Three Analytics Strategies to Drive Patient-Centered Care

This report is based on a 2020 Healthcare Analytics Summit (HAS 20 Virtual) presentation by Tyler Gauthier, MHA, CPHQ, CSM, Director of Value-Based Care, OneCare Vermont, and Katelyn Muir, CPQH, CSM, MPH, Supervisor of Population Health Analytics, OneCare Vermont, titled, “Prioritizing the Patient in Value-Based Care: A Data-Informed Approach.”

In an attempt to curb healthcare’s rising costs, CMS is increasing the pressure on health systems to deliver and measure value with value-based care (VBC). One way CMS is promoting this value-driven healthcare model is by offering health systems and providers financial incentives to work more closely together and better coordinate care through formal networks, such as ACOs. Even without CMS’s financial incentives, organizations already feel the pressure to eliminate uncoordinated care, costing health systems between $27.2 billion and $78.2 billion a year.

As more health systems shift from fee-for-service to value-based payments, they struggle to balance the financial focus of VBC with the patient-focused needs of clinical care. Managing rising costs and meeting criteria to qualify for reimbursements and bonuses can easily distract from prioritizing patients in a value-based landscape.

However, in spite of competing priorities, health systems can rely on data to manage the financial demands of value-based payment models and ensure patient-centric care. Access to accurate, comprehensive patient data allows care teams to understand complex patient populations and tailor care delivery to unique patient needs while also meeting the criteria for reimbursement in value-based contracts.

Prioritize Patients with Comprehensive Data and a Patient-Centered Care Model

With a variety of data sources (e.g., claims, clinical, social determinates of health) across multiple locations, health systems must first aggregate all of their data into one place. Without comprehensive data in one place, leaders deliver care based on disjointed, incomplete data. Having aggregated data in one place also means every provider can make decisions based on the same data (e.g., a shared MRI)—even when patients receive care at different facilities.

With data driving a patient-centered care delivery model, and providers and resources supporting patients, patient needs guide care priorities and resource allotment (versus team members’ opinions or budget numbers driving care decisions). Health systems should use the patient-centric support model as a systemwide reminder that every initiative, strategy, and goal must focus on the patient.

A patient-centered care model also allows health systems to identify patient-centered metrics to help them reach targets and evaluate current and future interventions’ effectiveness. Without patient-focused metrics that align with the care model, health systems fail to accurately measure value. Relying on data-driven insights aligned with a patient-centered care model helps organizations meet the stringent standards of VBC reimbursements (e.g., target rates for low hospital readmission). A data-driven approach also helps team members stay in sync with strategic goals because they understand the value of data, the role it plays in delivering patient-centered care, and how the organization uses data to define success.

Three Analytics Strategies to Keep the Spotlight on Patient-Centered Care

As health systems continually learn the best way to prioritize patient-centered care while simultaneously managing the financial demands of VBC, they can rely on three analytics strategies to keep them on track:

#1: Prioritize Patient Outreach by Risk Level

Leadership teams set targets and goals based on risk stratification that reveals the highest-risk patients (e.g., comorbidities by zip code), who are candidates for outreach. With data-driven insight into at-risk populations, care teams can focus on effective care coordination and early care management interventions targeted to these populations’ needs.

Using data to understand the health of patients is imperative to managing large populations. Accurate, comprehensive patient data allows care teams to track and identify risk trends over time. Reviewing and identifying data-informed trends enables health systems to proactively address health concerns and provide preventive solutions in at-risk areas. For example, if a specific group of people based in one area code shows signs of prediabetes, a health system could implement preventive measures and additional health monitoring in that community.

#2: Deploy Data Tools to Combat COVID-19

Although COVID-19 has brought unprecedented challenges, health systems that prioritized data- and patient-centric strategies before the pandemic have been more prepared to respond to the outbreak. With team members already analytics versed, these health systems focused on deploying data-driven tools to identify patients with the highest risk of contracting COVID-19, rather than first educating team members about the need for data in identifying at-risk patients and then developing the tracking tools.

Certain health systems responded to the virus better than others by developing data-informed tools and processes soon after the onset of COVID-19. The fast response prevented the spread of COVID-19 and proactively protected communities by monitoring exposure through pandemic support tools, such as contact tracing.

Because COVID-19 is novel, there is very little data or information about the virus, making data sharing across organizations more critical than ever. Health systems that didn’t have a data-centered culture and a data-friendly infrastructure were ill-equipped to share data and gain more insights about the virus as it evolved. This deficit delayed patient care, leading to worse outcomes. Inversely, health systems with an existing data infrastructure had already prepared for effective data interoperability, continually learning more about the virus and, in turn, adapting patient care and emergency planning according to those changes. With constant, updated data coming in, health systems can continually pivot their strategy to fight COVID-19 and target metrics to stay patient-centric amid constant change.

Leveraging data throughout the pandemic has allowed health systems to understand capacity, utilization, and the resources they should allot for different healthcare delivery methods, such as telemedicine. Using data to understand how to continue care delivery prioritizes patients over processes in any circumstance.

#3: Promote Data Literacy

Health systems can improve how they use data to make patient-centric decisions and improve outcomes by cultivating data literacy. With a patient-centered model and patient-centered metrics, team members need the literacy tools to understand and apply data throughout the care delivery process. Data-literate team members who can access actionable data understand how that data drives patient-centered care during the COVID-19 crisis and future challenges.

Data teams can start by developing various in-person and virtual data literacy training opportunities (especially with team members working remotely due to COVID-19). A data literacy program can begin with a data readiness checklist that defines a literacy baseline. Organizations can use that baseline to measure progress. Health systems can also improve data literacy by creating data literacy champions to promote data-informed processes and decision making throughout the health system. For example, health systems can train physicians, nurses, and administrators as data champions within their teams, units, and departments. Leveraging existing leaders as data champions extends the data beyond analysts and leaders, increasing the likelihood that team members will model the behavior of their managers and also turn to data before making decisions about patient care.

Data Powers Patient-Centered Care in Value-Based Care

When health systems leverage analytics insight to understand a patient’s complete health, they can use that insight to guide quality care that qualifies for the demanding CMS reimbursement standards of VBC (e.g., cost, quality, and provider performance). Without actionable insight, care teams can’t effectively identify at-risk patients, or ensure they are delivering high-quality, cost-effective care. Analytics is essential to VBC because it prevents care teams from wasting resources, spending too much time on the wrong interventions, or failing to prioritize the patient.

Detailed information from comprehensive data sets allows patient needs to drive care interventions, essential to patient-centered care in VBC. Whether health systems are facing populations with chronic diseases like diabetes or responding to COVID-19, a culture that values data in the care delivery process will have the tools to deliver sustainable VBC.

Additional Reading

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

1. The Medicare Shared Savings Program: Four Tools for Better Profit Margins and High-Quality Care
2. From Volume to Value: 10 Essential Strategies for Navigating the Healthcare Shift
3. Activity-Based Costing in Healthcare During COVID-19: Meeting Four Critical Needs
4. Population Health Management: A Path to Value
5. Value-Based Purchasing 2020: A 10-Year Progress Report

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