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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
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)
Focus of Identification • Total health care spending • Case management • Hospitalization • Disease management
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
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)
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)?
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
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
Potential Risk Factors • Age • Gender • Diagnoses • Employee/dependent status • (During timeframe: no copays, deductibles)
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
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 and Public Health
Illustrative ACG Decision Tree Entire Population ACG X ACG Y ACG Z Assignment is based on age, gender, ADGs, and optionally, delivery status and birthweight There are actually around 106 ACGs ©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
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
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%
Yield of Model Score – AdultsConfirmatory 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
Discussion • Unstable chronic medical conditions were predictive of continued high use. • Good candidates for disease management.
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.
Discussion 3 • Scoring model was able to consistently identify a sizeable portion of the persistent high users, but not effective among pediatric patients.
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.
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.