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Predicting persistently high primary care use.

James M Naessens1, Macaran A Baird, Holly K Van Houten

  • 1Division of Health Care Policy & Research, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN 55905, USA. naessens.james@mayo.edu

Annals of Family Medicine
|July 28, 2005
PubMed
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Persistently high primary care use is linked to specific adult health conditions, not typically resolved by medical visits alone. Some patients may need psychosocial support or disease management interventions.

Area of Science:

  • Health Services Research
  • Primary Care Medicine
  • Health Economics

Background:

  • Understanding drivers of high primary care utilization is crucial for resource allocation and patient care optimization.
  • Fee-for-service models without co-payments or referral requirements may influence utilization patterns.

Purpose of the Study:

  • To identify risk factors associated with persistently high primary care visit frequency.
  • To develop a predictive model for identifying patients likely to maintain high primary care use.

Main Methods:

  • Analysis of outpatient visits from 1997-1999 in a Midwestern city.
  • Logistic regression used to predict repeat high users (≥10 visits) based on demographic and diagnostic categories (Adjusted Clinical Groups - ACGs).
  • Model validated using a confirmatory dataset.

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Main Results:

  • Two percent of patients had ≥10 visits in 1997, with nearly 19% repeating high use the following year.
  • For adults, four Ambulatory Diagnosis Groups (ADGs) predicted repeated high use: unstable chronic conditions, 'see and reassure' conditions, minor psychosocial issues, and minor symptoms.
  • Pregnancy was negatively associated with high use; a predictive model for pediatric patients was not satisfactory.

Conclusions:

  • Persistently high primary care users may be overserviced yet underserved, with underlying issues not fully addressed by medical care.
  • Interventions such as psychosocial support or targeted disease management may benefit specific patient subgroups.
  • The predictive model demonstrated moderate accuracy for adults (ROC curves 0.794 and 0.752).