How to Build a Healthcare Data Quality Coalition to Optimize Decision Making

healthcare data qualityHealthcare organizations increasingly rely on data to inform strategic decisions—a relationship that COVID-19 has amplified exponentially. This growing dependence makes ensuring the data across the organization are fit for purpose (i.e., data has appropriate quality attributes to serve its intended purpose) more critical than ever.

Decision-making challenges associated with pandemic-driven urgency, a variety of data, and a lack of resources have highlighted the critical importance of healthcare data quality as a prerequisite for any analytic use case. More specifically, COVID-19 has required specific value sets, urgent lab code updates, faster turnaround of data and analytics, all with resources, reporting structures, and communications more strained than ever. Additionally, as recovery activities began, the breadth and depth of data required to inform a path forward only exposed more questions and data quality issues.

Data-informed organizations will have a data quality coalition to develop and implement a well-defined data quality management strategy. A robust plan considers the people, processes, and technology necessary to define, evaluate, and monitor data quality. Having a data quality coalition and strategy in place allows for a quick, effective, and sustained response at an organizational scale, keeping the focus on the task at hand with all resources working within a well-defined structure.

Building a Healthcare Data Quality Coalition: Three Approaches

Organizational leaders often first identify important initiatives through grassroots efforts. Improving data quality and building a data quality coalition is no exception. However, once the initiative’s value becomes clear or reaches a critical mass across the organization, decision-makers tend to shift to a top-down administrative approach to amplify the importance and organization complexity. While both grassroots and top-down tactics have merit, a hybrid of the two will most effectively reduce the challenges and optimize the data quality management benefits of each.

Table 1 (adapted from a Dataversity education resource) summarizes three approaches to building a data quality initiative and coalition: grassroots, top-down, and hybrid.

Approach Operating Model Emphasis Benefits Challenges
Grassroots Decentralized—Independent prioritization with resources across departments and teams Self-service and management More autonomy/ ownership and subject matter expert (SME) Slower and more variation
Top Down Centralized—Single department controlling priorities and resources Policy, guidelines, and cost Faster and more cohesive Less autonomy and adoption
Hybrid Distributed—Coordinated decisions with decentralized resource management (recommended) Needs, impact, and maturity Cohesive, strong buy-in, SME, fast Coordination and complexity

 Table 1: Three models for building a data quality initiative and coalition.

Grassroots Enables Individual Input

Grassroots, also known as bottom-up or decentralized, initiatives allow team members to identify the significant problem, determine how best to solve it, and rally willing parties to support the effort. A more formal implementation might include department- or team-level prioritization and partial resource time carved out to focus on data quality.

A grassroots operating model for data quality may allow a team more autonomy to solve a problem within their subject area. However, this decentralized approach will likely undermine solving similar issues across the organization at scale due to three main challenges:

  • Poor alignment between the needs of a siloed subgroup and organization-level objectives.
  • Lacking technical capacity (passion and goodwill cannot sustain the effort).
  • Siloed resourcing that may only scratch the surface versus addressing the actual root cause.

A Top-Down Initiative Leverages Leadership

Top-down initiatives, or centralized operating models, rely on organization leadership to determine high-level goals and projects, then communicate the plan to the teams and individuals who will carry it out. A more subject matter-centered implementation of this approach might involve a single centralized department controlling data quality prioritization and resources.

While a top-down approach for data quality may better achieve scale while attempting to solve well-aligned problems, there are two main challenges to watch:

  • Prioritizing issues because they are most common, not necessarily most impactful.
  • Yielding less SME participation and adoption due to less autonomy.

A Hybrid Approach to Data Quality Calls for Organizational Alignment and Individual Adoption

A hybrid approach, or distributed operating model, relies on coordinated decisions with decentralized resource management for data quality. This approach promotes organizational alignment and strong adoption by SMEs, which helps achieve the depth and scale necessary to solve impactful data quality problems. The distributed teams achieve better scale by investing time and resources in a standard set of processes and technology instead of relearning the same lessons.

However, a hybrid approach requires significant coordination due to the increased complexity of working across an organization while focusing on particular problems. For example, a data quality coalition that leverages a hybrid approach and distributed operating model will include a small core team to manage processes and technology. This core team will serve as a center of data quality excellence that distills and operationalizes guidance from organization leaders, managers, subject matter experts, and analytics professionals—all with a vested and shared interest in ensuring data quality.

The coalition will agree on a standard approach to advance proven processes and avoid spending resources reinventing the wheel. It needs support from the highest level of leadership for organizational alignment of objectives and resources. Subject matter experts will help inform the coalition’s priorities, as they will understand how best to understand what incentives drive good data quality by data producers. Analytics professionals will no longer be the default solution to poor data quality and will understand better where to focus their efforts.

The Hybrid Healthcare Data Quality Coalition Meets Today’s Fast-Paced, High-Stakes Demands

The rapid pace and significant consequence of healthcare data-informed decision making today require that data and analytics be incredibly high quality. Organizations need to have a data quality management strategy and data quality coalition ready to support new use cases (COVID-19 and beyond) and issues that hamper the ability to make the right decisions. An organization can emphasize specific department-level needs, leverage cross-organizational subject matter expertise, benefit from strong buy-in, and move quickly by building a distributed coalition that leverages a hybrid of bottom-up and top-down approaches.

Additional Reading

Would you like to learn more about this topic? Here are some articles we suggest:

  1. How to Run Analytics for More Actionable, Timely Insights: A Healthcare Data Quality Framework
  2. Why Health Systems Must Use Data Science to Improve Outcomes
  3. Three Must-Haves for a Successful Healthcare Data Strategy
  4. Smartsourcing Clinical Data Abstraction Improves Quality, Reduces Costs, and Optimizes Team Member Engagement
  5. Quality Data Is Essential for Doctors Concerned with Patient Engagement

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