The lack of common rules and guidelines in the deployment of AI in healthcare may lead to a lack of trust in AI-powered solutions. Healthcare firms must ensure discipline and accountability in the rollout of AI for healthcare delivery.
Laura Craft filed this report for CDO Trends:
By 2021, Gartner predicts that 75% of healthcare delivery organizations (HDOs) will have invested in an AI capability that is explicitly improving either operational performance or clinical outcomes. The more activity there is around using AI in healthcare, the greater the need for HDOs to establish AI governance.
AI governance is necessary, especially for clinical applications of the technology. However, because new AI techniques are largely new territory for most HDOs, there is a lack of common rules, processes, and guidelines for eager entrepreneurs to follow as they design their pilots.
Most HDOs have not developed an enterprise strategy for how AI will be introduced, invested in and managed. This leads to a lack of trust in AI-powered solutions and creates a new problem that only healthcare provider CIOs are equipped to address. It is important that CIOs take a lead role in making sure there are discipline and accountability around the use of AI in HDOs.
Craft shares three actions that healthcare provider CIOs should take to ensure that any implementation of AI is safe, protected and realizes its potential.
- Establish an AI Governance Council. AI governance need not be separate from an existing leadership body of authority. If a strategic leadership council for a data and analytics program already exists, then this is the most obvious fit, as AI is a natural extension of an analytics program.
- Establish Common Definitions and Strategic Value of AI. Organizations must have a common definition of AI to have productive conversations around its value and investment. This means involving clinicians, scientists, technologists and end-users in the conversation to produce a universal agreement across all stakeholders. CIOs should use the AI governance council to facilitate discussion and formally adopt an enterprise-wide perspective.
- Anticipate Any Data Challenges. Data is often a big obstacle to the smooth and successful implementation and use of AI. When attempting to curate a clean, complete and accurate dataset, HDOs are challenged by poor data quality; lack of needed data; incomplete data; and issues of data consent, privacy, and security. To address this, successful CIOs scrutinize current data governance practices and data acquisition methods by providing guidance for the types of data that will be needed and upgrading data curation tools and services
