The federal healthcare community has an unquenchable appetite for qualitative and quantitative data, especially when that data has the potential to help agencies improve patient services and outcomes, inform public health professionals and drive decision-making.
However, the variety, volume and velocity of data that agencies collect, combined with the limitations of legacy systems, makes it difficult to access and share that data in an already siloed environment. Those limitations on sharing, in turn, prevent federal health agencies from finding subtle connections between various data points to answer critical program questions. It further prevents them from realizing predicative analytical capabilities that would augment the decision making required to mitigate or prevent the next public health crisis.
Solving this problem has taken on a new urgency in the wake of the COVID-19 pandemic. Agencies are dealing with hundreds, if not thousands, of non-standardized systems, each with its own isolated data set. In many cases, stakeholders of the larger ecosystem are unaware of data outside of their purview that can help them. Systems are not designed to allow easy sharing of data.
Federal health agencies are making progress in breaking down the barriers to sharing, but there is still considerable ground to cover. As CIOs are brainstorming their “infrastructure of the future” and evaluating enabling solutions, there are four key things they should bear in mind:
- Data modernization or transformation is a journey, not a destination. There are no shortcuts or quick pathways to success. It is also not a standalone initiative—those strategizing the transition must consider the entire environment holistically, including the way data is generated and stored, the analytics tools applied to it, along with ongoing or planned technology transformation and cyber security initiatives.
- Data transformation and modernization efforts must be incremental, agile and iterative. Several organizations have taken a giant leap in the right direction by appointing chief data officers and setting up CDO offices. CDOs will be instrumental in helping define the vision, governance, best practices and roadmap for data transformation. They will act as visionaries and orchestrators, but will need the support of the entire ecosystem to be effective. Naturally, chief information officers, chief information security officers and program stakeholders will all play a key role in defining the use cases and planning system implementation, driving technology decisions that enable a successful implementation. But organizations will need a vision of where they want to head and identify use cases to implement.
- Culture change is essential for data modernization to succeed. Data modernization can be a daunting initiative with no clear path of how to implement it. In most cases, defining top-line questions for the agency to advance its mission goal is a good starting point. The project team should also identify the data required to answer these priority questions and identify few use cases that will provide some quick wins. But, to realize success, the agency must move towards a culture of sharing data. Program teams tend to be extremely mission-focused. A strong governance framework and change management program can mitigate risk, foster employee buy-in and allay employee concerns associated with oversharing or violating privacy laws.
- Be ready to deal with bottlenecks in legacy systems. Modernizing data necessarily entails modernizing technology as well. The level of skill and maturity level available among federal agency staff (government and contractor) varies from one organization to another. The government as a whole has significant work ahead to ensure that the right level of human capital is available to address the growing needs of data science and data analytics. In addition to training and retooling of human capital, federal organizations are leveraging the deep expertise available in academia through collaboration with universities.
Data has power to change the entire healthcare landscape and bring about positive outcomes to public health. The public health outcomes that can be achieved with the right data transformation efforts – executed in the right way – can completely change the healthcare landscape and quality of human life.
Whether as a defined initiative or an organic evolution, most organizations have already started their journey. It is not something that can be implemented as a standalone initiative. There are many technical and non-technical factors that are needed to make this effective including policy updates, platform modernization of platforms to facilitate data sharing across the ecosystem, business process reengineering, instituting governance, the choice of tools, subject matter expertise and security.
Right now, with a recent and ongoing public health crisis adding urgency, there is significant momentum on leveraging the power of data and defining the art of possible. Federal agencies should lead the charge.