1. © 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved. 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 James M. Naessens, MPH Macaran A. Baird, MD, MS Holly K. Van Houten, BA David J. Vanness, PhD Claudia R. Campbell, PhD

2. © 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) Many factors related to high use Patient demographics Certain diagnoses Chronic conditions Disability Severity of disease Prior use (health care and medications)

3. © 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved. Focus of Identification Total health care spending Case management Hospitalization Disease management Total health care spending Case management Hospitalization Disease management

4. © 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved. Physician Visits Employee Health Plan, 1997

5. © 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved. Physician Visits - Specialty Care

6. © 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved. Physician Visits - Primary Care

7. © 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

8. © 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved. Persistence of High Primary Care Use

9. © 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 non- medical issues (Lundin, 2001; Sweden)

10. © 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved. Dr. Bairds 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)?

11. © 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved. 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 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

12. © 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 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

13. © 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved.

14. Potential Risk Factors Age Gender Diagnoses Employee/dependent status (During timeframe: no copays, deductibles) Age Gender Diagnoses Employee/dependent status (During timeframe: no copays, deductibles)

15. © 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved. 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 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

16. © 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved. Going from Diagnosis Codes to ACGs Diagnosis Codes Adjusted Diagnosis Groups (ADGs): 32 (ACGs)-Adjusted Clinical Groups Age, Gender ©1998 The Johns Hopkins University School of Hygiene and Public Health

17. © 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved. Illustrative 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 XACG YACG Z ©1998 The Johns Hopkins University School of Hygiene and Public Health

18. © 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved. To better understand what factors may be important in predicting primary care visits, we used the ADGs as our clinical risk factor

19. © 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved. Model Results – Overall: Development

20. © 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved. Persistent High Primary Care Use by Model Score

21. © 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved. 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 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

22. © 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved. 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% 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%

23. © 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved. Persistence of High Primary Care Use – Confirmatory Sample

24. © 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved. Comparison of Model Scores 1998 vs 1999

25. © 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 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

26. © 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved. Discussion 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.

27. © 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. 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.

28. © 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.

29. © 2004 – Mayo College of Medicine, Mayo Clinic. All rights reserved. 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. 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.

30. © 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. 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.