Episode groupers: a crucial tool for population health management

A practical guide to episode groupers for cost-of-illness analysis in health services research

Summary of analytic components in selected episode groupers.

Product	Episode example	Sample conceptual focusa	Number of episodes	Clinical setting	Public episode definition	Linked risk-adjustment approach
3M Patient-focused Episode Software	Not reported.	• Event-based episodes per patient • Cohort-based episodes among patients with a shared condition or characteristic	>500	All	No	3M Clinical Risk Groups
Cave Grouper	Urinary tract infection	• Physician relative efficiency and effectiveness scores • High-cost patient prediction	>500	All	No	CCGroup MediScreen
CMS-BPCI	Urinary tract infection	Inpatient and post-acute care	~50	Inpatient, skilled nursing facility, inpatient rehabilitation facility, long-term care hospital or home health agency	Yes	No
McKinsey & Company	Perinatal	Principal Accountable Provider	>100	All	Yes	Yesb
Optum Symmetry Episode Treatment Groups	Pregnancy, with delivery	• Patient total cost of care by condition categories • Provider profiling	>500	All	Yes	Optum Symmetry Episode Risk Groups
OptumInsight Symmetry Procedure Episode Groups	Radical hysterectomy	• Medical and surgical procedure cost • Provider profiling	~200	All	No	Optum Symmetry Episode Risk Groups
Prometheus Analytics	Pregnancy	Potentially avoidable complications	~100	All	Yes	Prometheus Analytics risk adjustment
Medical Episode Grouper	Cardiac arrhythmias	• Population profiling • Provider profiling	>500	All	No	Disease Staging and Diagnostic Cost Groups

Information as of January 2019 in public documentation reviewed for this article, which comprised peer-reviewed articles and Internet searches for vendor product names; sources as cited in the References list. Readers are encouraged to check those and related sources for more details and updated information on the groupers briefly summarized here.

CMS-BPCI Centers for Medicaid and Medicare Services’ Bundled Payments for Care Improvement.

aAs highlighted in public documentation primarily from vendors; this is not an exhaustive list of conceptual orientations among profiled groupers.

bNot detailed in public documentation reviewed for this article in cited sources.

Measuring morbidity vs. measuring episodes: Two parallel views

Clinical risk groups and patient complexity: a case study with a primary care clinic in Alberta

In order to assess the health risk of a population there are two main options: Morbidity adjustment and Episodes of care. The first one can use Clinical Risk Groups, while the latter Patient focused episodes. The morbidity adjustment is useful for adjusting at population level, it is a categorical system, while episode measurement adjusts at patient level .
In this article you'll find an interesting application to a primary care center.

CRGs have definite value with respect to predicting health care utilization, but it is important to note the limitations of the CRG as a stand-alone classification of complexity, particularly for the categorization of patients in the health status 1 through
5 categories. In order to enhance the accuracy, relevance and predictive value of the CRG classification methodology, we see great value in pursuing methods that allow for the careful and systematic inclusion of information from the care record.

The article is trying to use the CRGs for episode measurement, and this is a wrong approach. CRGs are useful as a whole picture, physicians need details, only episodes can provide such information.

Hopper

Patient focused episodes

We all know that no measurement means no management. In health care the measurement of the burden of disease is not that easy. Fortunately at a global level there is the recent study published at Lancet and quoted in this post. If we need to be precise in the measurement with consequences for health care management then we need better tools. Diseases finally appear around episodes, and we may have three type of episodes: event based, disease cohort and population based. The definition of episode needs to be patient-focused rather than disease centered. If you want to know the details of the newest approach to morbidity measurement have a look at this document. It is the evolution of former Clinical Risk Groups towards a new model that will be extremely helpful for management decision making and the definition of appropriate incentives.

PS. Some months ago I explained that new payment systems were in train of being defined. An impact analysis may be found here. My post was titled: A retrofuturist payment system. Now, I would like to change the title once I've seen the details, my proposal is: A complete MESS that needs to be rebuilt from scratch. (to be continued)

PS. Yesterday I attended a book presentation: "I am not Sidney Poitier", by Percival Everett. It was at La Central bookstore. Percival explained the rationale of the book and its subliminal messages.  This is not the kind of novel I'll read.

Una nova mesura de la morbiditat poblacional

 Development and Assessment of a New Framework for Disease Surveillance, Prediction, and Risk Adjustment: The Diagnostic Items Classification System

En Randy Ellis ja abans de la pandèmia estava apuntant cap un nou sistema que permetés la mesura de la morbiditat amb dades diagnòstiques sense una classificació categòrica dels pacients. És a dir es tracta d'una evolució dels models que ell ha proposat des de fa anys junt amb Arlene Ash, els DCGs, i alhora una evolució del Clinical Classification Software.

L'objectiu:

Our objective was to create a clinically detailed, transparent, well-documented, nonproprietary classification system suitable for predicting diverse outcomes using ICD-10-CM diagnostic information and share a core set of predictive models that can be used on other data sets and populations.

Els tres tipus:

We created 3 types of DXIs. The primary or main effect DXIs, called DXI_1, focus on clinical dimensions in each diagnosis. Diagnoses were assigned up to 4 DXI_1s. In some cases, we created both broader and narrower DXI_1s that overlapped because we did not know a priori the level of detail preferred for prediction. We illustrate this approach below in our discussion of sepsis and hypertension in pregnancy DXI_1s.

The second group, DXI_2 modifiers, cut across DXI_1s. Some identify disease severity, such as “with complications,” “hemorrhage,” “secondary,” “bilateral,” and “with coma.” Others may be useful for disease monitoring, including flags for future research and epidemiological surveillance, such as sexually transmitted and vaccine-preventable infectious diseases. Certain diagnoses for external causes and factors influencing health status (whose codes begin with V-Z) were not assigned a DXI_1 and were instead only assigned DXI_2 modifiers.

Finally, DXI_3 scaled variables capture test results, disease severity, or clinically relevant distinctions not easily captured in binary DXI_1 categories. These include body mass index (BMI; calculated as weight in kilograms divided by height in meters squared), neonatal birth weight, neonatal gestational age, pregnancy trimester, low vision/blindness stages, coma scale measures, stroke scores, and duration of unconsciousness. As an example, the DXI_3 variable for BMI, calculated as weight in kilograms divided by height in meters squared, takes on values between 18.5 and 70, corresponding to ordered groups of BMI ranges. When comparing the DXI classification system to existing models, we included only main effects (DXI_1s) as predictors. This comparison cleanly demonstrates the value of the DXIs richer classification of diagnoses. Quantifying the additional value of using DXI_2 and DXI_3 items is left for future research.

 Exemples:

La capacitat predictiva en despesa, aquí:

Si això és així, caldrà provar-ho ben aviat, amb 2929 variables explicatives assoleix una variació explicada del 51%.

El software per provar-lo en SAS aquí.

The right to healthcare access

 Population Health and Human Rights

From NEJM article:

The study of population health encompasses two main objects of analysis: the health conditions affecting a population (the frequency, distribution, and determinants of diseases and risk factors) and the organized social response to those conditions, particularly the way in which that response is articulated in the health system, including the principles and rules that determine who has access to which services and at what cost to whom. These services include both clinical and public health interventions. Since the 19th century, national health systems have sought to provide health services to an increasing proportion of the population, using four eligibility principles: purchasing power, poverty, socially defined priority, and social rights. Reliance on purchasing power means that access is  determined by ability to pay, with governments limiting their role to basic regulation. Because this principle excludes many people, governments have historically intervened to expand access, either through public assistance programs covering families with incomes below a predetermined level or through social insurance schemes for prioritized groups (e.g., the armed forces, industrial workers, civil servants, or older adults). All these eligibility principles result in only  partial coverage, but the ideal of universality has influenced public policy in most countries, though the design and performance of health systems vary widely.