Through the continued journey of transitioning to a high-value, person-centric care model from a high-volume, process-centric care model, providers are forced to view care delivery through a new lens. As organizations consider digitization of their service offerings, having an effective analytics framework that produces actionable insights becomes imperative.
The need for actionable information continues to be a key strategic and operational gap for leaders of most healthcare organizations, and it presents challenges as they transition into value-based care. Provider organizations, particularly large, complicated health systems, have incredible amounts of data spread across disparate systems that do not easily communicate with one another.
Although many organizations have heavily invested in various data-aggregation and analytics technologies, many struggle to realize value from their investment to mobilize digital and clinical workflows. Although they realize the need to establish a governance layer at an enterprise level to cohesively activate data-based decision making, organizations are challenged with how to begin building a lean and an effective data-governance model that is agile and drives decisions.
High-performing healthcare organizations that have developed results-oriented digital and analytics frameworks have incorporated best practices that focus on building a multifaceted data governance structure with a clear vision of creating actionable information. Below are some of these best practices.
Conduct a current-state assessment of the organization to leverage existing strengths and talents. Meet the organization where it is: Objectively evaluating organizational capabilities in terms of strategic goals, operational needs, technology portfolio, data availability, data integrity, data literacy, and culture adaptability allows for identification of assets (technology, resources, and knowledge) that must be leveraged in the incremental build plan.
Establish a future-state vision. Identify key problem statements that data governance must solve for. Outlining clear problem statements helps identify digital and analytics capabilities that must be incrementally built upon.
Consider four key categories for problem statements that help achieve strategic goals for value-based programs:
- Retrospective view: How has the organization performed in key service areas?
- Current-state analysis: How is the organization performing in those areas of strategic interest? What are the current gaps?
- What will the future look like? What are the current trends in the market space that influence the organization’s digital strategy? What are key opportunities to tap into from a value-based-care perspective?
- What actions can the organization take today to affect outcomes? What value and return on investment (ROI) can the organization anticipate by making investments in certain value-based programs?
Create multidisciplinary data governance. Be a champion for participation across key business units and stakeholder groups. It is important to identify key consumers of analytics early on. Understanding the information needs of the stakeholders, their level of data literacy (i.e., their ability to understand and interpret data), and the ability to act on information offered via analytics determines the pace at which your organization can adopt a culture of knowledge-based decision making. Consider creating layers of data governance, each with a specific purpose.
Setting up a multidisciplinary and multilayered data-governance structure that includes the following is an important element of success:
- Guidelines for management of the quality of data being leveraged across the continuum
- Roadmap to incrementally building data literacy among consumers of analytics
- An operational framework that allows for maximizing of data exploitation for the organization’s benefit
- Roadmap to how analytics capabilities will be augmented as the organization brings in more digital solutions to support value-based-care programs.
Multidisciplinary data governance ensures that the analytic direction supports the strategic initiatives of the organization. It becomes an important mechanism in establishing data integrity, alignment of technology platform criteria, and producing actionable information.
Start small and build for scalability. Use a problem-backed approach to create tangible results and quick wins. Organizations that have successfully built action-oriented data governance have started small, often around a proof of concept, and gradually expanded participation and scope of governance guided by their strategic needs. A pragmatic, results-based approach allows organizations to establish building blocks of processes around data quality, data integrity, information visualization, technology integration, and data literacy.
Build for adaptability to evolving strategic needs. Quick wins via structured proofs of concept allow for increased engagement with governance stakeholders. They will also highlight the gaps in collection of data at the front lines. Consider establishing a change-management framework that helps manage the culture change in how the organization collects data, uses actionable information, and makes outcome-based decisions. Data governance will drive culture change within the organization; others will begin viewing data as a strategic asset, and data quality is a key factor in determining the value of the asset.
Build for sustainability by focusing on organizational literacy. Creating a change-management roadmap allows you to gradually build the required digital literacy and analytics skillset within your organization and among stakeholders. This minimizes risk and dependency on outside resources and creates ownership. As your organizational capabilities mature, the need to consume complex information increases. Plan to bring in new talent to your organization, including data scientists.
Build for supporting organizational and service line innovation versus being driven by technology capabilities. An effective data governance structure ensures that the organization builds internal capabilities (technology, process, and literacy) to harness the power of complex analytical information versus siloed application users. Unless closely aligned with service line innovation in service to high-value-based-care programs, technology innovations can become a burden for an organization to maintain. Data governance plays a key role in ensuring proofs of concept produce tangible results and value in support of these programs, creating a competitive edge for the organization in the market space.
Healthcare organizations that leverage the abovementioned best practices to avoid common pitfalls can be assured of sustained success in the journey to value-based care.
Principal Shaillee Chopra specializes in developing and managing innovative technology portfolios for health systems, accountable care organizations, and clinically integrated healthcare collaboratives.