Multivariate Risk Adjustment of Primary Care Patient Panels in a Public Health Setting: A Comparison of Statistical Models.

We compared prospective risk adjustment models for adjusting patient panels at the San Francisco Department of Public Health. We used 4 statistical models (linear regression, two-part model, zero-inflated Poisson, and zero-inflated negative binomial) and 4 subsets of predictor variables (age/gender categories, chronic diagnoses, homelessness, and a loss to follow-up indicator) to predict primary care visit frequency. Predicted visit frequency was then used to calculate patient weights and adjusted panel sizes.

Assessing and increasing patient panel size in the public sector.

Context: Panel management is a central component of the primary care medical home, but faces numerous challenges in the safety net setting. In the San Francisco Department of Public Health, many of our community-based primary care clinics have difficulty accommodating all patients seeking care.

Objective: We evaluated patient panel size in our 7 clinics providing cradle-to-grave primary care services to more than 25,000 active patients.

Creating a medical home in the San Francisco department of public health: establishing patient panels.

Patients with a medical home tend to fare better. One of the first steps toward establishing a medical home is to create panels by designating a clinic responsible for each patient. In 2006, we defined active clinic panels (all patients assigned to a clinic and seen there for one or more outpatient medical visits during the past 2 years) for the San Francisco Department of Public Health's 13 community- and four public hospital-based primary care clinics and began automatically assigning previously unassigned patients to clinics based on utilization.