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The Role of Data Governance in Controlling COVID-19

First off, on behalf of IIA, I would like to acknowledge the amazing work healthcare organizations have accomplished during the pandemic. It’s been inspiring to read about the progress and successes as well as hear firsthand how organizations have been innovating to try to stay ahead of the disease while keeping organizations safe and operational during this very stressful and challenging time. As many of you likely know, healthcare organizations are being tapped by all levels of government during the pandemic – county, city, state, and federal groups such as HHS, CDC, FEMA, CMS – to inform decision making and adjust local and national models. Data governance is playing a critical role in this process for a number of reasons and for those organizations that have an established data governance program in place, it can be assumed they are faring much better than those without. This is also an opportunity for organizations not in the healthcare space to learn and be prepared should some global event suddenly disrupt their industry.

A bit of context on the COVID-19 reporting efforts: On March 29, 2020, the Federal COVID-19 taskforce requested that every hospital, hospital laboratory, and acute care facility respond to 32 questions and provide over 70 data points daily. In the beginning of the pandemic reporting, all data was due by 5pm Eastern regardless of facility location but has since shifted to a time that is flexible for each organization as long as there is consistency in reporting times. All data was reported directly to the state or CDC (varied by state based on authorization) but on July 15, 2020, the federal taskforce unexpectedly switched up the process and required all data be reported directly to the TeleTracking Portal managed by Health and Human Services (HHS), the umbrella organization for which the CDC falls under. One can only imagine how challenging this was for healthcare organizations already scrambling to keep up with the reporting requests and requirements that continued to shift and evolve.

So, in these extraordinary times, what are the hallmarks of a data governance program that could support this type of crisis reporting effort, especially one with many complexities and moving parts?

  1. Collaboration: Cross functional leaders all have an important role to play when it comes to data governance; in order for data governance to work effectively, one group cannot dictate how to do things without input from others. For healthcare organizations: care providers, analytics leaders, business operations, finance, etc. all need to understand one another’s roles as it relates to data collection and reporting and how they will function as a team to accomplish defined goals. This level of collaboration may result in a data council where diverse roles come together frequently to discuss data related topics.

  2. Role Clarity and Accountability: When it comes to working with the data, roles, responsibilities, and expectations need to be clearly defined e.g., who owns the data, who’s responsible for its quality, who has access to the data, etc. This is especially critical in times of crisis as all systems and people need to be ready to go without hesitation. Some organizations that have prepared for crisis reporting run simulations on a quarterly basis whether it’s planning for a security breach or natural disaster, governance teams have helped organize such activities. Further, roles such as data owners, data stewards, and data custodians are also becoming more commonplace as are data governance councils (as noted above) that embody a diverse group of roles and functions to oversee data policies and standards.

  3. Communication: In order to maintain progress, communication and transparency is critical for cross functional leaders especially in a crisis situation; daily meetings may be required to keep everyone informed on expectations and deliverables.

  4. Data Accessibility: Analysts need to be able to access raw data from one or more source systems in near real time as required.

  5. Data Literacy: Anyone touching or manipulating data should have the basic education and understanding that the quality of the results begins with data capture; garbage in, garbage out (GIGO) can be avoided by educating people on proper processes and how each step impacts the models and final output.

  6. Data Terminology: Data dictionaries and data glossaries should be established and accessible so everyone is working with the same understanding of terminology. For example, something as simple as “length of stay” can be classified differently depending on each department’s definition. All functions should sign off on enterprise naming conventions that everyone understands and can support. See this blog by IIA’s Bill Franks that further describes this example.

  7. Flexibility: Leaders need to be nimble and prepared for shifts in demands and requirements; contingency plans should be in place if expectations and deliverables should change mid-stream as they have with the recent HHS reporting directive.

While this is not an exhaustive list, it is meant to capture some of the high level elements of a data governance program and what’s needed to meet reporting demands either internally or externally. Implementing data governance – especially across the enterprise – is not easy or quick so as with any new initiative, leaders from change management should be included to make the process as seamless as possible. The reality is you need to build this capability over time, as this 2017 case study, from a leading healthcare provider who is now in the midst of the chaos, proves.

Be well, stay safe, and thank you again to all of our healthcare workers.