The Future of AI in Healthcare: Strategies for Successful Adoption

Healthcare leaders are increasingly recognizing the transformative potential of AI, aiming to enhance operational efficiency, improve the clinician experience, boost patient engagement and outcomes, and fortify financial health — all critical focus areas, but especially so following the pandemic. Recent data from the American Medical Association (AMA) reveals that 38 percent of physicians now utilize AI in their practices, a significant increase from pre-pandemic levels that sat at 10 percent.

Watch the full 17-minute episode to learn more about how health systems are leveraging AI

As interesting applications emerge across medical domains, healthcare leaders must strategically prioritize and align AI use cases against business goals to achieve meaningful success. Let's explore the top use cases today and how to get started.

Top AI use cases in healthcare

Generative AI (GenAI), with its natural language interfaces, allows clinicians and staff to interact with AI systems using conversational language. This breakthrough can make AI solutions more accessible, engaging and user-friendly. 

When combined with automation, GenAI-powered solutions open up exciting possibilities for healthcare leaders. Here are two prominent use cases they should explore: 

Diagnostic assistance

AI systems are helping healthcare professionals make more accurate and timely diagnoses. By analyzing vast amounts of medical data, including patient histories, symptoms and test results, AI algorithms can identify patterns and provide diagnostic recommendations. This not only enhances diagnostic accuracy but also reduces the risk of human error, ultimately leading to improved patient outcomes.

Workflow automation

AI is already streamlining various healthcare workflows, from scheduling appointments to managing digital medical records. Intelligent automation can handle many repetitive administrative tasks, freeing healthcare professionals to focus on more complex, higher-order and patient-centric activities. 

Integrating AI solutions into existing electronic health record (EHR) systems and clinical workflows is a good starting point. Healthcare professionals are already accustomed to these systems, and AI should augment rather than disrupt their established processes. Additionally, AI-powered systems can help healthcare systems optimize resource allocation, ensuring the efficient utilization of staff, equipment and facilities.

Overcoming hurdles to AI adoption in healthcare

As the healthcare industry continues to experiment with AI use cases, several challenges stand in the way of successful implementation and adoption.

Stakeholder skepticism, distrust and fatigue

While clinicians and healthcare professionals are increasingly embracing AI, concerns persist around the "black-box" nature of some AI models due to their lack of transparency or interpretability. 

Fostering greater stakeholder involvement and providing education and visibility into how these systems work is crucial for building trust and acceptance. Healthcare organizations must prioritize efforts to demystify AI and clearly communicate their vision for AI, the expected benefits and risks, and the decision-making processes that will shape how end-users experience AI day to day. 

This can be achieved through effective data governance, responsible data practices and robust controls. Ensuring data quality, security and privacy is crucial as AI models rely heavily on the integrity of the underlying data. Additionally, implementing explainability measures that detail how or why an AI model generated an output can foster transparency and interpretability, enabling clinicians to understand and trust AI-driven decisions.

Prioritizing and managing demand

As AI continues to gain traction in the industry, organizations will face increasing demand from different lines of business seeking AI solutions tailored to their specific needs. This will present a challenge in terms of prioritizing and managing these requests effectively. Healthcare leaders must establish a clear framework for evaluating and prioritizing AI initiatives based on factors such as potential impact (good and bad), complexity of the project and alignment with business goals. 

Decentralization and shadow IT

Historically, the introduction of new technologies in healthcare has often led to decentralized and fragmented implementations. However, with the scale and cross-functional nature of AI, a more centralized and standardized approach is necessary. 

Healthcare organizations should focus on developing a centralized AI strategy and governance framework — often called an AI Center of Excellence or AI Factory. This approach will not only enable centralized AI project prioritization, resources and budgets, but it will ensure the consistency of data curation, solution interoperability and the effective management of AI solutions across the organization. This systematic approach will also enable better coordination and sharing of resources, data and best practices. 

Additionally, it is crucial to be aware of the potential dangers of shadow AI — the use of AI systems or models within an organization that are developed or deployed without the knowledge, approval or oversight of the organization's centralized AI governance framework. Shadow AI can pop up for many reasons, including a lack of awareness about officially sanctioned AI initiatives, a perception that IT or procurement processes are slow or inadequate, or simply a desire for quick and autonomous decision-making. This can lead to inconsistent results, potential breaches in data security and a lack of accountability.

Steps to successful healthcare AI deployments

As healthcare organizations navigate the complexities of AI adoption, there are some key action items that will lead to successful deployments that drive tangible outcomes:

• Prioritize high-value, manageable use cases: By carefully evaluating the healthcare organization's needs and objectives, you can identify the most impactful areas where AI can be applied effectively while accounting for the complexities of development and adoption. This strategic approach ensures that resources are allocated to initiatives with the greatest potential for return on investment — whether in the form of productivity gains, cost savings, better patient and clinician experiences, or improved outcomes.
• Facilitate collaboration between IT and business stakeholders: Successful AI deployments require seamless integration between technical teams and end users. This alignment ensures not only that AI solutions are reliable and technically sound, but that they meet the organization's specific requirements. Foster open communication and align perspectives to drive adoption and maximize the impact of AI solutions.
• Bridge critical IT skills gaps: Healthcare organizations often lack the necessary expertise in areas such as data science and architecture, which are essential for developing and deploying AI solutions. Consider upskilling your team through expert-led cyber range exercises and online labs and learning paths. Consultants can also help fill these gaps, bringing specialized knowledge and experience while providing guidance and support throughout the entire process. They can also train existing teams on the ongoing management of AI deployments.
• Properly evaluate technology before committing to a particular AI solution: Technology selection is a crucial aspect of any AI initiative. You should test each solution for efficacy, scalability and flexibility in a virtual lab environment. This enables more informed decision-making while safeguarding production environments — and all at a fraction of the cost and time you'd spend investing in a lab environment to do your own testing. You'll be able to make informed choices that align with your budgets and long-term goals, balancing the potential return on investment with the total cost of ownership.

By following these steps to successful deployment, healthcare organizations can unlock the potential of AI while maintaining the highest standards of patient care and trust.