EDITOR’S NOTE: This is the first in a two-part series that examines episodes of care as healthcare payment model. Part II will be published on July 26, 2016.
The financing of healthcare is rapidly changing in this country. The Centers for Medicare and Medicaid Services (CMS) has introduced a wide array of value-based alternative payment models including bundled payments and shared savings through Accountable Care Organizations and Pay for Performance programs. The passage of the Medicare Access and CHIP Reauthorization Act (MACRA) includes the dual pathways of the Merit-based Incentive Payment System (MIPS) and a variety of alternative payment models.
This accelerated direction suggests a future comprised of payment models that are progressively tied to patients’ health conditions and the outcomes and cost for the care of those conditions, rather than the historical fee-for-service model.
The planned migration away from traditional fee-for-service payment typically relies upon aggregating services into various groupings. This change is comparable to the shift in payment for hospital admissions, exchanging the old “per diem” to percent of charges model in favor of payment for admissions tied to a patient’s diagnoses.
It is generally believed that the implementation of DRGs (Diagnosis Related Groups) in the early 1980s substantially reduced the Medicare program’s expenditures for inpatient hospital stays by bundling services into a single hospital encounter based on the nature of a patient’s condition and, in some cases, the procedures performed. Policy analysts for government programs and commercial health insurers have expressed a longstanding desire to create a similar payment model for the delivery of care across defined episodes across all healthcare settings. A variety of methodologies for achieving that end have been created and used for analytic purposes for many years, but challenges in implementing these have limited the use of these methods as part of a broader scheme for payment.
At the center of many of these approaches is the “episode of care” as the basis for payment, risk adjustment, and the measurement of quality and efficiency. This paper will attempt to describe what an episode of care is, how it is defined, and the challenges with implementation that must be considered prior to any attempt at a wide implementation nationally.
Defining the Episode
Stedman’s Dictionary defines an “episode of care” as: “All services provided to a patient with a medical problem within a specific period of time across a continuum of care in an integrated system.” A number of models currently in use do not satisfy those criteria. For example, a Diagnosis Related Group (DRG) is not truly an episode of care as this definition suggests, since it does not include all services and is specifically limited to an inpatient hospital encounter for that problem. Nor do most bundled payment models meet this definition since those substantially limit the bundling to specific parameters driven by an aggregation of providers or specific services, rather than by the patient’s “medical problem.” These parameters could include factors such as acuity, type, stage, classification, complications, and a host of other disease parameters that make a dramatic difference in the risk and severity of the patient’s condition.
Episodes of care currently in use are defined by algorithms that set parameters for the definition of each episode. These definitions will vary depending on the specific methodology that groups the elements of time spent, services provided, diagnoses identified, and providers participating in care delivery into an aggregated experience of a patient-centric health condition.
The Episode Grouper for Medicare (EGM) as proposed in MACRA is the method for grouping claims related to the experience of a patient’s health condition. While there are a number of different types of groupers, the moving parts for the logic that drive these groupers are similar. Based on the published methodology for EGM there are significant challenges in defining these episodes so that the proper data is accurately and completely attributed to the specific episode.
Defining the Condition or Service
The first step in defining an episode is to identify the condition or service that is the basis of this episode. That definition is relatively straightforward for conditions like “Abdominal aortic aneurysm;” it is less straightforward for conditions like “Asthma-COPD Acute” or “Asthma-COPD Chronic” as explained below.
Challenge: Currently the conditions and procedures that are the primary basis for identifying an episode are not adequately defined. It is difficult to determine what is included or excluded from the definition of the episode. For example, which of the many pulmonary conditions should be included in “Asthma-COPD Acute”? Which pulmonary conditions are excluded? What is the specific definition of the category that might suggest why certain conditions should be included or excluded? What makes the episode of “Asthma-COPD” acute or chronic? Looking at the current “trigger” and “episode relevant” diagnosis codes for these two episodes, they are exactly the same. The proper definition of episodes will require an answer to these questions.
Trigger Events that Initiate Episode Grouping Process
CMS has defined sets of diagnosis and service codes that will trigger the creation of an episode of care within the EGM. The episode logic uses these sets of codes where certain criteria have been met. For example, one criterion might be that the trigger is the primary code on an inpatient claim, while another criterion might be that the trigger is associated with any claim code where there is an evaluation and management service. Based on the defined logic, certain diagnosis codes will trigger the creation of the episode while others will not.
Challenge: From a clinical perspective, the criteria for triggering an episode appear to be inconsistent in that they would include a number of conditions that may not clinically meet that episode definition (assuming there was a definition). Many conditions would fall outside of that episode logic, even though clinically they would seem to belong to that episode. For example, for the episode “Acute ischemic stroke” one of the triggers would be the codes for “vascular myelopathies” which would not clinically be considered an acute ischemic stroke but which would, according to the defined logic, trigger that episode. A further complication is that all of the codes used in the episode logic are currently defined using ICD-9 codes. As the health care community moved to ICD-10 as of October 1, 2015, all of this code-based logic will need to be redefined, replacing ICD-9 with ICD-10. For those of us who have spent several years redefining these condition-based code sets for a variety of edits, coverage definitions, quality measures, and other processes that use aggregated codes, we know this is not a simple “crosswalk” effort. Rather, new concepts have been introduced or redefined in ICD-10 that may change the rationale for episode assignment. Also, many clinical scenarios exist where the same patient may have multiple episodes created, based on the same data. Each one of these episodes may have the same data attributed to them based on the current logic. Where does the data belong? If attributed to multiple episodes, will the expenditures and other data parameters be counted multiple times for each patient/provider associated with those episodes?
EDITOR’S NOTE: In Part II of this article, Dr. Nichols will look at diagnoses and services that are considered episode relevant; attribution; incentives; and what you as a provider should do.
About the Author
Dr. Nichols is a board-certified orthopedic surgeon with a long history in health information technology. He has a wide range of experiences in healthcare information technology on the provider, payer, government, and vendor side of the healthcare business. He has served in positions in executive management, system design, logical database architecture, product management, consulting, and healthcare value measurement for the last 15 of his 35 years in the healthcare industry. He has given over 100 presentations nationally related to ICD-10 over the past three years on behalf of payers, providers, integrated delivery systems, consulting groups, CMS, universities, government entities, vendors, and trade associations. He co-chairs the WEDI (Workgroup on Electronic Data Interchange) translation and coding sub-workgroup and has received WEDI merit awards three years in succession. He is also an AHIMA-approved ICD-10 coding trainer. He is currently providing consulting services as the president of Health Data Consulting Inc.
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