Most healthcare digital transformations fail — Start small with data governance

There is always excitement when it comes to the potential for digital transformation in healthcare. The impact artificial intelligence (AI) and machine learning (ML) can have on organizations and patients’ lives is certainly cause for exploration. Recent survey data from the American Hospital Association suggests ~85% of hospital executives claim digital innovation is part of their long-term strategy [1]. However, foundational data levers required to yield a positive return on investment are often omitted from the conversation. Namely, a deep understanding and control of internal processes to support proper data governance across the organization. Digital technology is only as valuable as the underlying data it leverages. How can organizations prepare for the digital transformation already underway in the healthcare space? It starts with identifying the strategic business objectives to be achieved through digital change, then prioritizing the reliability, accuracy, and accessibility of health data at its origination. In order to capitalize on innovative digital strategies, leaders must first optimize operational processes and uphold strong data governance structures across the enterprise.

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].

The holistic patient experience is another area ripe for digital innovation. Artificial Intelligence is often associated with a more extensive time horizon for development and application, but Virtual Health has emerged as a more imminent solution to pain points that plague both providers and consumers across the end-to-end patient journey. By employing technology that is already commonplace among digital consumers (i.e. video interface, online portals, mobile applications), remote clinical access transcends traditional methods of care delivery (i.e. hospital visits) to afford better health outcomes at scale that would be impossible with existing digital infrastructure. This provides patients a single-point solution to access the services they need while reducing resource expenditure for providers. Yet, driving the patient journey forward through virtual health is only feasible when information is collected through reliable data sources. Before determining what data assets to leverage, providers must consider the processes that support those inputs. The World Economic Forum outlines a few key enablers for generating return on digital investments as part of their Data Management Lifecycle Framework [5]:

  • Strong data infrastructure to enable origination
  • Robust data warehousing to enable storage
  • Capabilities to structure and analyze data
  • Tools and assets to communicate and take action on insights

Adhering to the above not only supports an organization’s digital strategic priorities, but also the foundations for population health management across the enterprise. As providers struggle to understand how and where big data fits into their model of care, structured data governance will continue to be the focal point of achieving positive ROI from digital innovation.

It would be remiss not to acknowledge the potential for blockchain to completely upend the way data is supported and managed in healthcare. Its functionality inherently promotes transparency, accessibility, and security of health information across the value chain and emphasizes the same data governance principles previously mentioned. Platforms supported by blockchain facilitate efficient and secure exchanges of health information between related parties across the care continuum, thereby translating historic and real-time data into detailed chronological record of the patient care journey [6]. This offers clinicians insights for delivering informed, synchronized care. In a similar fashion, successful blockchain implementations require clean data. The interoperability provided is based on distributed data ledgers that once recorded are immutable. This beckons organizations to evaluate the integrity of existing information assets that will underpin the technology well before implementing blockchain into their infrastructure. Again, the common theme of prioritized data governance persists as the catalyst for driving return on large-scale digital investments.

The collective healthcare ecosystem stands to greatly benefit from the digital transformation underway in what is a historically laggard market sector. Organizations are racing to innovate and gain a competitive edge by being first movers in adopting innovative technologies. Yet doing so effectively requires more up-front problem-solving. Consider the framework below when undergoing digital transformation projects: