Leveraging Data for Risk-Adjusting Bundled Payments

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Data requirements and analytics advice for healthcare executives and IT teams

Until very recently, bundled payments were a strictly voluntary alternative payment model (APM). But as of April 1, the Centers for Medicare and Medicaid Services (CMS) declared that participation in bundled payments for hip and knee replacement procedures is mandatory for approximately 800 hospitals under Medicare’s Comprehensive Care for Joint Replacement (CJR) reimbursement model.1

This represents the first time that CMS has declared any form of bundling mandatory. It’s all part of the industry’s transformation from fee-for-service to a risk-based payment model. And it shows Medicare’s steadfast commitment to coordinating all aspects of the patient experience—from admission to 90 days after discharge—and holding providers responsible for both care and cost.

Unlike some others, this resolution has teeth. Hospitals that do not successfully adopt the CJR model are at risk for serious financial penalties that increase over time. Moreover, providers who can utilize APMs stand to be rewarded, with the Medicare Access and CHIP Reauthorization Act (MACRA) providing a 5% annual bonus payment to physicians participating in APMs.2

A handful of hospitals made the decision to go down the bundled-payment road ahead of the curve. Lessons learned from their experience follow, along with data requirements and analytics advice for healthcare executives and IT teams.

Adopting the Bundled-Payment Model

As an industry, we will doubtless spend millions of dollars trying to figure out how to make bundled payments work for cost reduction and care quality. And, like anything tech related, there will be first-movers and laggards. Being late to the bundled-payments game, however, could have dire consequences for hospitals and health systems.

Some community hospitals in rural areas are joining the bundled-payment game with little or no data analysis. Larger hospitals have been more likely to use a measured, data-driven analytical approach. In general, organizations that can demonstrate mastery of analytics, effectively tracking all pertinent data from admission through 90 days past discharge, will be best positioned to reduce cost and maximize efficiency under bundled-payment models, thus becoming more attractive to payers.

For the CJR specifically, it is of paramount importance that hospitals and health systems ensure clean, quality data across three months of joint-replacement surgical care. All costs for the surgery and associated care are reimbursed to the hospital via a single payment. Then the organization is responsible for properly and effectively distributing it. Hospitals that hold costs below the target price keep the difference. Those that don’t must repay Medicare.

Therefore, to successfully participate in APMs, especially the CJR, data is king.

Key Data Considerations

Arguably, bundled payments may have less to do with the concept of great quality care, and more to do with being able to efficiently manage care within a fixed fee, allowing CMS to better control its costs by transferring risk to the coordinating provider. As these bundled payments move from voluntary to mandatory, the ripple effect will be profound. Understanding the financial data within an entire CJR treatment plan is essential. Data must be relevant, clean and correct across all bundle participants.

Governance is critical to maintaining the integrity of data. CJR participation involves extending information beyond a minimum data set (MDS) while also focusing on documentation frequency, consistency and quality. Financial, clinical and operating systems must begin to be more logically configured and aligned so that organizations can function cohesively from a data analysis and business intelligence perspective.

Not only must all relevant data across bundle participants be clean and correct, it must also be tracked throughout multiple care settings and data streams: pre-admission, surgical episode and post-discharge. For example, 5 to10 years ago, hospitals were divesting themselves of skilled nursing and home-health agencies. Now, there’s pressure to bring post-acute care services back into the fold in order to retain control of patients (and their data) after discharge.

Pre-admission

Successfully adopting bundled payments starts with correct patient selection and the need to understand populations. Hospitals may want to select patients most likely to be successful within a bundled-care regimen and align them to specific programs with proven high-performing providers.

Analyzing pre-admission information provides valuable insight. Knowing what therapeutic and preventative activities were attempted prior to joint replacement surgery and percentage of patient compliance are keys to optimal patient selection for participation in CJR. Socioeconomic risk factors also play a part in CJR patient selection. With this in mind, it is important to proactively address the challenge of capturing and incorporating socioeconomic information  during the pre-selection process, prior to delivery of care.

Surgical Episode

During the encounter, data is also needed to establish a direct relationship between the cost of specific devices, services and drugs to the overall patient experience. Under an orthopedic bundle, consideration must be given to many areas—such as prep, setting, physical therapy, product selection, drug regimen and coordination of care after discharge. Most organizations are working hard to first understand the comparative clinical efficacy of the products they use to treat CJR patients. The next evolution of analytics focuses on understanding the value of a product or treatment modality, where value is defined as the ratio of efficacy to cost.

Post-discharge

Bundled payments also require data from other providers outside the hospital as mentioned above. CIOs can address this need to pull data from other sources through access to the health information exchange (HIE) or other affiliation with ambulatory services. Narrow networks are another option garnering traction. Narrow networks tightly align hospitals with regional physicians and other care providers to create a private HIE.

These new information demands open new doors for information executives and data analytics specialists.

CIOs Manage Risk, Measure Value

CIO roles will evolve as more bundles are introduced and required. To that end, CIOs should spend more time understanding how to leverage data and analytics for risk-adjusted bundles.

The first step is to understand what is driving risk in the hospital, and then pick solutions to help manage the risk. This could be as simple as looking at technology to reduce hospital infection rates, improve utilization (workflow applications), and enhance staff efficiency (productivity tools). The same holds true for managing risk within CJR bundles.

To understand bundled-payment risk, data must be pulled from clinical, financial and claims systems. The challenge is managing the number of data stores and unstructured formats. Executives should leverage detailed and accurate cost data from the following applications for participation in the CJR:

  • General ledger (GL)
  • Physician fee schedule (PFS)
  • ICD-9/10 codes and diagnosis-related groups (DRGs)
  • Practice management system (PMS) data
  • Current procedure terminology (CPT) codes
  • Time and attendance (T&A)
  • Clinical data from health information systems (HIS), electronic medical records (EMRs) and other systems that store clinical data

Second, analytics are used to measure value. This requires detailed transactional data pertaining to clinical outcomes, revenue outcomes and patient satisfaction. CIOs will need to integrate these data streams at a patient level, over time, and be able to tease out the statistical relationships through analytics applications.

The most promising use of analytics to measure value within CJR includes the ability to compare detailed payer contract data with the actual cost and quality of care.[BF2]

The New Analytics

Traditional revenue cycle analytics focus on topics such as overall A/R performance or the stratification and characteristics of certain groupings of outstanding receivables (billed or unbilled), allowing hospitals to make adjustments to collect cash more efficiently. As hospitals shift toward value-based care models, more complex analytics become more important—even vital for survival.

Due to the multifaceted nature of CJR and other APMs, several departments should have input into new analytics initiatives—including the director/VP of revenue cycle, the director/VP of payer contracting, head of supply chain, pharmacy director, CFO, decision support, and the chief medical information officer.

One example might be the ability to assess the cost and quality performance associated with specific treatment modalities for CJR participants within and across participating payer groupings. This information is necessary for hospitals to advantageously track—or better yet, proactively pursue—a value-based incentive.

Valuable Byproducts Ahead

Successfully adopting bundled-payment models will yield byproducts, which include advancement of care integration and analytics expertise. And these byproducts will lead to a more positive patient experience and reduced costs for CJR participants. However, all parties involved will need to have mastery of analytics. Ultimately, strong financial and clinical analytics programs are the only way to manage the migration from fee-for-service to value-based care.

Data Requirements for CJR

  • All financial data and detailed costs, within an entire CJR treatment plan—  including post-discharge services from other providers outside of the hospital
  • Clinical data including pre-surgical treatments, surgical prep, setting, physical therapy, product selection, drug regimen and coordination of care after discharge
  • Socioeconomic risk factors
  • Detailed payer contract data
  • Clinical outcomes data and quality performance results

References

  1. Medicare Program; Comprehensive Care for Joint Replacement Payment Model for Acute Care Hospitals Furnishing Lower Extremity Joint Replacement Services.
  2. Understanding Medicare Payment Reform (MACRA).
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About Author

Timothy Lantz
Timothy Lantz

Timothy Lantz is an experienced executive with a background in both finance and operations. He possesses over 15 years of leadership and consulting experience across multiple industries, and currently serves as senior vice president at Sentry Data Systems, overseeing the company’s advanced healthcare analytics software and services. Prior to joining Sentry in 2014, Lantz designed and implemented financial and operations solutions for large hospitals and healthcare systems and led projects focused on supplier diversity, margin performance and contracting. Tim currently serves on the IPMI Healthcare Finance Institute Advisory Committee, provides educational presentations on a variety of data analytics topics, and is a published contributor and national speaker. A National Merit Scholar, Tim holds a degree in finance and real estate from the University of Wisconsin.

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