Healthcare has always relied heavily upon data as its most valuable resource. As the industry moves further into the electronic management of scattered information sources — insurance claims, physician notes, medical records/images, etc., frustrations with existing processes persist as the volume of available data grows. Developing a digital strategy to drive maximum ROI while minimizing disruption to existing workflows poses a challenge for organizations. Many healthcare leaders understand the need for data governance, but overlook the importance of:

• Understanding where data is originated, stored, and accessed across the enterprise
• Implementing effective processes to promote sustainable data governance, and
• Developing the right resources and supporting assets to leverage healthcare information.

Once business goals are recognized at the highest level, it is crucial to start small by focusing on operational excellence to validate different applications and use cases. This will steer digital projects away from lofty pursuits and toward data-driven investments that will demonstrate immediate stakeholder value and long-term sustainability.

Perhaps the most prominent topic in future-oriented business is Artificial Intelligence, an area of computer science emphasizing the creation of intelligent machines to work and react like humans. Today physician burnout costs an estimated $4.6 billion annually to cover physician turnover and reduced clinical hours [2]. Clinicians are left to balance patient care delivery with an overwhelming burden of paperwork and manual administrative duties that strain operations and leave an immense volume of medical information underleveraged. AI presents several opportunities to slow physician burnout and improve resource utilization in the clinical setting. Among them, the ability to eliminate tedious tasks for greater focus on patient care and automate repetitive learning and discovery to support decision-making in an increasingly complex clinical environment. But what must first be in place to then leverage advanced analytics? The answer lies with consistent information governance that mandates providers develop standardized policies and procedures for creating and managing medical data. The American Health Information Management Association’s principles of information governance champion the following [3]:

• Educating clinicians and other data-creators about the importance of information governance across the organization.
• Improving the quality of data at the source with clinical documentation improvement initiatives.
• Investing in open, standards-based data warehousing infrastructure that prevents the development of data silos.
• Ensuring that all data assets include appropriate metadata to improve accountability and extend the usability of datasets.
• Maintaining high standards of data privacy and security to protect patients from unauthorized uses of information.

These measures among others lend visibility into what / how much data is available, its utility in advanced analytics, and the types of patient outcomes it can influence. Above all, data integrity is paramount. Understanding and controlling the processes that manage information from the beginning is a crucial first step toward strong data governance and must be established before trying to tackle business problems using advanced analytics. Keep in mind — if the process governing of your health data is out of control, AI won’t magically fix it [4].