Improving Data Governance and AI Implementation in Healthcare
Healthcare organizations are increasingly relying on data analytics and artificial intelligence (AI) to optimize their operations and improve patient care. Core applications such as electronic health record systems, customer relationship management tools, and enterprise resource planning solutions generate vast amounts of valuable data. However, this data can often be siloed, hindering the ability to gain actionable insights.
Data quality and governance are major priorities for healthcare organizations as they explore AI’s role in their workflows, especially given the pressures on clinical teams and tight budgets. As a result, healthcare organizations are working hard to refine their existing data infrastructure to prepare for the implementation of generative AI solutions.
Recent research by the Harvard Business Review Analytic Services, sponsored by Amazon Web Services, reveals a trend across industries: 49% of respondents are focused on improving data quality and cleaning, while 41% are enhancing their data governance policies and standards. This trend is particularly relevant for healthcare, where the importance of a robust data strategy will only grow with widespread AI adoption.
Evaluate Your Current Data Maturity
The size of an organization may influence its approach to data, but the key factor is overall data maturity. Organizations should determine if they have established data governance practices and existing analytics capabilities. Larger institutions may have an advantage, but smaller organizations can also excel if they have more advanced data maturity.
Various frameworks can help organizations assess their readiness for embracing these changes. The updated HIMSS Analytics Maturity Assessment Model aims to help providers prepare for AI adoption, while Gartner offers more general benchmark resources. Partnering with an experienced consultant is generally the best approach to evaluate your current data strategy. Once you’ve fully leveraged the data management and analytics capabilities of your core vendors, it is also valuable to consider augmenting and channeling your applications into a modern data platform. This process also involves adopting cloud-based solutions and more up-to-date approaches to collecting, managing, storing, and moving data through data platform modernization.
Focus on the People Aspect of Data Governance
Data governance is the foundation for treating data as a valuable asset, encompassing how it’s managed, protected, and used. It’s not a supplementary consideration; it must be a core component of any forward-thinking healthcare organization. This holistic approach allows business and technical teams to connect more effectively on data and clearly define and redistribute responsibilities.
Organizations can usually mature data governance alongside AI governance because AI solutions require data to be efficient. AI governance establishes standards and methods to address bias, transparency, and risk associated with a tool, aligning with data governance principles. The human aspect of governance cannot be overstated.
Organizations must communicate with and involve stakeholders who will be relying on such solutions and requiring data. Key questions that organizations should consider include the following:
- What training and education are needed to prepare a workforce for an AI implementation?
- How can a solution free up team members to shift from rote tasks to higher-level work?
- What is the process of evaluating a solution for specific workflows?
This shift also includes a cultural change. While it is common for team members to be apprehensive about new technologies, organizations must clearly communicate their expectations for AI and try specific use cases. A work environment that embraces change over fearing the “unknown” will be essential. Being able to convey the meaningful connection between a technology and the business or clinical processes that it will impact is a fundamental skill that every organization will need to develop to succeed in its data and analytics efforts.
Being able to convey the meaningful connection between a technology and the business or clinical processes that it will impact is a fundamental skill that every organization will need to get better at in order to be successful with data and analytics efforts going forward.
This article is part of HealthTech’s MonITor blog series.
