How to harness analytics for data-driven value-based care

Despite the broad acceptance of value-based care (VBC) payment models, a 2025 survey shows that barriers such as data interoperability and the cost of building technology infrastructure may soon slow adoption. To advance the adoption of this model and thrive under it, healthcare organizations must prioritize data-driven value-based care.

By applying analytics across the care continuum, health teams can turn data into actionable insights that drive better overall performance, including patient outcomes, operational efficiency, and financial sustainability. In this article, we’ll explore the power of data-driven approaches and how to implement them by covering:

• FAQs about data-driven value-based care
• How to utilize analytics for data-driven value-based care 

FAQs about data-driven value-based care

Here is a quick summary of what you need to know about data-driven VBC:

• Data-driven value-based care involves using analytics tools to extract and apply insights to workflows and interventions.
• The top benefits of a data-driven value-based care approach are proactive care, enhanced financial performance, and stronger payer-provider alignment.
• The challenges of implementing value-based care analytics include poor data quality, a lack of interoperability, and resistance to workflow changes.

What does data-driven value-based care mean?

A data-driven approach to value-based care involves using analytics tools to extract and apply insights to workflows and interventions. Because VBC contracts base provider compensation on improvements in the member population’s overall health, a data-driven approach is the best way to make informed decisions that yield better patient outcomes, higher care quality, and cost savings.

What are the key aspects of data-driven VBC?

Key elements of data-driven VBC approaches include:

• Data aggregation: Health systems must be prepared to aggregate data from various sources, including electronic health records (EHRs), claims data, social determinants of health (SDoH), and other repositories, to access a comprehensive view of patient health.
• Predictive analytics: Providers can use data to predict health risks and intervene early, preventing adverse health events and the costs associated with them.
• Performance measurement: Tracking outcome metrics like patient satisfaction, cost efficiency, and readmission rates can signal a health organization’s performance under a value-based care payment model.

What are the benefits of a data-driven approach to value-based care?

The benefits of a data-driven value-based care approach include:

• Proactive care: Instead of waiting for a patient to experience a health emergency, analytics allows providers to intervene early, preventing complications and improving overall care quality.
• Enhanced financial performance: Data-driven VBC ensures accurate risk adjustment. By capturing a complete picture of a patient’s health, providers ensure they receive the appropriate funding to manage complex conditions and population needs.
• Stronger payer-provider alignment: Shared data acts as a single source of truth. When all interested parties look at the same performance metrics, it fosters a collaborative environment focused on the patient.

What are the challenges of implementing value-based care analytics?

While the benefits are clear, the path to becoming a data-driven healthcare organization often contains obstacles, such as:

• Poor data quality: If data is inaccurate or outdated, the insights derived from it will be untrustworthy. Ensuring clean data is a continuous challenge for IT departments, but it ultimately results in more reliable analytics.
• Lack of data interoperability: Healthcare data is notoriously scattered across electronic health records (EHRs), claims systems, and other sources. Integrating these disparate sources into a cohesive, longitudinal patient record is a common technical barrier to data-driven value-based care.
• Opposition to new workflows: 80% of healthcare organizations cite provider resistance to workflow changes as a barrier to VBC growth. Success in implementing data-driven workflows requires analytics tools that automate and simplify data analysis rather than adding another layer of administrative burden.

How to utilize analytics for data-driven value-based care

Here is a quick rundown of the steps needed for value-based care analytics:

1. Leverage a high-performance data platform.
2. Understand the population.
3. Optimize patient outreach.

Let’s take a closer look at these steps in detail.

1. Leverage a high-performance data platform

Data-driven VBC demands a sophisticated infrastructure that turns massive data volumes into a clear roadmap for action. Implementing a high-performance analytics platform is the foundational step in achieving a data-driven approach that balances clinical excellence with financial health.

The right platform should manage:

• Unified data integration: Consolidate disparate data from EHRs, claims, and social determinants of health (SDoH) into a single, longitudinal patient record.
• Proactive risk stratification: Use AI-driven insights to identify high-risk patients before they require costly, acute interventions.
• Performance visualization: Provide intuitive dashboards that enable leaders to monitor quality metrics and financial trends in real time.
• Point-of-care insights: Deliver actionable data directly into the clinician’s workflow to ensure every patient encounter is high-quality and informed.

By aggregating complex data sets into a single source of truth, Arcadia’s healthcare data platform enables teams to drive proactive care, ensure high-quality outcomes, and maintain long-term financial sustainability. The result is a more resilient healthcare organization equipped to act on relevant insights across the entire care continuum.

2. Understand the population

To effectively engage patients, healthcare organizations must first understand the populations they serve. Stratification is a crucial step in this process. As Anna Basevich emphasizes in Arcadia’s Byte-Sized Booth Talk at HIMSS, merely dividing patients into low-risk, rising-risk, and high-risk cohorts is not enough. Each cohort comprises individuals with unique needs and contexts. The key is to dive deeper and create more tailored groups within these cohorts.

Low-risk

The low-risk population typically consists of young and relatively healthy individuals who may not see immediate value in routine primary care. However, it is essential to educate them about the significance of preventative care and regular check-ups. For instance, when reminding patients about cancer screenings, it is essential to address their age-specific needs to increase response rates.

Since the low-risk population is generally more tech-savvy, healthcare providers can leverage technology to engage them effectively. Utilizing platforms like MyChart for virtual visits and appointment scheduling can make healthcare more accessible and convenient for these patients.

Rising-risk

The rising-risk population includes patients with early-stage chronic diseases or specific healthcare challenges. To engage this group, healthcare providers must tailor their communication to each patient's condition. This could involve providing evidence-based guidelines and emphasizing the value of primary care for managing chronic conditions.

Additionally, identifying barriers to care is crucial for this population. Leveraging data to understand preferred contact languages, transportation challenges, or affordability concerns can help providers remove obstacles to care and improve patient engagement.

High-risk

The high-risk population requires vigilant engagement to ensure they do not miss essential follow-ups and care. Personalized, rapid outreach is vital in this scenario. Healthcare providers should use technology to schedule follow-up visits, send educational materials, and offer support through care management programs. Engaging family members can also be beneficial in coordinating care and ensuring patients receive the necessary attention across their care continuum.

3. Optimize patient outreach

Data-driven value-based care informs effective patient engagement and outreach, helping healthcare providers identify patients who can benefit from specific outreach efforts. Analytics can also help address social determinants of health (SDoH) and behavioral health challenges.

Basevich offers the following best practices for healthcare organizations looking to implement data-driven patient outreach strategies:
 

• Leverage data for stratification. Use data to identify actionable cohorts of patients for targeted messaging and engagement
• Tailor communication. Ensure patient outreach resonates with individuals by personalizing messages based on their conditions and preferences
• Understand barriers to care. Collaborate with care managers and social health workers to identify patient concerns and address potential obstacles to care
• Empower providers. While data is essential, it should never replace genuine human conversation. Providers should use data as a tool to inform and complement their understanding of patients' unique needs and experiences
• Prioritize consistency and speed. Consistent, timely patient follow-up can make a significant difference in patient outcomes, especially for high-risk populations

Specific use cases include:
 

• Behavioral health impact scores: Build behavioral health impact scores for patients with chronic conditions and behavioral health challenges. These scores help identify patients who could benefit from behavioral health interventions to improve both their behavioral health and traditional medical outcomes.
• Addressing social drivers of health: Leverage data to create registries around social drivers of health, such as language barriers, transportation challenges, or affordability issues. Understanding and addressing these factors in patient messaging can break through barriers to care and foster patient engagement.
• High-risk maternity registries: For high-risk maternity patients, data can be used to identify opportunities for early engagement and consistent support. Leveraging EHR data and monitoring patients' health status can help healthcare providers intervene promptly and improve maternal and infant outcomes.

Final thoughts on data-driven value-based care

Data-driven approaches hold immense potential for improving patient engagement and care outcomes. By using data to stratify populations, plan interventions, and tailor patient messaging, healthcare providers can bridge the gaps in care delivery.

However, it is essential to remember that data is a tool to enhance, not replace, human connection and empathy in healthcare. By combining data insights with genuine understanding and support, healthcare organizations can create meaningful patient engagement that leads to better health outcomes for all.