© 2004 – mayo college of medicine, mayo clinic. all rights reserved. predicting persistently high...

© 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved.

Predicting Persistently High Primary Care UsePredicting 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

James M. Naessens, MPH

Macaran A. Baird, MD, MS

Holly K. Van Houten, BA

David J. Vanness, PhD

Claudia R. Campbell, PhD


Identification of Costly PatientsIdentification 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)

• Many factors related to high use• Patient demographics• Certain diagnoses

• Chronic conditions• Disability

• Severity of disease • Prior use (health care and medications)


Focus of IdentificationFocus of Identification

• Total health care spending• Case management

• Hospitalization• Disease management

• Total health care spending• Case management

• Hospitalization• Disease management


Physician VisitsEmployee Health Plan, 1997Physician VisitsEmployee Health Plan, 1997

11%

44%

0%

10%

20%

30%

40%

50%

0 2 4 6 8 10+# of visits

Patients Visits

11%

44%

0%

10%

20%

30%

40%

50%

0 2 4 6 8 10+# of visits

Patients Visits


Physician Visits - Specialty CarePhysician Visits - Specialty Care

4%

36%

0%

10%

20%

30%

40%

50%

0 1 2 3 4 5 6 7 8 9 10+# of visits

Patients Visits

4%

36%

0%

10%

20%

30%

40%

50%

0 1 2 3 4 5 6 7 8 9 10+# of visits

Patients Visits


Physician Visits - Primary CarePhysician Visits - Primary Care

2%

18%

0%

10%

20%

30%

40%

50%

0 2 4 6 8 10+# of visits

Patients Visits

2%

18%

0%

10%

20%

30%

40%

50%

0 2 4 6 8 10+# of visits

Patients Visits


ReactionsReactions

• 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

• 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


Persistence of High Primary Care UsePersistence 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%

1997 10+ 1998 <10 PC visits

1998 10+ PC visits

Pediatrics (n=152)

77.0% 23.0%

Adult (n=867)

82.2% 17.8%


High Primary Care UseHigh Primary Care Use

• A large percentage of primary care use may be

incurred by patients seeking help on non-

medical issues (Lundin, 2001; Sweden)

• A large percentage of primary care use may be

incurred by patients seeking help on non-

medical issues (Lundin, 2001; Sweden)


Dr. Baird’s QuestionsDr. Baird’s Questions

Do we have people who are “over-serviced”, but

“under-served”?

Can we predict who they might be (and possibly

intervene)?

Do we have people who are “over-serviced”, but

“under-served”?

Can we predict who they might be (and possibly

intervene)?


Study PopulationStudy 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

• 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


MethodsMethods

• 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

• 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


High Users 1997 High Users 1998

n = 987

n = 1120

Recurrent high use in 1998

n=163

Not eligible in 1999

n = 10

New high users in 1998

n = 947

n = 929

Not eligible in 1998 n = 58


Potential Risk FactorsPotential Risk Factors

• Age

• Gender

• Diagnoses

• Employee/dependent status

• (During timeframe: no copays, deductibles)

• Age

• Gender

• Diagnoses

• Employee/dependent status

• (During timeframe: no copays, deductibles)


Clinical Risk FactorsClinical 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

• 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


Going from Diagnosis Codes to ACGsGoing from Diagnosis Codes to ACGsDiagnosis Codes

Adjusted Diagnosis Groups (ADGs): 32

(ACGs)-Adjusted Clinical Groups

Age, Gender

©1998 The Johns Hopkins University School of Hygiene and Public Health


Illustrative ACG Decision TreeIllustrative ACG Decision Tree

Assignment is based on age, gender, ADGs, and optionally, delivery status and birthweight

There are actually around 106 ACGs

Entire Population

ACG X ACG Y ACG Z

©1998 The Johns Hopkins University School of Hygiene and Public Health


• To better understand what factors may be

important in predicting primary care visits,

we used the ADGs as our clinical risk factor

• To better understand what factors may be

important in predicting primary care visits,

we used the ADGs as our clinical risk factor


Model Results – Overall: DevelopmentModel Results – Overall: Development

ADG Odds Ratio 95% CI Score 33 Pregnancy 0.17 (0.10,0.28) -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

ADG Odds Ratio 95% CI Score 33 Pregnancy 0.17 (0.10,0.28) -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


Persistent High Primary Care Use by Model Score Persistent High Primary Care Use by Model Score

0102030

405060

-4 to -2

-1 0 1 2 3 4 5+

Score

% P

ersi

sten

t

OverallAdultsPeds

0102030

405060

-4 to -2

-1 0 1 2 3 4 5+

Score

% P

ersi

sten

t

OverallAdultsPeds


Yield of Model Score - AdultsYield of Model Score - Adults

• Using a score of 1 or greater• Sensitivity – 80.3% Specificity – 62.7%


• Area under ROC curve – 0.794



• Area under ROC curve – 0.794


Yield of Model Score - PediatricsYield of Model Score - Pediatrics

Prediction among pediatrics is not useful:

• score of 1 or greater• Sensitivity - 78.3% Specificity - 29.9%


Prediction among pediatrics is not useful:




Persistence of High Primary Care Use – Confirmatory SamplePersistence 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%

1998 10+ 1999 <10 PC visits

1999 10+ PC visits

Pediatrics (n=237)

74.7% 25.3%

Adult (n=873)

80.4% 19.6%


Comparison of Model Scores1998 vs 1999Comparison of Model Scores1998 vs 1999

010203040506070

-4 to -2

-1 0 1 2 3 4 5+

Score

% P

ersi

sten

t

19981999

010203040506070

-4 to -2

-1 0 1 2 3 4 5+

Score

% P

ersi

sten

t

19981999


Yield of Model Score – AdultsConfirmatory DataYield of Model Score – AdultsConfirmatory Data• Using a score of 1 or greater

• Sensitivity – 75.8% Specificity – 57.9%


• Area under ROC curve – 0.752• New persistent – 0.713 Recurrent – 0.594



• Area under ROC curve – 0.752• New persistent – 0.713 Recurrent – 0.594


DiscussionDiscussion

• Unstable chronic medical conditions were predictive of continued high use.• Good candidates for disease management.

• Unstable chronic medical conditions were predictive of continued high use.• Good candidates for disease management.


Discussion 2Discussion 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.

• 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.


Discussion 3Discussion 3

• Scoring model was able to consistently identify a sizeable portion of the persistent high users, but not effective among pediatric patients.

• Scoring model was able to consistently identify a sizeable portion of the persistent high users, but not effective among pediatric patients.


LimitationsLimitations

• 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.

• 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.


Further ResearchFurther 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.

• 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. predicting persistently high...

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