Understanding healthcare costs at the population level is essential for researchers, policymakers, and health system managers. One of the key tools for this purpose is the episode grouper—a sophisticated software system that categorizes healthcare services into meaningful units called episodes. This guide provides an in-depth overview of episode groupers, their role in cost-of-illness studies, and how to select the appropriate tool for your research. By leveraging these systems, analysts can more accurately measure resource use and costs associated with specific health conditions, ultimately informing better health policy and clinical decision-making.
Introduction
In health services research, quantifying the economic burden of diseases is fundamental. While traditional methods such as encounter-based or person-based cost analyses have been widely used, the rise of episode-based analysis offers a promising alternative. This approach groups related healthcare services into episodes, capturing the full scope of care associated with a particular clinical event or condition. The advent of episode groupers—automated algorithms that systematically define these episodes—has facilitated this methodology, providing researchers with standardized and replicable tools for comprehensive cost assessments.
Healthcare episode groupers are complex software tools designed to systematically bundle healthcare services received by patients into clinically coherent episodes. These tools are particularly valuable when analyzing large administrative claims datasets, such as those from US fee-for-service programs, because they help delineate the boundaries of care over a specified period. For instance, an episode might encompass all services related to a childbirth event, from prenatal visits to postpartum care, or all treatments for a chronic condition like diabetes within a certain timeframe.
While some health system stakeholders have long relied on privately-developed groupers for provider comparisons and reimbursement models, their use in public health research remains limited. This is partly due to challenges related to transparency, accessibility, and methodological heterogeneity among different tools. Nonetheless, understanding the properties and differences among various episode groupers is crucial for researchers seeking to conduct robust cost-of-illness analyses and for policymakers aiming to evaluate or implement bundled payment models.
Context for Episode-Based Cost-of-Illness Analysis
When addressing the question, “What is the economic impact of condition X?” researchers must choose an appropriate methodological approach. In the context of US healthcare claims data, three primary methods are typically employed:
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Encounter-based analysis: Summing all service payments associated with a specific diagnosis or procedure code during a particular period. For example, calculating diabetes-related inpatient costs by aggregating all claims with a diabetes diagnosis.
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Person-based analysis: Using statistical models to allocate costs to individuals based on their characteristics, such as regression techniques that estimate average spending for patients with a specific condition.
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Episode-based analysis: Algorithmically grouping related services into discrete episodes, capturing the entire care cycle for a clinical event or condition. This method combines the clinical homogeneity of encounter-based approaches with the comprehensive scope of person-based models.
While encounter- and person-based analyses are more common, episode-based analysis provides a nuanced perspective by considering the continuity and complexity of care over time. For instance, an episode might include all services from the onset of a disease episode until recovery or stabilization, such as all treatments related to a cardiac arrhythmia over a defined period. Implementing this approach requires a reliable method to identify and define episodes, which is where episode groupers come into play.
What Are Episode Groupers?
An episode grouper is a specialized software system comprising algorithms that determine which healthcare services belong to each episode. These algorithms analyze claims data—often including diagnosis codes, procedure codes, service dates, and provider information—and assign individual claims to specific episodes based on predefined criteria.
Different groupers use various rules and parameters to delineate episodes. For example, some may consider the type of diagnosis, the timing of services, or clinical guidelines to define episode boundaries. The key advantage of using a grouper lies in its ability to standardize episode definitions across large datasets, enabling consistent and comparable analyses.
Although researchers can define episodes manually or adapt existing tools like Clinical Classifications Software (CCS), using established groupers offers several benefits:
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They incorporate clinical expertise and coding norms, ensuring clinically meaningful episodes.
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They include criteria for assigning services like prescription drugs, which often lack diagnosis codes.
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They promote reproducibility and comparability across studies using the same tools.
However, the heterogeneity in methods among different groupers can pose challenges. Variations in inclusion criteria, episode scope, and risk adjustment approaches can lead to differences in estimated costs and resource use for similar conditions. Therefore, selecting an appropriate episode grouper necessitates careful evaluation of these characteristics.
Selecting an Episode Grouper
Choosing the right episode grouper depends on multiple factors, including the specific condition under study, the available data, and research objectives. Several commercially available and publicly documented groupers exist, each with distinct features:
| Product | Episode example | Sample focus | Number of episodes | Clinical setting | Publicly available definitions | Risk adjustment approach |
|—|—|—|—|—|—|—|
| 3M PFE | Not reported | Event-based and cohort-based episodes | >500 | All | No | 3M Clinical Risk Groups |
| Cave Grouper | Urinary tract infection | Efficiency and high-cost patient prediction | >500 | All | No | CCGroup MediScreen |
| CMS-BPCI | Urinary tract infection | Inpatient and post-acute care | ~50 | Inpatient, SNF, rehab, LTAC, home health | Yes | No |
| McKinsey & Co | Perinatal | Principal accountable provider | >100 | All | Yes | Yes |
| Optum Symmetry ETG | Pregnancy with delivery | Cost and provider profiling | >500 | All | Yes | Optum Symmetry Risk Groups |
| Optum PEG | Radical hysterectomy | Procedure-related costs | ~200 | All | No | Optum Symmetry Risk Groups |
| Prometheus | Pregnancy | Avoidable complications | ~100 | All | Yes | Prometheus risk adjustment |
| Medical Grouper | Cardiac arrhythmias | Population and provider profiling | >500 | All | No | Disease staging |
Source: Public documentation as of January 2019, with updates available from vendor sources.
When selecting an episode grouper, consider whether it includes a comprehensive list of episodes relevant to your condition, as well as the transparency of its definitions. For example, CMS-BPCI offers detailed episode lists for inpatient and post-acute care, whereas some private groupers may not publicly disclose all definitions. The conceptual focus also varies: some groupers target surgical procedures, others focus on chronic disease management or avoidable complications.
It is also important to evaluate whether the episode definitions are narrowly or broadly scoped. Narrow definitions tend to create more homogeneous episodes, which facilitate risk adjustment but may exclude some relevant cases. Broader definitions increase heterogeneity but might better capture the full spectrum of care, especially for chronic conditions with ongoing management.
Another key factor is whether the definitions and algorithms are publicly accessible. Public documentation, including lists of codes and criteria, enhances transparency and reproducibility. Many groupers also provide risk adjustment methods, which are essential for fair cost comparisons across different patient populations.
The choice of a grouper also involves logistical considerations, such as the availability of machine-readable definitions (e.g., SAS, R scripts) and associated costs, as many products are proprietary.
A detailed comparison of these tools would involve examining their coding input requirements, episode boundaries, severity adjustment methods, and other technical features. Careful evaluation ensures that the selected grouper aligns with the specific aims of your cost-of-illness study.
Conclusion
Episode groupers are powerful tools that enable detailed and standardized analysis of healthcare costs by defining clinical episodes within administrative claims data. They facilitate a more comprehensive understanding of resource utilization and costs associated with specific health conditions, which is crucial for research, policy, and reimbursement reforms. This guide has outlined the fundamental concepts, practical considerations, and key differences among popular episode groupers, equipping health services researchers and managers with the knowledge needed to incorporate these tools into their analyses.
While the landscape of episode groupers continues to evolve—with many products remaining proprietary and lacking full transparency—ongoing efforts to improve accessibility and standardization will enhance their utility. Researchers should critically assess the definitions, scope, and risk adjustment approaches of available groupers to ensure their suitability for specific studies. Ultimately, leveraging episode-based analysis can lead to more accurate, comparable, and actionable insights into healthcare costs and quality.
Additional Resources
For more details on the roles and qualifications of those analyzing healthcare data, explore what is a healthcare data analyst. To understand broader healthcare system differences, see how does the US healthcare system compare to other countries. For insights into optimal healthcare models, review what is the best healthcare system in the world. Finally, understanding the structure of integrated systems can be aided by reading what is an integrated healthcare system.