AI for clinical practice, but medical schools lack AI courses. Professional associations must improve specialists' skills. Integrating AI into EHRs will be crucial for widespread deployment in medical practice. Intelligent chatbots may help patients with mental health conditions. Administrative workers in healthcare will experience the greatest impact from AI and automation.

Ethical considerations

The World Health Organization has ethical principles for AI in healthcare to ensure transparency and accountability. However, compliance can be difficult for some AI systems, such as deep learning models in radiological image analysis. Few AI adopters have a system for ensuring compliance with all AI applications, which can slow clinical AI development.

Planning for AI adoption

A deliberate pace for AI adoption in healthcare allows organizations to plan and adapt and depends on several factors. Organizations must prioritize a strategic focus on AI, core transaction applications, a single vendor across the enterprise, and experience in moving new technologies from pilot to broad deployment. These factors are essential to accelerate AI adoption and maximize its benefits.

Finally, AI tech is advancing rapidly, but healthcare is changing slowly. Still, AI has great value, so all healthcare providers should evaluate it and consider how it can transform processes.