Predicting Persistently High Primary Care
Use
James M. Naessens, MPH
Macaran A. Baird, MD, MS
Holly K. Van Houten, BA
David J. Vanness, PhD
Claudia R. Campbell, PhD
© 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved.
Identification of Costly Patients
• Many factors related to high use
• Patient demographics
• Certain diagnoses
• Chronic conditions
• Disability
• Severity of disease
• Prior use (health care and medications)
© 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved.
Focus of Identification
• Total health care spending
• Case management
• Hospitalization
• Disease management
© 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved.
Physician Visits
Employee Health Plan, 1997
50%
44%
40%
30%
20%
11%
10%
0%
0
2
4
6
# of visits
Patients
8
10+
Visits
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Physician Visits - Specialty Care
50%
36%
40%
30%
20%
10%
4%
0%
0
1
2
3
4 5 6
# of visits
Patients
7
8
9 10+
Visits
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Physician Visits - Primary Care
50%
40%
30%
18%
20%
10%
2%
0%
0
2
6
4
# of visits
Patients
8
10+
Visits
© 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved.
Reactions
• Expect a small number of individuals to have a
large number of visits to specialists;
however, we did not expect such concentration
of visits to primary care providers
© 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved.
Persistence of High Primary Care Use
1997 10+
1998 <10
PC visits
1998 10+
PC visits
Pediatrics
(n=152)
77.0%
23.0%
Adult
(n=867)
82.2%
17.8%
© 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved.
High Primary Care Use
• A large percentage of primary care use may be
incurred by patients seeking help on nonmedical issues (Lundin, 2001; Sweden)
© 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved.
Dr. Baird’s Questions
Do we have people who are “over-serviced”, but
“under-served”?
Can we predict who they might be (and possibly
intervene)?
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Study Population
• 54,074 eligible patients with research
authorization
• 6% of population excluded due to HIPAA and
Minnesota regulations
• Outpatient office visits: 1997-1999
• Primary care:
• Family medicine
• General internal medicine
• General pediatrics
• Obstetrics
© 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved.
Methods
• Identify factors associated with “persistent,
high” primary care use:
• 10+ visits in two consecutive years
• Develop logistic model on 1997-1998 data
• Confirm model on 1998-1999 data
© 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved.
High Users 1997
n = 929
High Users 1998
Recurrent high
use in 1998
n=163
Not eligible
in 1998
n = 58
New high users in
1998
n = 947
n = 987
Not eligible in
1999
n = 10
n = 1120
© 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved.
Potential Risk Factors
• Age
• Gender
• Diagnoses
• Employee/dependent status
• (During timeframe: no copays, deductibles)
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Clinical Risk Factors
• Adjusted Clinical Groups – Johns Hopkins
• Based on all diagnoses for patient in year
• Clinically meaningful
• Developed by medical experts in primary care
• Predictive of utilization and resource costs
© 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved.
Going from Diagnosis Codes to ACGs
Diagnosis Codes
Adjusted Diagnosis Groups (ADGs): 32
Age, Gender
(ACGs)-Adjusted Clinical Groups
©1998 ©
The
Johns
Hopkins
University
School
of Hygiene
andrights
Public
Health
2004
– Mayo
College
of Medicine,
Mayo
Clinic. All
reserved.
Illustrative ACG Decision Tree
Entire Population
ACG X
ACG Y
Assignment is based on age,
gender, ADGs, and optionally,
delivery status and birthweight
ACG Z
There are actually around 106
ACGs
©1998 The
University
School Mayo
of Hygiene
© Johns
2004 –Hopkins
Mayo College
of Medicine,
Clinic.and
AllPublic
rights Health
reserved.
• To better understand what factors may be
important in predicting primary care visits,
we used the ADGs as our clinical risk factor
© 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved.
Model Results – Overall: Development
33
ADG
Pregnancy
Odds Ratio
0.17
95% CI
(0.10,0.28)
Score
-4
11
Chronic Med: Unstable
2.07
(1.37,3.12)
+2
30
See and Reassure
2.06
(1.24,3.41)
+2
26
Signs & Sympt: Minor
1.51
(1.02,2.22)
+1
23
Psychosocial: Time
Limited, Minor
1.56
(1.01,2.41)
+1
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% Persistent
Persistent High Primary Care Use by Model
Score
60
50
40
30
20
10
0
Overall
Adults
Peds
-4 to 2
-1
0
1
2
3
4
5+
Score
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Yield of Model Score - Adults
• Using a score of 1 or greater
• Sensitivity – 80.3% Specificity – 62.7%
• Using a score of 2 or greater
• Sensitivity – 50.3% Specificity – 81.2%
• Area under ROC curve – 0.794
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Yield of Model Score - Pediatrics
Prediction among pediatrics is not useful:
• score of 1 or greater
• Sensitivity - 78.3% Specificity - 29.9%
• score of 2 or greater
• Sensitivity - 33.3% Specificity - 75.1%
© 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved.
Persistence of High Primary Care Use –
Confirmatory Sample
1998 10+
1999 <10
PC visits
1999 10+
PC visits
Pediatrics
(n=237)
74.7%
25.3%
Adult
(n=873)
80.4%
19.6%
© 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved.
% Persistent
Comparison of Model Scores
1998 vs 1999
70
60
50
40
30
20
10
0
1998
1999
-4 to 2
-1
0
1
2
3
4
5+
Score
© 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved.
Yield of Model Score – Adults
Confirmatory Data
• Using a score of 1 or greater
• Sensitivity – 75.8% Specificity – 57.9%
• Using a score of 2 or greater
• Sensitivity – 49.8% Specificity – 80.0%
• Area under ROC curve – 0.752
• New persistent – 0.713 Recurrent – 0.594
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Discussion
• Unstable chronic medical conditions were
predictive of continued high use.
• Good candidates for disease management.
© 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved.
Discussion 2
• Time-limited minor psychosocial conditions,
minor signs and symptoms, and see and reassure
conditions were also predictive.
• These “over-serviced, under-served” may
benefit from alternative social support
services or integrated consultations with
primary care providers to better address
patient needs through non-medical
approaches.
© 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved.
Discussion 3
• Scoring model was able to consistently identify a
sizeable portion of the persistent high users, but
not effective among pediatric patients.
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Limitations
• Single group of covered employees and
dependents in small urban setting in a
Midwestern state.
• Fee-for-service coverage with no co-payments,
co-insurance or deductibles at time of study.
• Limited risk factors considered.
© 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved.
Further Research
• Family Practice team is evaluating “reflective
interviews” and integrated consultations among
patients with high primary care use.
• Need to evaluate cost effectiveness of proposed
interventions.
© 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved.