Artificial intelligence (AI) has emerged as a game-changer in the healthcare industry, offering solutions to streamline administrative tasks and manage vast amounts of data. However, implementing AI in healthcare organizations comes with its own set of challenges, requiring significant resources and ongoing maintenance to ensure optimal performance.
According to Rob Havasy, senior director for informatics strategy at HIMSS, while some healthcare organizations have been using AI tools for tasks like interpreting imaging results for years, the introduction of generative AI tools and the rapid advancement of AI technology have posed new challenges for the sector. The increasing pace of change is beginning to overwhelm an already burdened workforce.
Here are three key considerations for health systems when implementing AI tools:
- Know your metrics: Before deploying an AI tool, health systems need to clearly define their goals and establish metrics for measuring success. A report by the Project Management Institute (PMI) revealed that only about half of healthcare projects are successful, with many failing due to a lack of clear goals and evaluation mechanisms. It’s essential to identify potential conflicts in goals, such as improving clinical care versus seeing more patients, and devise a plan to address them.
- Lean on informaticists and project managers: Utilizing project managers can help healthcare organizations navigate the complexities of deploying AI tools. Project managers can facilitate communication between different departments within a hospital and ensure that tasks are coordinated efficiently. In the healthcare setting, informaticists play a crucial role in using technology and data to enhance care delivery. By leveraging the expertise of informaticists and project managers, health systems can streamline the implementation process.
- Consider training data for under-resourced providers: Implementing AI projects in under-resourced healthcare organizations can be particularly challenging due to limited technical expertise and resources. While EHR companies are incorporating AI tools into their offerings, providers must ensure that these tools are trained on data relevant to their patient population. Monitoring the performance of AI tools and making necessary adjustments are essential to ensure their effectiveness in a real-world clinical setting.
In conclusion, the adoption of AI in healthcare holds immense potential for improving patient care and operational efficiency. By carefully considering metrics, leveraging informaticists and project managers, and addressing the unique challenges faced by under-resourced providers, healthcare organizations can successfully integrate AI tools into their workflows and drive positive outcomes.
