How Data Will Transform Value-Based Care

Particle Health explains why data is essential for successful value-based care. Leveraging data effectively can improve the quality of care and patient outcomes while reducing costs. When it’s all about value-based care, data is the secret sauce that makes it work.

As the healthcare industry continues to move towards value-based care (VBC), it's becoming increasingly evident that success in the VBC landscape depends on having a robust data strategy that enables healthcare providers to access, analyze, and act on data in real-time. In this article, we'll explore why a scalable data infrastructure is critical, along with the different ways that data can be leveraged.

What is value-based care (VBC)? 

Value-based care is a healthcare delivery model that represents a shift from reactive to proactive care. Under this model, providers are paid based on the outcomes of their patients and the quality of the care provided. By incentivizing organizations to keep patients healthy and out of acute care, VBC enables lower costs and better results. However, achieving this goal can be challenging, and that's where data comes into play.

You need the whole patient story

Value-based care without access to comprehensive data is like trying to navigate a maze blindfolded. Providers need complete and accurate patient data in order to understand and mitigate the actual amount of risk that is taken on with each patient. Fragmented data leads to unnecessary overspending in several ways:

  • Duplicate procedures: When patient information is scattered across different systems or providers, it becomes challenging to track previous procedures or tests. This can result in unnecessary repetition of tests or procedures, leading to increased costs without contributing to improved patient outcomes.
  • Inefficient care coordination: Fragmented data hinders effective communication and care coordination among different healthcare providers involved in a patient's treatment. This lack of coordination can lead to redundant or conflicting treatments, driving up costs while potentially compromising patient safety.
  • Missed preventative care opportunities: Incomplete patient data makes it difficult to identify and address health risks or early signs of illness. As a result, opportunities for timely preventative care are missed, leading to the development of more serious conditions that require more intensive, and costly, interventions.
  • Medication errors: Inadequate access to a patient's complete medication history can lead to prescription errors, such as prescribing medications that interact negatively or duplicate existing treatments. These errors can result in adverse reactions, hospitalizations, and additional expenses.
  • Uncoordinated chronic disease management: For patients with chronic conditions, fragmented data can prevent healthcare providers from getting a holistic view of the patient's health status. This leads to suboptimal management of chronic conditions, potentially causing complications and avoidable hospitalizations.
  • Delayed care and diagnoses: When patient data is scattered across different systems, it takes longer for healthcare providers to access the information they need. Delays in obtaining critical information can lead to delayed diagnoses, prolonged treatments, and increased costs.
  • Limited data-driven insights: Fragmented data prevents the generation of meaningful insights and analytics that can guide evidence-based decision-making. Without comprehensive data, healthcare organizations may struggle to identify trends, patterns, and opportunities for cost-saving initiatives.

In value-based care models, where the goal is to improve patient outcomes while controlling costs, fragmented data undermines these objectives. The 21st Century Cures Act made data exchange table stakes for all healthcare organizations, but data exchange on its own between an individual practice and a hospital isn't enough. Value-based care organizations need integrated information from all possible sources in order to be successful. True interoperability creates a holistic view of a patient's health, enabling healthcare providers to quickly access and share data seamlessly across different systems.

When consolidating medical records from multiple sources, ensuring data quality is critical. The value-based care model prioritizes quality outcomes, not the number of tests performed or how many patients are seen in an hour. The only way to report on those outcomes—and get paid for the services provided—is through quality data. When you understand the patterns in your data and patient population, you can jump on trends before they turn into costly problems.

The right care at the right time 

Once you’ve built a foundation of high quality, interoperable data, you can leverage this information to deliver better care, reduce spending, and measure quality-based care metrics. For example, providers can quickly identify patients who are at high risk of readmission and provide them with targeted interventions to prevent future hospitalizations. Data can also be used to monitor broader population-based health trends, improve adherence of referrals, identify patients at a high risk of developing certain diseases, and highlight areas where costs can be reduced without compromising the quality of care. For example, patients who are using unnecessary services, like someone seeking frequent emergency room visits for non-emergency issues, can be redirected to more appropriate care that is cost-effective.

As technology continues to advance and healthcare data becomes more widely available, the role of data in value-based care will continue to evolve. However one thing is clear: a unified data ecosystem is necessary. Looking ahead, here are some of the future roles that data may play in value-based care:

  • Predictive analytics: As healthcare providers collect and analyze more patient data, they will be able to use predictive analytics to anticipate patient needs, generate customized and predictive risk scores, and identify high-risk patients who require more intensive interventions. This approach can enable healthcare providers to intervene earlier, reducing the likelihood of adverse health outcomes and improving patient outcomes.
  • Artificial intelligence: Artificial intelligence (AI) can be used to analyze large amounts of data, identifying patterns and trends that may be difficult for human analysts to identify. AI can also be used to automate routine tasks, freeing up healthcare providers to focus on more complex and high-value activities.
  • Patient-generated data: As patients become more involved in their care, they will generate more data through wearable devices and other digital health tools. This patient-generated data can provide valuable insights into patients' health status, preferences, and needs, enabling healthcare providers to deliver more personalized care.
  • Social determinants of health (SDOH): Data can be used to identify social determinants of health, such as income, education, and housing, which can have a significant impact on patients' health outcomes. By addressing these social determinants of health, healthcare providers can improve patient outcomes and reduce healthcare costs.

Scaling up with a data partner

As the healthcare industry continues to shift towards value-based care, it’s crucial that organizations choose a strong clinical data partner that can help unlock ongoing value from data over time. Collaborating with a partner who can focus on insights allows providers to focus on the patients, not the data. Particle’s data platform connects your organization to the whole patient story - we're talking deduplicated, well-formatted, usable data through a single API integration. Reach out to our team to learn more about how Particle Health can accelerate your success in the value-based care space.