Health systems have invested millions toward organizational intelligence—and rightly so. With a mature data and analytics program, organizations can improve healthcare across many pressing issues: delivering higher quality care, reducing healthcare disparities, increasing patient and workforce engagement, and transforming business operations. But a high-performing data and analytics program remains elusive for most.  

With the rapid uptake of artificial intelligence (AI) in healthcare, the stakes are escalating. This is particularly true as the technology rapidly evolves—and with it, associated considerations around compliance, competition, security, and ethical use and outcomes. Unless organizations have optimized data and analytics capabilities, they cannot safely or effectively deploy technologies like generative AI, machine learning (ML), and robotic process automation (RPA). In fact, leveraging faulty data could exacerbate disparities, lose the trust of patients and employees, and draw misguided conclusions that could put patient lives at undue risk.  

While other industries have begun to infuse AI into their businesses, healthcare lags behind, despite an influx of startups promising to revolutionize healthcare with emerging technologies. Effecting change and driving improvements through data and analytics requires healthcare organizations to first develop 3 essential capabilities.   

Fig. 1: Data Can Be Converted to Valuable Insights 

Fig. 1: Data Can Be Converted to Valuable Insights


How a Health System Leveraged Its Data to Regrow Its Market Share

Faced with a significant loss in market share despite its high performance and strong outcomes, a large Southern-based system turned to data and analytics to overcome this challenge. The health system formed a multidisciplinary leadership group focused on partnering with the analytics team to define and address the problem. 

The group identified and combined additional data sources that allowed advanced analysis to uncover factors contributing to the market share loss. The group developed predictive models that generated win-back strategies for patients who hadn’t engaged in 2 years or more. It used a segmentation model to generate patient personas, and it leveraged data on preferred patient visit times, locations, and communication channels to renew the health system’s focus on customer service and satisfaction. These needs might have been overlooked if the health system had maintained its siloed approach. 

The health system created a comprehensive strategy and executed a targeted activation program to successfully recapture and grow its market share—yielding a 47% visit conversion rate and more than $15 million in revenue.

Gain Value from Analytics and Overcome Common Pain Points 

Unlocking the value of an organization’s data can be a complex and intimidating challenge, but focusing on key foundational points can help yield immediate improvements.  

Organizations must have 3 essential capabilities for an effective analytics program: (1) oversight and program structure, (2) data management, and (3) analytics services. When organizations create an enterprise-wide analytics vision and a programmatic approach built on these 3 essential capabilities, they can address common pain points and optimize value generation from their data and analytics resources. 

Fig. 2: 3 Interrelated Essential Capabilities Unlock Value from Analytics

Fig. 2: 3 Interrelated Essential Capabilities Unlock Value from Analytics

1. Oversight and program structure

  • Pain point: Analytics aren’t aligned to strategic priorities.  
    Leading-practice solution: Program governance and leadership ensure analytic projects tie to strategic priorities of the organization. 

  • Pain point: No one is responsible for the performance of analytics resources to ensure alignment of decision-making with goals.  
    Leading-practice solution: An analytics leader who is empowered to allocate all analytics resources across the organization to actively support data-driven decision-making. 

2. Data management

  • Executives and clinicians don’t trust the data. 
    Leading-practice solution: A data governance framework with accountable ownership from the highest to lowest levels of the organization. 

  • Pain point: Financial, clinical, operational, and administrative departments argue over whose information is accurate.  
    Leading-practice solution: A data stewardship program, widespread data literacy, and common well-understood data definitions.

3. Analytics services

  • Pain point: Leaders don’t know whom to ask to get to the right data.  
    Leading-practice solution: An intake process with a single point of entry. 

  • Pain point: It takes too long to get information.  
    Leading-practice solution: Full transparency into prioritization and the analytics development cycle to address strategic gaps. 

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. 

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