Barbara Starfield:
“how health systems impact health”
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How Information Can Improve
Equity and Efficiency
in the Delivery of
Primary Health Care
Karen Kinder, PHD, MBA
AAFP Family Medicine Global Workshop
San Diego, California
October 13, 2011
Technologies for Primary Care
• Primary care assessment - PCATs
• Morbidity burden: assess and manage
ACG System
• Problem recognition/follow-up (outcomes),
including adverse effects - ICPCs
Information is key to improving health
ANALYTICAL
TOOLS
REPORT
GENERATORS
FEEDBACK LOOP
INTERVENTION
PROGRAMS
IMPROVED POPULATION
HEALTH STATUS
EMR
(DATABASE
WAREHOUSE)
Primary health care is primary
care applied on a population
level. As a population strategy,
it requires the commitment of
governments to develop a
population-oriented set of
primary care services in the
context of other levels and
types of services.
Why Is Primary Care
Important?
Better health outcomes
Lower costs
Greater equity in health
Relationship between Strength of Primary
Care and Combined Outcomes
12
USA
GER
Rank*
10
BEL
AUS
8
Primary Care
SWE
CAN
6
SP
4
NTH
DK
2
FIN
UK
0
*1=best
11=worst
0
1
2
3
4
5
6
7
Outcomes Indicators (Rank)
8
9
Average Rankings for World Health
Organization Health Indicators for Countries
Grouped by Primary Care Orientation
DALEs
Child Survival
Equity
Overall
Health
Worse primary care
(Belgium, France,
Germany, US)
16.3
22.5
36.3
Better primary care
(Australia, Canada,
Sweden, Japan, Denmark,
Finland, Netherlands,
Spain, UK)
11.0
15.8
29.1
DALE: Disability adjusted life expectancy (life lived in good health)
Child survival: survival to age 2, with a disparities component
Overall health: DALE minus DALE in absence of a health system
Maximum DALE for health expenditures
minus same in absence of a health system
Source: Calculated from WHO,
World Health Report 2000.
In 7 African countries
• The highest 1/5 of the population receives
well over twice as much financial benefit
from overall government health spending
(30% vs 12%).
• For primary care, the poor/rich benefit ratio
is much lower (23% vs 15%).
“From an equity perspective, the move
toward primary care represents a clear step
in the right direction.”
Source: Gwatkin, Int J Epidemiol 2001; 30:720-3, based on
Castro-Leal et al, Bull World Health Organ 2000; 78:66-74.
Studies in other developing and middle income
countries also show benefit from primary care
reform.
• In Bolivia, reform in deprived areas lowered
under-5 mortality rates compared with
comparison areas.
• In Costa Rica, primary care reforms in the 1990s
decreased infant mortality and increased life
expectancy to rates comparable to those in
industrialized countries.
• In Mexico, improvements in primary care
practices reduced child mortality in socially
deprived areas.
Sources: Perry et al, Health Policy Plann 1998; 13:140-51; Reyes et al, Health Policy Plann 1997; 12:214-23;
Rosero-Bixby, Rev Panam Salud Publica 2004; 15:94-103; Rosero-Bixby, Soc Sci Med 2004; 58:1271-84.
Primary Care Oriented
Countries Have
•
•
•
•
Fewer low birth weight infants
Lower infant mortality, especially postneonatal
Fewer years of life lost due to suicide
Fewer years of life lost due to “all except
external” causes
• Higher life expectancy at all ages except at age
80
• BETTER HEALTH AT LOWER COSTS
Sources: Starfield. Primary Care: Balancing Health Needs, Services, and
Technology. Oxford U. Press, 1998. Starfield & Shi, Health Policy 2002; 60:201-18.
Primary Care Oriented
Countries Have
• more equitable resource distributions
• health insurance or services that are provided by
the government
• little or no private health insurance
• no or low co-payments for health services
• Are rated as better by their populations
• primary care that includes a wider range of
services and is family oriented
Sources: Starfield. Primary Care: Balancing Health Needs, Services, and
Technology. Oxford U. Press, 1998. Starfield & Shi, Health Policy 2002; 60:201-18.
Specialists vs. Primary Care Providers
In the United States, half of all
outpatient visits to specialist physicians
are for the purpose of routine follow-up.
Does this seem like a prudent use of
expensive resources, when primary
care physicians could and should be
responsible for ongoing patient-focused
care over time?
Source: Valderas et al, Ann Fam Med 2009;7:104-11.
In New Zealand, Australia, and the US,
an average of 1.4 problems (excluding
visits for prevention) were managed in
each visit. However, primary care
physicians in the US managed a narrower
range: 46 problems accounted for 75% of
problems managed in primary care, as
compared with 52 in Australia and 57 in
New Zealand.
Source: Bindman et al, BMJ 2007; 334:1261-6.
The Primary Care
Assessment Tools
(PCATs)*
*©Johns Hopkins University
Primary care is the provision of
first contact, person-focused
ongoing care over time that
meets the health-related needs
of people, referring only those
too uncommon to maintain
competence, and coordinates
care when people receive
services at other levels of care.
Evaluating the Delivery
of Primary Care
An existing suite of instruments makes it possible to
evaluate the primary care orientation of health systems and
facilities. It includes surveys of:
• Professionals knowledgeable about the health system
• People in communities
• Patients, professionals, and administrators of health care
facilities
It is known as the PCAT (Primary Care Assessment Tool).
The PCAT is used to assess the achievement of primary care
History of the PCAT
The impetus for the development of the
primary care assessment tools began in
the early 1990s, when the need for health
systems oriented around primary (health)
care began to be recognized.
The first widespread attention to
assessment of primary care came with the
conduct and publication of the international
comparisons of primary care.
This international comparison initially selected
10 industrialized countries with populations of at
least 4 million and data from the mid 1980s, later
expanding the number to 13, with data from the
early to mid 1990s.
Health statistics were obtained from reputable
international sources, and information was
obtained from country experts regarding health
system characteristics.
Clear definitions for each health system
characteristic were provided.
Sources: Starfield, Lancet 1994;344:1129-33.
Starfield & Simpson, JAMA 1993;269:3136-9.
Primary Care Orientation
of Health Systems:
Rating Criteria
Each country was rated (scores
of 0, 1, or 2) on the strength of 9
characteristics of health policy
that are conducive to strong
primary care.
Practice Characteristics
(Rank*)
System (PHC) and Practice (PC) Characteristics
Facilitating Primary Care, Early-Mid 1990s
12
11
10
9
8
7
6
5
4
3
2
1
0
GER
FR
BEL
US
SWE
JAP
CAN
FIN
AUS
SP
DK
NTH
UK
0
1
2
3
4
5
6
7
8
9 10 11 12 13
System Characteristics (Rank*)
*Best level of health indicator is ranked 1; worst is ranked 13;
thus, lower average ranks indicate better performance.
Based on data in Starfield & Shi, Health Policy 2002; 60:201-18.
At the same time the international
comparisons were being carried out, efforts
were initiated to develop a tool that could be
used to assess the clinical aspects of
primary care. This set of tools because
known as the PCAT – Primary Care
Assessment Tools.
These tools were initially tested for reliability
and validity in the United States. Within a
decade, they had also been tested in Spain
and in Canada.
Utility of the PCATs
• To compare one type of facility with
another
• To compare one type of practitioner
with another
• To compare one country or region with
another
• To detect particular functions that
appear to be suboptimal, and explore
why
PCAT Versions
Primary Health Care
Systems Assessment
Primary Care
Adult consumer long/short
Child consumer long/short
Facility long/short
Provider long/short
Primary Care Orientation of
Health Systems - Domains
– First-contact
–
–
–
–
–
–
Person-focus over time
Comprehensiveness
Coordination
Family-centeredness
Community orientation
Cultural competence
Source: Starfield. Primary Care: Balancing Health Needs,
Services, and Technology. Oxford U. Press, 1998.
The Primary Care Assessment
Tool  Systems Version
This tool assesses the primary health care
and primary care characteristics at the
system level. It addresses all of the primary
care functions.
It is being considered for widespread use in
comparing the primary care orientation of
different health systems, both within and
across countries.
Domains of the Systems PCAT
• Equity in distribution of resources
• Universality of financing
• Role of government in policy regarding quality,
comprehensiveness, and payment for services
How Are the Features of Primary
Care Actually Measured?
Principle: Each domain of primary (health)
care has two subdomains, one related to
important characteristics of the facility or
practice and one related to the performance
of the practitioner or facility on primary care
functions.
Primary Care Domains and
Subdomains: First Contact
First-contact: accessibility
• Health system characteristics that facilitate
access; e.g., if closed on weekend days would the
individual be seen by a practitioner from the
facility?
First-contact: utilization
(consumer only)
• Use of primary care place for each new need
(regular checkup, immunization, an acute illness.)
Primary Care Domains and
Subdomains: Longitudinality
Longitudinality: strength of affiliation
(consumer only)
• Strength of relationship with a specific provider,
e.g., degree to which the identified provider is also
the place who knows the individual best and from
whom care would be sought for a new problem.
Longitudinality: interpersonal relationship
• Person orientation of practitioner/patient
interactions, e.g., degree of interest of doctor in the
individual as a person, rather than as someone
with a medical problem.
Primary Care Domains:
Comprehensiveness
Comprehensiveness in primary care is
necessary in order to avoid unnecessary
referrals to specialists, especially in
people with comorbidity
Primary Care Domains and
Subdomains: Comprehensiveness
Comprehensiveness: services available
• Availability of 11 specific services, e.g., family
planning.
Comprehensiveness: services provided
• Services received from the primary care
source, e.g., discussions of ways to stay
healthy.
Primary Care Domains and
Subdomains: Coordination
Coordination: medical record continuity
(provider only)
• Do you use flow sheets to assure that needed
services are provided? (Also, printed practice
guidelines, periodic medical audits, problem lists,
medication lists.)
Coordination: integration of referrals
• Quality of primary care-referral interface, e.g.,
Did the primary care practitioner know you made
a visit to a specialist?
Primary Care Domains and Subdomains:
Family Centeredness
• Family-focused personnel
• Methods to record family needs
• Family representation in policy-making
• Family focus groups/meetings
Primary Care Domains and
Subdomains: Community Orientation
• Knowledge of community characteristics
• Knowledge of primary care needs
• Methods to identify community needs
• Monitoring of service effectiveness
Primary Care Domains and
Subdomains: Cultural Competence
• Provides care in native language/ dialect/ has
translator/ provides language-appropriate
educational materials
• Employs individuals from community as advocates
• Appreciates the impact of poverty on health and
responsiveness to medical intervention
PCAT Languages
•
•
•
•
•
•
•
•
English
Spanish
Catalan
Portuguese
French (Quebecois)
Korean
Turkish
In progress: Mandarin, Maltese
Some of the countries in which the PCATs are being
used or is planned for use (other than just for
research), as of 2011:
•
US (some patient-centered medical home demonstrations);
•
Spain;
•
Brazil;
•
Korea;
•
Turkey;
•
Hong Kong and PRC;
•
Uruguay;
•
Vietnam;
•
Malaysia;
•
South Africa
Managing Co-morbidity
in a Population
Co-morbidity is the concurrent
existence of one or more unrelated
conditions in an individual with any
given condition. Multi-morbidity is
the co-occurrence of biologically
unrelated illnesses.
For convenience and by common
terminology, we use co-morbidity
to represent both co- and multimorbidity.
People and populations differ in
their overall vulnerability and
resistance to threats to health.
Some have more than their
share of illness, and some have
less. Morbidity mix (sometimes
called case-mix) describes this
clustering of ill health in patients
and populations.
Not all persons have the
same need for health care
Percent of
Population
Percent of Health
Care Dollars
Consumed
1%
30%
10%
70%
50%
97%
Comorbidity Prevalence
The percentage of Medicare beneficiaries with 5+ treated
conditions increased from 31 to 40 to 50 in 1987, 1997,
2002.
The percentage of those with 5+ treated conditions who
reported being in excellent or good health increased from
10% to 30% between 1987 and 2002.
MESSAGE: “Discretionary diagnoses” are increasing in
prevalence, particularly those associated with new
pharmaceuticals. How much of this is appropriate?
Source: Thorpe & Howard, Health Aff 2006; 25:W378-W388.
Total morbidity is not the same as the
sum of different diseases, because
diseases cluster and are inter-related
in various ways.
A more accurate way of
characterizing morbidity is to
characterize the pattern of diseases
in people and populations.
Co-morbidity is the norm
among older adults
Diabetes
9%
Heart Disease
11%
Arthritis
12%
Hypertension
22%
21%
20%
Condition + 1
Source: Partnership for Solutions
27%
24%
23%
24%
0%
21%
25%
22%
17%
Single Condition
21%
19%
22%
23%
40%
Condition + 2
21%
20%
60%
16%
80%
Condition + 3
100%
Condition + 4+
These patterns are linked to the prevalence of
chronic co-morbidities (Data for Americans 65+)
# Chronic
Co-morbidities
% Pop.
Relative
Cost
(Per Pt.)
Est. % of
Total
Medicare
Costs
Avg. #
Unique
MDs/Yr.
Avg. #
Filled
Rx / Yr.
5+
20%
3.2
66%
13.8
49
3-4
27%
.9
23%
7.3
26
0-2
53%
.1
11%
3.0
11
Data Source: G. Anderson et. al., Johns Hopkins Univ. 2003. (Derived from Medicare claims and
beneficiary survey.)
Co-morbidity, Inpatient Hospitalization,
Avoidable Events, and Costs*
400
16000
362
13,973
(4 or more
conditions)
350
14000
296
300
12000
250
10000
216
233
200
8000
169
182
150
6000
152
119
4701
Costs
Rate per 1000 beneficiaries
267
119
100
4000
74
86
2394
40
50
1154
211
20
34
8
1
0
0
8
4
1
2000
57
2
17
3
0
4
5
6
7
8
9
10+
Number of types of conditions
ACSC
Source: Wolff et al, Arch
Intern Med 2002; 162:2269-76.
Complications
Costs
Starfield 10/03
*ages 65+, chronic conditions only
The greater the morbidity burden,
the greater the persistence of any
given diagnosis.
That is, with high comorbidity,
even acute diseases are more
likely to persist.
With high morbidity burden, the number
of different physicians seen rises to a
greater extent than is the case for
number of visits, for both primary care
and specialist care.
Therefore, coordination of care is a
major challenge for those with high
morbidity burden.
Controlled for morbidity burden*:
The more DIFFERENT generalists seen: higher
total costs, medical costs, diagnostic tests and
interventions.
The more different generalists seen, the more
DIFFERENT specialists seen among patients with
high morbidity burdens. The effect is independent
of the number of generalist visits. That is, the
benefits of primary care are greatest for people
with the greatest burden of illness.
*Using the Johns Hopkins Adjusted Clinical Groups (ACGs)
Source: Starfield et al, J Ambul Care Manage 2009;32:216-25.
Importance of Co-morbidity
•
•
•
•
Disease case management
Guideline relevance
Costs and complications
Orientation of health systems
– Primary care vs specialty care
– Appropriate use of specialty services
• Quality of care: processes vs.
outcomes
Starfield 10/03
Clinical Observations Underpin
the ACG System
• Morbidity is NOT randomly distributed
across individuals.
– 1) Morbidity “clusters”.
– 2) Diagnoses co-occur.
• The “illness burden” of providers’ practices
is NOT randomly distributed.
– 1) Some providers care for “sicker” patients.
– 2) Sick patients choose certain providers referentially.
The ACG System:
Concept and Method
Case Mix
Case mix ( risk adjustment ) is the
process by which the health status
(morbidity profile) of a population is taken
into consideration when setting budgets
or capitation rates, evaluating provider
performance, or assessing outcomes of
care.
What Can Be Achieved with
Case Mix Adjustment
• Equity and fairness
• To identify those patients most in need of health care
resources
• To facilitate providers who specialize in treating patients
with higher than average illness burden.
• Create incentives to encourage providers to match
services to needs (appropriateness)
• Ensure appropriate comparisons for research and
performance assessment
History of ACGs
• The ACG System grew out of clinical observations made
by Barbara Starfield, MD, MPH, in pediatric populations.
• Research by Dr. Starfield and her colleagues in the early
1980s showed that children using the most health care
resources were not those with a single chronic illness, but
rather children with multiple, seemingly unrelated
conditions.
• Dr. Starfield was able to extend these findings to all
patients and ultimately demonstrate that the clustering of
morbidity is a better predictor of health services resource
use than the presence of specific diseases.
Conceptual Basis for ACGs
• Individual diagnoses are less important in the
care of patients and populations than are
patterns and overall burdens of morbidity
• Models of care need to be based on overall
morbidity burdens rather than on specific
diagnoses
• Assessing the appropriateness of care needs to
be based on patterns of morbidity rather than on
specific diagnoses
Overview of
the ACG System
Overview of Johns Hopkins
ACG System
• TOTAL POPULATION – Not just those who have been
in hospital and includes non-users.
• TOTAL EXPERIENCE - Applied using all diagnoses
describing the person. They do not focus on individual
visits. Ideally they are derived from primary and
specialty ambulatory contacts as well as inpatient.
• TOTAL PERSON -Comprehensive measure of a
population’s risk and morbidity burden. They do not just
categorize organ system-based diseases.
ACG Actuarial Cells Reflect the
Constellation Of Health Problems
Experienced by a Patient
Time Period (e.g., 1 year)
Treated
Morbidities
Visit 1
Diagnostic
Codes
Morbidity
Groups
Code A
ADG10
Code B
Visit 2
Code C
ADG21
Visit 3
Code D
ADG03
Clinician
Judgment
Clinical
Grouping
ACG
Category
Data
Analysis
What is an ADG?
• Definition: An ADG is a morbidity cluster
that indicates severity and persistence of a
patient’s condition treated over time.
• Diagnoses within the same ADG are similar
in terms of clinical criteria and expected
need for health care resources.
• ADGs are not mutually exclusive.
Criteria Used to Assign
Diseases/Conditions Into ADGs:
•
Duration
Acute, chronic or recurrent
• Severity
Minor/stable versus major/unstable
• Diagnostic certainty
Symptoms versus disease
• Etiology
Infectious, injury or other
• Specialty care involvement
Assignment of ICD* Codes to
ADGs: Diabetes Mellitus
ICD-9 Code
Description
ADG
250.0
Diabetes Mellitus Uncomplicated
10: Chronic Medical Stable
250.03
Diabetes Mellitus without
complications
11: Chronic Medical Unstable
250.1
Diabetes with Ketoacidosis
09: Likely to Recur, Progressive
362.0
Diabetes Retinopathy
18: Chronic Specialty, UnstableEye
* ICD – WHO’s international classification of disease.
Can also be use with Read codes and ICPC codes
ADGs and Health Care Needs
Evidence from BC
Relationship between No. of ADGs and Hospitalization & Death,
BC Adults 1996/97
60%
Percent
50%
40%
Hospitalization
30%
Death
20%
10%
0%
1
2-3
4-5
6-9
10 +
Number of Ambulatory Diagnosis Groups (ADGs)
ACG Actuarial Cells Reflect the
Constellation Of Health Problems
Experienced by a Patient
Time Period (e.g., 1 year)
Treated
Morbidities
Visit 1
Diagnostic
Codes
Morbidity
Groups
Code A
ADG10
Code B
Visit 2
Code C
ADG21
Visit 3
Code D
ADG03
Clinician
Judgment
Clinical
Grouping
ACG
Category
Data
Analysis
Examples of ACG Categories
ACG
Description
0200
0600
Acute Minor, Age 2-5
1722
2800
4430
5322
Likely to Recur, without Allergies
Pregnancy: 2-3 ADGs, no major
ADGs, not delivered
Acute Major and Likely to Recur
4-5 other ADG combinations, Age
> 44, 2+ major ADGs
Infants: 0-5 ADGs, 1+ major
ADGs, low birth weight
Expanded Diagnosis Clusters
(EDCs)
• EDCs categorize different diseases and
conditions
• Based only on ICD codes (no procedure codes)
• EDCs complement ACGs by serving as a
surrogate for chronic condition episodes.
• Provides a greater clinical context to the case-mix
• Useful for examining the epidemiology of a
population or comparing two populations
• Can help identify patients for inclusion in DMPs
27 Major EDCs
•
•
•
•
•
•
•
•
•
•
Administrative
Allergy
Cardiovascular
Dental
Ear, Nose, Throat
Endocrine
Eye
Female Reproductive
Gastrointestinal/Hepatic
General Signs and
Symptoms
• General Surgery
• Genetic
• Genito-urinary
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Hematologic
Infections
Malignancies
Musculoskeletal
Neonatal
Neurologic
Nutrition
Psychosocial
Reconstructive
Renal
Respiratory
Rheumatologic
Skin
Toxic Effects and Adverse
Events
Example:
The Cardiovascular EDCs
CAR01
CAR03
Cardio Signs & Symptoms
Ischemic Heart Disease
CAR04
CAR05
CAR06
CAR07
CAR08
Congenital Heart Disease
Congestive Heart Failure
Cardiac Valve Disorders
Cardio-myopathy
Heart Murmur
CAR09
CAR10
CAR11
CAR12
Cardiac Arrhythmia
Generalized Atherosclerosis
Disorder of Lipoid Metabolism
Acute Myocardial Infarction
CAR13 Cardiac Arrest/Shock
CAR14 Hypertension w/o Major Comp
CAR15 Hypertension w/ Major Comp
Assignment of ICD Codes to
EDCs: Diabetes Mellitus
ICD-9 Code
Description
ADG
EDC
250.0
Diabetes Mellitus
Uncomplicated
10: Chronic Medical
Stable
END06: Type 2
diabetes, w/o
complication
250.03
Diabetes Mellitus without
complications,
uncontrolled
11: Chronic Medical
Unstable
END08: Type 1
diabetes, w/o
complication
250.1
09: Likely to Recur,
Diabetes with Ketoacidosis
Progressive
END07: Type 2
diabetes, with
complication
648.0
Gestational Diabetes
11: Chronic Medical
Unstable
FRE04: Pregnancy
and Delivery with
Complications
362.0
Diabetes Retinopathy
18: Chronic Specialty,
Unstable-Eye
EYE13: Diabetic
Retinopathy
How are EDCs Used?
• EDCs are used primarily for looking at disease
prevalence and Standardized Morbidity Ratios
(SMRs) -- which tell us, is the prevalence of
the sub-group of analyses different than the
overall population from which the sub-group
was drawn.
• EDCs are also used to demonstrate variability
of risk within disease category
Co-Morbidity Adjusted Costs
By Disease Category
Disease Group
(Based on EDCs)
Distribution by “RUB”
Morbidity Level Group
Relative Cost of those in
each RUB Group
Low
Mid
High
Low
Mid
High
Total Population
49.0
27.5
4.0
0.33
1.64
9.80
Asthma
24.0
63.8
12.2
0.44
1.76
10.05
Hypertension
20.7
65.4
13.9
0.34
1.85
11.60
Ischemic Heart Dis.
3.9
49.0
47.1
0.58
2.20
12.19
CHF
2.6
35.1
62.3
0.58
2.33
16.47
Diabetes
13.9
63.2
22.9
0.39
1.92
11.75
Osteoporosis
11.1
50.0
38.9
0.33
2.27
12.43
Thrombophlebitis
12.2
53.8
33.9
0.45
2.15
13.68
Depression
8.1
66.3
25.6
.042
2.20
13.14
ACG Predictive Models
The ACG Predictive Models
Predictive modeling is a process that applies
available data to:
• Identify persons who have high medical need
and are “at risk” for above average future medical
service utilization and therefore could benefit from
case management programs
• Predicts future resource use of patient groups
within a population
Value of Predictive Modeling
Population of Persons Enrolled Across Two Year Period
Prior
High Cost
Year-1
Predicted
High Risk
Year-2
(Prior Use)
(Using Year-1
Data)
Not High
Risk
Actual
High Cost
Year-2
High Risk,
Current Costs
Low, Future
Costs High
Risk Factors in the Johns Hopkins Predictive
Model
Age
Overall Disease
Burden
Gender
(ICD-10 
ACG)
Risk Score
Medications
(ATC Codes  Rx-MG)
Selected Medical
Conditions
(ICD-10  Expanded Dx
Clusters)
Special Population Markers
(ICD-10  HOSDOM, Frailty)
Selected
Resource Use
Measures
($)
The Johns Hopkins ACG System:
An Expanding Suite of Measures and
Tools
• Dx-PM – a “predictive model” that uses diagnoses to
calculate a score representing future risk. Based on ACGs,
EDCs and special high risk markers.
• Rx-PM - a predictive model that calculates a score
representing future risk and expected resources use
based only on pharmacy use history.
• DxRx-PM - The Rx-PM and Dx-PM measures can be
combined if both sources are available to calculate a
predictive score.
ACG Pharmacy Model
Combining both diagnoses and
prescription data provides expanded
information
Total # of
patients
identified
(ICD or pharm.)
Percent of
patients
identified by
diagnosis
Percent of
patients
uniquely
identified
pharmacy
Hypertension
59,937
70%
30%
Disorders of Lipoid metabolism
37,736
61%
39%
Congestive Heart Failure
11,223
61%
39%
1646
81%
19%
Depression and Anxiety
20,863
23%
77%
Diabetes
27,656
55%
45%
Condition
Chronic Renal Failure
Source: US HMO claims dataset of elderly n=90,000 in 2001;
Why Look at Pharmacy Data?
• Pharmacy data capture a unique constellation
of clinical information
• Expediency -- Pharmacy-based claims are
usually processed within 24 hours while office
or hospital claims can takes several months for
adjudication
• Provides an alternative data source when
claims are NOT available
Clinical Criteria for Rx-MG
Assignment
1) Morbidity-type
- symptom v disease
2) Duration of morbidity
- chronic v time-limited
3) Stability of morbidity
- stable v unstable
4) Route of administration
- oral, inhaled, topical, intramuscular, intravenous
5) Therapeutic goal
- curative, palliative, preventive
The Major Rx-MG Categories
•
•
•
•
•
•
•
•
Allergy/Immunology
Cardiovascular
Ears, Nose, Throat
Endocrine
Eye
Female Reproductive
Gastrointestinal/Hepatic
General Signs &
Symptoms
• Genito-urinary
• Hematologic
•
•
•
•
•
•
•
•
Infections
Malignancies
Musculoskeletal
Neurologic
Psychosocial
Respiratory
Skin
Toxic Effects/
Adverse Reactions
• Others / non-specific
medications
Rx-MG Example: Corticosteroids
Active Ingredient
Route of
Administration
Rx-MG
Description
methylprednisolone-neomycin
topical
SKNx020
Skin / Acute and Recurrent
Prednisolone
compounding
ALLx030
Allergy/Immunology / Immune Disorders
Prednisolone
injectable
MUSx020
Musculoskeletal / Inflammatory Conditions
Prednisolone
oral
ALLx030
Allergy/Immunology / Chronic Inflammatory
Prednisolone
ophthalmic
EYEx020
Eye / Acute Minor: Palliative
Prednisolone-sodium sulfacetamide
ophthalmic
EYEx010
Eye / Acute Minor: Curative
Beclomethasone
Compounding
RESx030
Respiratory / Cystic Fibrosis
Dexamethasone
Nasal
ALLx010
Allergy/Immunology / Acute Minor
Betamethasone
Injectable
ENDx020
Endocrine / Chronic medical
Dexamethasone
Compounding
ALLx030
Allergy/Immunology / Chronic Inflammatory
ciprofloxacin-dexamethasone otic
Otic
EARx010
Ears, Nose, Throat / Acute Minor
Dexamethasone
Intravenous
MUSx020
Musculoskeletal / Inflammatory Conditions
beclomethasone
inhalation
RESx040
Respiratory / Airway Hyperactivity
betamethasone-calcipotriene topical
topical
SKNx030
Skin / Chronic Medical
Applications
Possible Applications
• Population based need-assessment
across patient populations (e.g.,regions,
vulnerable patient groups)
• Assessing performance of providers (e.g.
hospital clinics, doctors, regions).
• Resource allocation / budgeting across
clinics, regions or other care units.
• “Predictive Risk” measurement to assist
in chronic care management.
• Quality improvement comparisons.
Population Profiling
Benefits of Health Status Monitoring
• Understanding population risk and overall
morbidity patterns
• Detection of life style issues that may lead to
health problems
• Ability to identify changes in population health
• Tailoring of health promotion and education
programs
Types of Morbidity Varies by Region
Capitation, Budgeting & Other
Financial Issues
Determining the Healthcare Budget
Involves a Variety of Factors
- Available
Budget
- Political
Forces
- Actuarial
Forecasts
Size
of the Healthcare
Pie
Risk Adjustment Can Be Used To Slice The Pie
Risk Adjustment
Plans differ in the morbidity burden of
their patients
1,3
Families/Children
Disabled
Risk Ratio
1,2
1,1
1
Average
Risk
0,9
0,8
0,7
Plan A
Plan B
Plan C
Plan D
All
Using ACGs, risk ratios were determined for each contracting managed care organization / health plan.
Expected values were determined separately for the two enrollee groups with this State Medicaid
program.
Alternative Ways to Apply
Case-Mix to Payment
• Applied concurrently, budgets are adjusted
retrospectively based on the experienced
morbidity profile of the population.
• Applied prospectively, capitation amounts are
adjusted based on the anticipated need for
health care resources.
• The portion of the payment which is case-mix
adjusted is arbitrary.
Risk Adjusted
Performance Profiling
Interpreting Profiling Results…
140
100
80
60
40
20
Potential
Access
Issues /
Witholding
Services
Performance
Feedback /
Contracting /
Incentives
Over Utilization /
Potential
Fraud/Abuse
0
<.
70
0,
70
0,
75
0,
80
0,
85
0,
90
0,
95
1,
00
1,
05
1,
10
1,
15
1,
20
1,
25
1,
30
>1
.3
Number of Physicians
120
Efficiency Index
Clinic Profiling
Pharmacy cost x patient: observed (
) and expected (
Efficiency Index: 0,79
)
Efficiency Index: 1,27
21% undercost
943.000 €
27% overcost
737.000 €
400
350
300
250
200
150
100
50
0
001
002
003
004
005
006
007
008
009
Average
Mean Cost (€)
182,58
291,57
274,75
212,19
337,71
289,03
328,99
287,14
196,36
270,49
Mean cost (€) expected
231,02
271,59
293,94
243,63
296,59
295,57
258,10
280,21
241,01
270,49
Overcost or undercost, related to standard
Efficiency Index
0,79
1,07
Impact (€) 943.068 510.658
0,97
0,87
1,14
0,98
1,27
280.254 481.278
715.386
121.540
736.869
1,02
144.487
0,81
281.209
Using ACGs to Risk-Adjust Performance
“Profiles” of Provider Groups
PMPM $
Unadjusted
Relative Cost
ACG “Illness
Burden”
ACG Adjusted
Efficiency Ratio
#1
$157
1.22
1.02
1.20
#2
153
1.19
1.21
0.99
#3
144
1.12
0.92
1.22
#4
98
0.76
0.69
1.11
All*
$129
1.00
1.00
1.00
Group
Risk Adjusted Practice Efficiency of Doctor Group #3 By
Service Category
Type of Service
Relative
Cost
ACG Illness
Burden
Efficiency
Inpatient
0.91
0.90
1.01
Primary Care
1.20
1.15
1.04
Surgery
2.23
0.91
2.45
Medical Specialties
1.61
0.92
1.75
Lab & x-ray
1.77
0.85
2.08
Pharmacy
.86
0.85
1.01
1.12
0.92
1.22
Total
How Profiling Results are
Typically Applied
• Developing financial incentives
– Distributing bonuses
– Defining tiered networks
– Differentiating fee schedules
• Profiling/Assessment tool
– To stimulate voluntary changes in behavior by sharing
valid data presented in a useful format.
– To identify potential fraud & abuse.
Can be developed on a number of entities, to include:
physicians, employers, networks, or health systems
Care Management &
Predictive Modeling
Using PM Risk Scores to Target Disease
Management Program Participants
% Enrollees in Rx-MG
Risk Category
Condition
Below
90%
Diabetes
Congestive
Heart
Failure
Resource Use of Cohort
Relative to Total Population
90-95%
Above
95%
Below
90%
90-95%
Above
95%
44.97
42.1
11.9
1.34
4.90
7.44
19.75
53.5
26.75
1.14
6.02
7.93
Tier 1
Tier 2
Tier 3
The ACG Software provides patient risk information in
support of nurse case managers
• Numerous co-morbidities
• Seeing 13 doctors
• At risk for future
hospitalization
• ER Visit with no admission
• Poly-pharmacy use
• Tobacco Use
Benefits of PM for Care
Management
• Provides robust clinical information
– Diagnosis-based condition markers
– Pharmacy-based morbidity markers
• Administratively efficient
– Rapid assessment
– Reductions in case finding and case preparation
– Allows for better allocation of scarce case
management resources
• Identifies up to 25% unique individuals for case
management compared to traditional methods
– Using pharmacy, this holds true with as little as 1
month of data
Real World Experiences
Interest in case mix is
increasing globally
• Population health care needs are rising, resource
availability is not; focusing on “higher risk” patients
makes sense.
• Data systems and data collection are improving.
• Management systems are integrating primary,
secondary, and community care.
• There is an increased interest in the equitable
delivery of health care.
ACG System’s
International Presence
•
•
•
•
•
•
•
•
•
•
•
•
Several Provinces in Canada
Numerous County Councils in Sweden
Several Regions of Spain
Multiple Primary Care Trusts in the UK
Sickness Funds in Germany
The largest Health Plan in Israel
Two Medical Schemes in South Africa
The veterans medical system in Taiwan
The Ministry of Health in Malaysia
Active piloting in Turkey, Denmark, Hong Kong and Chile
Research in Lithuania, Belgium, Korea, Thailand, the
PRC and Japan
Interest expressed in numerous other countries, including
the Middle Eastern Region
Success Stories
ACGs around the Globe :
Concurrent R-squared
Key independent variables in
model
Age,
gend
er,
ADGs
ACGs
alone
Country
Dependent Variables
Age,
gend
er
United States
Total Costs (including
pharmacy)
0.13
0.55
0.37
Canada - Manitoba
Ambulatory costs
0.08
0.50
0.43
Taiwan
Physician Costs
0.12
0.52
0.47
Dr. Visits
0.06
0.58
0.53
Primary care costs
0.11
NA
0.38
GP visits
0.13
0.59
0.53
Sweden
Spain
United Kingdom
GP visits
0.54
ACG System is Customizable
Recent Developments enable customizability for
• Local Diagnostic coding systems
• Local Pharmaceutical coding systems
• Incorporation of local resource measures
(costing measures)
• Local Practice Behaviour Patterns
• Incorporation of available data on socioeconomic measures, individual’s functionality,
living arrangement, and other non-morbidity
based markers
• Language
In Summary
The Johns Hopkins ACG System
• Comprehensive measure of risk and morbidity burden. They do
not just categorize organ system-based diseases.
• Roots were primary care / population based.
• ACGs are applied using all diagnoses (and/or pharmacy
information) describing the person. They do not focus on
individual visits. Ideally they are derived from primary and
specialty ambulatory contacts as well as inpatient
• Based at internationally respected academic institution provides
for stability, transparency, as well as ongoing support and
development. We have been doing this for 30 years.
• Johns Hopkins has been developing collaborative IT / consulting
and academic support infrastructure around the globe and
currently have projects in 17 countries.
Case Mix is critical to ensuring
the equitable delivery of health care,
promoting the continuity of care and
enabling the targeting of limited
resources.
In Closing…….
We have instruments to assess the
utility of health systems, the strength of
primary care, and the outcomes as
measured by morbidity burden. We
need the political will to use them.
For More Information
• PCATs
– www.jhsph.edu/pcpc/pca_tools.html
• ACG System
– www.acg.jhsph.edu
• Dr. Karen Kinder
– [email protected]
Starfield 10/03
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