Change in healthcare policy and practice happens quickly and often. Keeping a baseline awareness of key initiatives is fundamental to organizational success. Bundled payments, Medicare payment policy, and healthcare quality are three areas DataGen focuses on, updating our knowledge and insights as frequently as policy changes happen.
Alternative Payment Models (APMs) are a type of payment reform that incorporate quality and total cost of care into reimbursement, rather than a traditional fee-for-service structure. Some APMs include Accountable Care Organizations, Bundled Payments, and Patient-Centered Medical Homes. APMs offer new ways to pay providers for the care they give Medicare beneficiaries.
APMs allow flexibility in the types of services delivered and the types of providers who can deliver those services. Success is measured based on outcomes, not on adherence to one-size-fits-all standards for structure or processes. In addition, performance benchmarks must reflect differences in the costs and outcomes.
Bundled payments link payments for multiple services beneficiaries receive during an episode of care, including financial and performance accountability. These models drive higher quality and more coordinated care at a lower cost to Medicare. Bundled payments can be an organization’s first step into APMs; they provide clear focus, engage specialists, and do not upend a hospital’s fee-for-service (FFS) business model.
Bundled payments are an alternative to traditional fee-for-service (FFS) payments for patient healthcare. Reimbursements based on the utilization of each specific service associated with an episode can have a large amount of variance for payers and providers. By bundling these payments, providers receive a single amount for an entire episode, regardless of the cost. These models account for frequency and variance of the episode cost, and aim to reduce overall healthcare costs—while still covering costs incurred by providers.
Healthcare analytics refers to understanding and gaining insight from massive datasets. Modern electronic health datasets are so large and complex that they can be difficult (or impossible) to manage with traditional software and/or hardware; nor can they be easily managed with traditional or common data management tools and methods. Big data analytics applications in healthcare take advantage of the explosion in data to extract insight for making better-informed decisions.
Measuring the quality of healthcare gives us insight into how the health system is performing as a whole and on the individual organizational level. This drives the strategies to improve care across the continuum and prevents overuse, underuse, and misuse of healthcare services to ensure patient safety and satisfaction. Quality measures help to hold payers and provider organizations accountable for providing high-quality care. Finally, they measure and address disparities in how care is delivered.
Quality is measured by leveraging data to evaluate the performance of provider organizations against standardized quality metrics. Quality measures typically fall into four broad categories:
- Structure: Evaluates the characteristics of a care setting. For example, does an intensive care unit always have a critical care specialist on staff?
- Process: Assesses if the services provided to patients are consistent with routine clinical care. For example, are primary care physicians ensuring their patients receive recommended cancer screenings?
- Outcomes: Determines the efficacy of care delivered based on patient health over time. For example, how many patients had to be readmitted following surgery?
- Patient Experience: Analyzes feedback from patients based on their experience of care. For example, are providers explaining treatment options to patients in ways they can comprehend?
Many differing metrics can be used by payers to measure the quality of care delivered, the rates of readmissions, and disease management programs.
Quality metrics are reporting systems that measure and quantify healthcare processes, outcomes, patient perceptions, and organizational structure and/or systems. Understanding and meeting quality benchmarks is critical to reach the goals of the Triple Aim—improving the patient experience of care, improving the health of populations, and reducing the per capita cost of healthcare. These metrics also serve as the starting point for program management initiatives and quality improvement efforts.
In short, Medicare policy affects every aspect of provider operations and care delivery. Medicare policy is currently designed, at a high level, to motivate and incentivize providers to meet the goals of the Triple Aim—improve the quality of care, enhance the patient experience, and reduce costs. They are doing this by changing the way providers are paid for delivering care, monitoring patient health over time, and organizing their operations. Typically, private payers follow Medicare policy over time, so provider organizations that follow, understand, and adapt to Medicare policy changes can stay ahead of the curve when it comes to their overall success.