How Population Based Casemix can Improve the Delivery of Primary Care

Transcription

How Population Based Casemix can
Improve the Delivery of Primary Care
Karen Kinder, PhD, MBA
Johns Hopkins Bloomberg School of Public Health
Presented at the 5th Nordic Casemix Conference
Oslo, Norway
June 1, 2012
Goals of Presentation
• To introduce concepts surrounding population-based risk
adjustment
• To review examples of applications worldwide
• To briefly describe the Johns Hopkins “ACG” case-mix
adjustment / predictive risk modeling methodology
Copyright 2007 Johns Hopkins University
2
More than just EMRs
IMPROVED POPULATION
HEALTH STATUS
FEEDBACK LOOP
3
Understanding populationbased risk adjustment
4
Working Definitions
• Case mix (clinical) / risk adjustment
(economic) is the process by which the health
status of a population is taken into consideration
when setting budgets or capitation rates,
evaluating provider performance, or assessing
outcomes of care.
• Predictive risk modeling is the prospective (or
concurrent) application of risk adjustment
measures and statistical forecasting to identify
individuals with high medical need who would
likely benefit from care management interventions.
5
Population Case-Mix vs. Episodic Case -Mix
DRGs categorize individuals,
• considering their experience with the particular provider
• An illness or event,
• over a specified period of time
ACGs categorize individuals,
• considering their entire experience with the health care system,
• their whole morbidity,
• over a period of time
6
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.
7
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%
8
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.
9
Co-morbidity is the norm
among older adults
Diabetes
9%
Heart Disease
11%
Arthritis
12%
Hypertension
22%
21%
22%
17%
20%
Condition + 1
Source: Partnership for Solutions
21%
27%
25%
24%
19%
23%
22%
21%
24%
0%
Single Condition
21%
23%
40%
20%
60%
Condition + 2
16%
80%
Condition + 3
100%
Condition + 4+
10
Co-morbidity
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.
Starfield 03/06
CM 3371
11
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.)
12
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
13
Why interest in risk adjustment 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.
14
Some Real-World Applications
15
Domains
one system. many tools. many solutions. many benefits.Copyright 2007 Johns Hopkins University
16
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.
17
Population Profiling
18
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
• Development of education or outreach
programs
19
Types of Morbidity Varies by Region
20
Morbidity Distribution by ADG
ADGs
Total
Group 1
Group 2
1: Time Limited Minor
14.7%
14.8%
14.4%
3: Time Limited Major
5.5%
4.0%
12.3%
9: Likely to Recur Progressive
2.0%
0.8%
7.7%
10: Chronic Medical: Stable
12.9%
7.4%
37.1%
11: Chronic Medical: Unstable
8.6%
4.0%
28.8%
25: Psychosocial: Recurrent
or Persistent Unstable
5.8%
2.5%
20.1%
26: Signs/Symptoms: Minor
16.9%
15.3%
24.4%
32: Malignancy
1.0%
0.3%
4.0%
21
Stratifying Risk within Diseases
by RUB distribution
Patient
Count
RUB 1
RUB 2
RUB 3
RUB 4
RUB 5
Ischemic Heart Disease (excl AMI)
32
0
0
31.2
40.6
28.1
Cardiac Arrhythmia
33
0
0
18.1
42.4
39.3
Disorders of Lipoid Metabolism
173
0
9.2
61.8
20.2
8.6
Hypertension w/o Major Complications
166
0
7.8
55.4
24.6
12.0
Hypertension w/Major Complications
33
0
0
60.6
18.1
21.2
Type 2 Diabetes, w/o Complication
51
0
13.7
52.9
21.5
11.7
Chest Pain
88
5.6
6.8
51.1
25
11.3
Obesity
43
0
9.3
44.1
41.8
4.6
Anxiety, Neuroses
160
0
11.8
62.5
21.2
4.3
Tobacco Use
57
0
3.5
43.8
40.3
12.2
Depression
40
0
10
57.5
22.5
10
EDC Description
22
Example from Sweden
Relisted 1-Oct-2005-1-Oct-2006 and differences in comorbidity
66.22
0
11.33
1
Not relisted
17.35
2
4.90
3
4
0.19
0
50.00
1
Relisted
10.31
26.32
2
3
4
13.16
0.22
0
20
40
%
60
80
RUB:resource utilization band
Source: Zielinski A, et.al. Impact of comorbidity on the individual's choice of primary
2007 Johns Hopkins University
.
health care provider. Scand J Prim Health Care. 2011Copyright
Jun;29(2):104-9
23
Capitation, Budgeting & Other
Financial Issues
24
Determining the Healthcare Budget
Involves a Variety of Factors
- Available
Budget
- Political
Forces
- Actuarial
Forecasts
Size
of the Healthcare
Pie
25
Risk Adjustment Can Be Used To Slice The Pie
Risk Adjustment
26
Reasons why Risk Adjusted Payment
& Budgeting May Be Necessary
• To protect doctors, practices, or organizations that care
for costlier than average patient populations.
• To help ensure that those that finance care pay their
fair share (neither too high or low).
• To deter providers from selectively attracting healthier
patients .
• To facilitate organizations or providers wishing to
specialize in treating people with higher than average
illness burden.
27
Plans defer 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.
28
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.
29
Risk Adjusted
Performance Profiling
30
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
31
Clinic 1 vs. 2,
Expected Costs
ACG
Clinic 1’s
Panel N
(Expected)
Clinic 2’s
Panel
N (Expected)
Expected
(mean costs)
0100
5 ($500)
8 ($800)
$100
0200
2 ($500)
6 ($1,500)
$250
0300
3 ($900)
3 ($900)
$300
0400
10 ($5,000)
3 ($1,500)
$500
Total
20 ($6,900)
20 ($4,700)
$315
32
Using ACGs to Risk-Adjust Performance “Profiles” of
Provider Groups
Group
Actual
Cost
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
The relative cost is the
ratio of the actual cost to
the plan average cost.
“Illness Burden”
describes the overall
morbidity level of the
population served by
this provider..
“Efficiency” is the
ratio of “actual-toexpected.”
33
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
34
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
Mean Cost (€)
182,58
291,57
274,75
212,19
337,71
289,03
328,99
287,14
196,36
Average
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,
undercost related to standard
Efficiency Index
Impact (€)
0,79
943.068
1,07
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
35
Risk-Adjusted O/E (Efficiency) Profiling Ratios
for GPs Across UK Primary Care Trust (PCT)
(2005)
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
GP1 GP2 GP3 GP4 GP5 GP6 GP7 GP8
No of referrals No of unique prescriptions / month
GP9 GP10 GP11 GP12
No of unique radiology tests
36
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
37
Care Management &
Predictive Modeling
38
Uses
• Predicting the future medical and pharmacy expense
of individuals
• Identifying individuals who would benefit from disease,
case management and care management
interventions
• Identifying persons for inclusion in consumer-oriented
education/outreach programs.
• Identifying persons for inclusion in multi-disease
intensive case-management programs.
• Effectively target single-focus disease management
programs.
• Providing information useful to individual clinicians for
patient-specific quality improvement measures.
39
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
40
Clinical profile of a population by “risk levels”
(Based on JH-ACG risk scores)
ACG Risk Groups: Percentile Rank (Year 1)
Characteristic
0-10
11-25
26-50
51-75
76-90
91-95
95-99
Age, mean
9.1
22.5
28.1
37.1
42.9
47.3
49.4
% female
25.5
29.4
56.6
59.8
63.3
64.0
64.0
# chronic
conditions
.1
.1
.2
.6
1.1
1.9
3.1
Total # of
medical
conditions
1.9
1.8
2.8
3.9
5.6
7.6
10.5
# of drug
therapeutic
classes used
.2
.4
1.3
2.5
4.8
7.4
10.8
Data Source: n=904,007, Privately Insured below the aged of 65
41
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
42
Identify, Stratify, Intervene with Persons with
Diabetes
Our goal:
High
Complexity
Level 1
Moderate Complexity
Level 2
1.
Identify all persons with
diabetes, and
2.
Stratify them into three
levels of complexity, and
3.
Intervene appropriately.
Each level of complexity
has an appropriate level
of care management
intervention
Low Complexity
Level 3
43
Stratification: The Simplified Version
High
Complexity
n = 65
Moderate Complexity
n = 352
Low Complexity
n = 3,332
Diabetes Diagnosis
HbA1C > 9
ACG-pm > .6
Diabetes Diagnosis
HbA1C >7 and < 9
ACG-pm > .2 and < .6
Diabetes Diagnosis
HbA1C <7
ACG-pm <=0.2
44
Our services for Level 1, 2, and 3
Level 1
High risk
with multiple
chronic illness
Intensive Case Management: Guided Care
•RN or Social Work Case Manager
•Individualized Assessment
•Care Plan
•Self-Management Plan
Level 2
Moderate risk patients
with single chronic
illness or risk factors
Level 3
Low risk
Disease Management: Health Coaching and
Lifestyle Management
•Remote monitoring with TeleWatch
•Programs to modify diet, increase exercise,
smoking cessation, weight loss
Health Education and Promotion
•Healthwise information—online and
in print, handbooks and mailing
•Direct messaging via mail and web
•Healthy lifestyle program promotions
45
Predicting Hospital Events
Acute care hospitalization

excluding childbirth and injury
2 prediction periods: 6 and 12 months
Intensive care hospitalization (ICU/CCU)
Extensive length of stay (12+ days
cumulative)
Injury-related acute care hospitalization
46
46
Four Coordination Markers
A majority source of care (and percent of
outpatient visits provided)
A count of the number of unique providers
A count of the number of specialty types
(not the same as number of specialists
seen)
A marker for the ABSENCE of a generalist
6/6/2012
Copyright 2010, Johns Hopkins University
47
47
Two Outputs
• Probability of Unexpected Pharmacy
Cost is a numerical probability score
predicting individuals with moderate or high
morbidity who have unusually large
pharmacy expenditures
• High Risk for Unexpected Pharmacy
Cost is a binary flag that indicates
individuals with a Probability of Unexpected
Pharmacy Cost greater than 0.4.
48
Why Does This Matter?
High Pharmacy Utilization could suggest:
• Inappropriate Care: Physicians who prescribed
expensive medications
• Uncoordinated Care: Potentially replicated
prescriptions
• Drug Abuse: Patients shopping different providers for
prescriptions
• Data Problems: Need to be ruled out
6/6/2012
Copyright 2010, Johns Hopkins University
49
49
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
50
Overview of
the ACG System
51
Basis of the Johns Hopkins ACG System
• ACGs are generally 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
• Comprehensive measure of a population’s risk
and morbidity burden. They do not just categorize
organ system-based diseases. Roots were
primary care / population based.
• There is a comprehensive ACG “suite” of
risk/case-mix measures.
52
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
53
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.
54
Criteria Used to Assign
Diseases/Conditions Into ADGs:
•
Duration
9Acute, chronic or recurrent
• Severity
9Minor/stable versus major/unstable
• Diagnostic certainty
9Symptoms versus disease
• Etiology
9Infectious, injury or other
• Specialty care involvement
55
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
56
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)
57
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
58
Examples of ACG Categories
ACG
0200
0600
1722
2800
4430
5322
Description
Acute Minor, Age 2-5
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
59
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
60
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
61
Example:
The Cardiovascular EDCs
CAR01
Cardio Signs & Symptoms
CAR03
Ischemic Heart Disease
CAR04 Congenital Heart Disease
CAR05 Congestive Heart Failure
CAR06 Cardiac Valve Disorders
CAR07 Cardio-myopathy
CAR08 Heart Murmur
CAR09 Cardiac Arrhythmia
CAR10 Generalized Atherosclerosis
CAR11 Disorder of Lipoid Metabolism
CAR12 Acute Myocardial Infarction
CAR13 Cardiac Arrest/Shock
CAR14 Hypertension w/o Major Comp
CAR15 Hypertension w/ Major Comp
62
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
11: Chronic Medical
complications,
Unstable
uncontrolled
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
63
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
cost within disease category
64
ACG Predictive Models
65
The ACG Predictive Models
• Identifies persons likely to have serious
medical needs and could benefit from case
management programs
• Predicts future resource use of patient
groups within a population
66
Value of Predictive Modeling
Population of Persons Enrolled Across Two Year Period
Prior
High Cost
Year-1
Predicted
High Risk
Year-2
(Prior Use)
Not High
Risk
(Using Year-1
Data)
Actual
High Cost
Year-2
High Risk,
Current Costs
Low, Future
Costs High
67
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
Selected
Resource Use
Measures
($)
(ICD-10 → HOSDOM, Frailty)
68
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.
69
ACG Pharmacy Model
70
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;
71
Why Look at Pharmacy Data?
• Pharmacy data capture a unique constellation of
clinical information
– Non-Concordance
• Expediency -- Pharmacy-based claims are usually
processed within 24 hours while office or hospital
claims can takes several months for adjudication
– Avoids or shortens the “claims-lag” common to
much diagnosis-based risk adjustment
• Provides an alternative data source when claims
are NOT available
72
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
73
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
74
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
75
How are Rx-MGs Used?
• Used for patient profiles
• Understanding 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).
• Identifying patients w/ morbidities NOT identified by
diagnoses
76
In Summary
The future: Risk adjustment / predictive modeling are
part of the solution for many health system challenges
Costs are rising, quality levels are not. Areas where casemix
should play a role:
•
•
•
•
•
Cost saving / efficiency focused interventions
Care coordination and improvement for chronically ill
Performance monitoring
Access for special populations (disparities)
Financial incentives for quality and efficiency
78
Key non statistical considerations
for model selection
• Transparency
– How easily can the model be understood and explained
to all participants?
• Clinical Texture
– Does the system make sense to clinicians/physicians?
• Flexibility
– Does the system support a range of applications?
• Support
– What types of services are offered w/ the product? The
reputation of the vendor?
• Customizability
79
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.
80
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.
81
Over a Dozen nations
• 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
• Active piloting in Denmark, Italy, Hong Kong, Turkey and Chile
• Research in Japan, Lithuania, Thailand, and Taiwan
• Interest expressed in numerous other countries
82
ACGs around the Globe : Concurrent R-squared
Key independent variables in model
Age,
gender
, ADGs
ACGs
alone
Country
Dependent Variables
Age,
gender
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
83
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 socio-economic
measures, individual’s functionality, living arrangement,
and other non-morbidity based markers
• Language
84
Concluding Comment
Case Mix is critical to ensuring
the equitable delivery of health care,
promoting the continuity of care and
enabling the targeting of limited
resources.
85
Opportunities for learning more about
the Johns Hopkins ACG System
• Web Site:
– www.acg.jhsph.edu
• To learn more, contact:
– Dr. Karen Kinder, Executive Director
• [email protected]
86

How Population Based Casemix can Improve the Delivery of Primary Care

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