Editor’s Note: Chris Cooper is the healthcare strategy leader for Collibra, a provider of data governance helping healthcare organizations maximize the value of their data across the enterprise.
Healthcare organizations have invested significant resources to make information available to employees at all levels, with the goal to enable better, more efficient decision-making for both patients and the organization. But these investments — electronic medical records, data warehouses, self-service business intelligence tools, to name a few — have also yielded an explosion of data. As an industry, we have arrived at a crossroads where we realize that a wealth of data and technology does not directly equate to better information and decisions.
It’s ironic that the same systems designed to put trusted data into the hands of people who need it have instead created data overload and confusion. Employees tasked with performing their daily duties and using data to make better decisions are awash in reports scattered across the organization. Limitations in these technology systems make locating and understanding the data an exercise in frustration.
This reality sheds light on the stark contrast between how we use technology to find information in our personal lives and how we use it at work. At home, we’re accustomed to the ease and power of sites like Amazon and Google that not only offer easy-to-use, one-stop-shop search capabilities that span multiple information silos, but also provide relevant, curated metadata, such as detailed product specifications.
In the modern healthcare enterprise, the process of identifying relevant information resources is difficult. Most enterprises do not have a consolidated source or directory across multiple reporting tools and data environments, and more often than not, the context needed to effectively determine the suitability of a given report or data set is lacking.
To address this issue, forward-thinking leaders are implementing data governance programs and platforms that allow data and information assets to be easily located using natural language-like search and can be linked to related assets such as data sets and clinical systems.
Data governance helps to mitigate the limitations of the data produced by disparate systems. It provides a framework to manage the context of data – where it originated, how it can be used and what is necessary to define its meaning.
Bringing governance to healthcare data is no simple feat. Many factors have converged to put pressure on healthcare organizations’ ability to properly govern the use of data through manual processes. These include:
· The dramatic growth in both the volume and diversity of data and information assets
· Compliance, regulatory, and legal issues, becoming increasingly restrictive on how data can be used and more severe in the penalties for improper use
· Security and data protection, which carry huge investment costs and major risks if not properly addressed
· The complexity of clinical care and the data used to document care delivery
· Decision-making that mandates the highest levels of accuracy, integrity, precision, and speed
Data managers, information stewards and data consumers have been forced to confront a stark reality: manual processes for data governance simply won’t work. Instead, we must commit to the principles and practices of automating data governance to ensure that data is being properly documented and approved for an increasing array of data-driven applications and workloads.
Only through automation can healthcare organizations efficiently:
· Track incremental changes and improvements over time, showing the positive, even indirect impact of data governance
· Achieve the vision of self-service analytics and business intelligence initiatives; making data as well as business intelligence tools self-service
· Remove administrative burdens that can mire governance projects
· Increase adoption of analytics and data-driven decision processes
· Support changing compliance scenarios through flexible business rules and policy management
· Improve security and data protection
· Make governance practical given the volume of data in healthcare, where there is simply too much data to use brute force
· Lessen the impact on staff to facilitate adoption of new governance processes
· Support organizational change though guided workflows around new processes
· Create the kind of “one-stop-shop” search experience users demand when seeking data
By automating these and other data governance processes, we can begin to see widespread adoption and use of data, policy driven protection of patient-related data, the ease of use employees have come to expect and sustainable use of data to improve decision making at all levels of the organization.
Although data governance is becoming increasingly essential to organizations in every industry, healthcare presents a unique set of challenges. The increasing volume of data for clinical, regulatory, operational and administrative demands puts pressure on healthcare organizations to implement effective data governance.
This requires involvement from administrators, clinical staff, business users and IT to ensure that the needs of every group are met. It also means that organizations simply cannot throw more bodies and bigger budgets at data governance. Instead, they must look to automation to handle data governance in a highly dynamic environment.
By implementing a solution that understands and adapts to users’ unique requirements, healthcare organizations can achieve substantially higher end-user adoption—a prerequisite for today’s increasingly competitive and fast-changing healthcare environment.
