3 Analytics Capabilities Healthcare Organizations Need
1. Oversight and program structure: Often an overlooked Achilles’ heel for enterprise analytics programs, this capability ensures the right emphasis on leadership, program governance, and staffing to support the organization’s enterprise analytics program needs. To best maximize value, the analytics program must first align with the organization’s strategy. This involves partnering with leadership and key stakeholders across the organizations to determine key data and analytics requirements that will inform organizational decision-making and align resources to priority operational and strategic needs.
For instance, analytics and AI are closely connected—data informs AI initiatives and learning, and analytics validates and measures outcomes. For this reason, both data governance and the AI governance bodies should be very closely connected, working together toward organizational goals endorsed by the overarching analytics program governance body.
Analytics Leaders Are Critical to Program Success
An organization’s analytics leader must have the right mix of technical skills and emotional intelligence to foster collaboration, advise, and mediate concerns.
Many health systems have experienced new analytics leaders arriving with a predetermined fix-it strategy without fully understanding the challenges and needs of the organization. Making assumptions about similar problems and repeating what may have worked with a previous organization is not an effective approach for their new organization. These mismatches often end in struggle or failure.
One healthcare organization found an analytics leader whose ideas and vision aligned well with its own. This new leader worked collaboratively to identify and align key stakeholders and organizational leaders to focus on developing a common enterprise analytics vision aligned with the organization’s strategy and current and future needs. By building support and trust, he established key processes, aligned skills, standardized tools, and hired additional resources that advanced enterprise analytics. His efforts have helped the organization make significant progress toward high reliability and workforce optimization.
2. Data management: Data quality is vital to the success of enterprise analytics programs. But many healthcare organizations struggle to collect and maintain accurate, validated, and well-defined data. The data management capability focuses on data governance, data architecture, and tools and capabilities. It includes oversight of data infrastructure and curation processes—including data sources, transfers, data lakes/warehouses, the data definition, data management, and access and security activities to ensure the data’s integrity and availability.
Without an effective data management strategy and enforced governance policies, poor data quality prevents the organization from achieving value through AI and advanced analytics. For example, AI may require diverse types of data, involve sensitivities around privacy, and require techniques to address bias and hallucinations. These solutions often require sensitive data, including protected health information, that must not be exposed to security risks and should not contain biases that could lead to unpredictable results.
Data Quality Starts with Coordinated Stewardship
A children’s hospital that has materially improved the management and quality of its data began by developing partnerships with data creators and customers. Through these partnerships, the organization developed roles and a stewardship program that clarified ownership and responsibility with data creators.
Bringing together data stewards across the organization, the analytics team and key stakeholders developed common data definitions, identified missing and inaccurate data, and implemented operational processes to improve data entry and validate information prior to distribution. Consequently, the enterprise analytics team has curated trusted data sets for self-service analytics. Citizen analysts develop their own tools, analyses, visualizations, and collaborative data sets. This enablement has reduced the time to produce results and impact decision-making, and has allowed the analytics team to focus on emerging priorities for the organization.
3. Analytics services: The analytics services capability focuses on analytics distribution, collaboration and communication, and change management and training. It includes oversight of the resources and techniques used to deliver analytics services—including end-user collaboration and support, data modeling, data visualizations, self-service, and advanced/predictive analytics. An enterprise analytics program’s ability to understand the needs of its stakeholders requires business acumen, critical thinking, problem solving, and a customer service-based mentality. Giving end users an opportunity to provide feedback can lead to increasingly faster, more insightful analytic outputs.
Often, enterprise analytics programs struggle to keep up with the demand for their capabilities, resulting in the perception of poor service—but shared organizational goals and setting reasonable expectations can prioritize resources toward achieving results with the greatest impact.
Sharing Goals Increases Team Capacity and End Results
One healthcare organization applied project management principles to prioritize the analytics team’s efforts. The team aligned its resources to the organization’s strategy and developed shared goals with respective stakeholders for critical initiatives. This approach aligned relationships in which analysts, clinicians, administrators, and operational leaders worked together to understand which levers could be changed to improve outcomes and transform the organization.
One of the key initiatives was improving preventive cancer screening rates. The organization brought together leaders from primary care, quality, specialty care, patient access, performance improvement, population health, marketing, and enterprise analytics. By understanding this complex problem through the different lenses of these leaders and building consensus on a single tool to serve as the source of truth, analysts developed a 360-degree patient view and identified outreach techniques based on communication preferences and behaviors. Ultimately, the use of advanced analytics aided in additional provider capacity for more than 2,000 annual visits, a $3.3 million annual contribution margin in new patient volume, and a reduction of 6.5 days in the screening process time.
Analytics Insights Are Essential for Healthcare Transformation
Today’s healthcare leaders need to harness the power of data and analytics to stay relevant and competitive. Organizations that lack a mature data and analytics program risk being left behind or using technology in ways that could produce harmful and unpredictable outcomes. But organizations that align on the 3 essential capabilities and cultivate a sophisticated, modernized data and analytics program are positioned to safely and effectively adopt AI and other tools to enable transformative change to care delivery, digitalization, patient and provider engagement, and business operations.
Next Intelligence Takeaways:
A high-performing analytics program requires:
- A vision with a programmatic approach: Create and execute a thorough roadmap and implementation plan of the 3 interrelated components to connect all data and analytics activities.
- A coalition of engaged senior leaders: Ensure leadership consensus on measurable outcomes, shared accountability, and alignment on data and analytics priorities.
- A collaborative analytics leader: A leader well-positioned to create a culture of collaboration around data and analytics throughout the organization.