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The Johns Hopkins ACG System is more than just a risk adjustment
model, it is a comprehensive family of measurement tools
designed to help explain and predict how health care resources
are delivered and consumed. The developers of the ACG System
recognize that no two health care systems are exactly alike,
and so the components of the ACG System have been designed
to allow users to customize the model for their organization’s
needs. The ACG System provides a varied toolkit to develop
the most appropriate solution for each application.
The ACG actuarial cells places every patient into one of
about 90 mutually exclusive case-mix cells based on age,
gender and all of the ICD-9 diagnoses assigned to them by
clinicians during an entire year. The clinical and statistical
algorithms, by which the tens of billions of potential disease
combinations are distilled down to this fixed number of health
status categories, are the essence of ACGs. By using the
ACG System, you can harness the power of the ambulatory and
inpatient diagnostic information already captured within
your organization’s databases.
ACGs:
Adjusted Clinical Groups
The foundation of the system is the original
Adjusted Clinical Group algorithm. ACGs assign persons
to unique, mutually exclusive morbidity categories based
on patterns of disease and expected resource requirements.
ACGs can be used in place of traditional age/sex categories
when attempting to account for variations in morbidity
burden across two or more patient populations. A person
falls into one of 93 mutually-exclusive ACG health status
categories based on a combination of ADGs, age, gender
and, if available, birth weight for newborns and delivery
status for pregnant women. Click
for ACG examples.
ADGs: Aggregated Diagnosis
Groups
ACGs are based on building blocks called
Aggregated Diagnosis Groups (ADGs). Each
ADG is a grouping of diagnosis codes that are similar in
terms of severity and likelihood of persistence of the
health condition over time. All ICD-9
codes assigned by clinicians over an extended period, such
as a year, are assigned to one of 32 ADGs. ADGs can be
considered a type of morbidity marker. A person may have
multiple ADGs. Click
for ADG examples.
EDCs: Expanded Diagnosis
Clusters
To provide a comprehensive clinical
context, the ACG software system also includes Expanded
Diagnosis Clusters (EDCs). EDCs are groupings of diagnostic
codes (see table for examples) that describe the same or
related condition. For example, the many diagnostic codes
that describe different forms of congestive heart failure
are all clustered into a single EDC. EDCs are useful for
examining the epidemiology of disease within a population.
They can also be used as a disease/condition marker for
various applications, such as identifying patients for
inclusion in a targeted disease management program. Click
for EDC examples.
ACG-PM: ACG Predictive
Model
 The
Johns Hopkins ACG toolkit also includes a sophisticated
predictive model that takes full advantage of the capabilities
of the ACG System (see graphic). The model has been calibrated
to identify patients at high risk for using large amounts
of health care resources in the future, and to estimate
potential expenses. Before their health care situation
worsens and service use increases, the ACG “Predictive
Model” (ACG-PM) can help to identify persons who
could benefit from intensive disease management, case management,
and other types of outreach. The ACG-PM can also be used
to estimate future resource use for sub-groups within a
population and the method has many applications within
the quality improvement domain. The ACG-PM also includes
a unique model to assist in the management of pharmacy
benefits.
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