Table 5.
PCP-patient selection on observables.
| Utilization outcome: | Δi,y | Utilization outcome: | Δi,y |
|---|---|---|---|
| Predicted primary care $ | −0.031*** (0.003) | Predicted # primary care office visits | −0.040*** (0.003) |
| Predicted all physician $ | −0.010*** (0.002) | Predicted # all office visits | −0.016*** (0.002) |
| Predicted pharma drug $ | 0.014*** (0.002) | Predicted # diagnoses | 0.018*** (0.003) |
| Predicted outpatient $ | 0.016*** (0.002) | Predicted # chronic conditions | −0.003 (0.002) |
| Predicted inpatient $ | 0.020*** (0.002) | Predicted # ED visits | 0.014*** (0.001) |
| Predicted post-acute care $ | 0.014*** (0.002) | Predicted # avoid hospitalizations | 0.014*** (0.001) |
| Predicted total $ | 0.019*** (0.002) | Predicted Pr(fluvaccine = 1) | −0.010*** (0.003) |
| Predicted Pr(diabetes care = 1) | −0.014*** (0.002) |
Notes: This table assesses the degree of potential selection into the new PCP-patient match based on observables. To combine all the information available in our data in a systematic way, we proceed as follows: for each utilization outcome yit, we begin by creating a prediction as a function of pre-exit patient characteristics, using all ofthe demographic variables and indicators for chronic and disabling conditions contained in the MBSF (which are listed in Table 1). We do not include the chronic and disabling conditions when predicting the number of diagnoses or the number of chronic conditions; instead, we only include patient characteristics from the top two panels of Table 1. Using these predictions, we then estimate regressions to test whether patients with higher predicted utilization for outcome y are more likely to switch to PCPs with higher intensity practice styles for outcome y. The coefficient estimates for those associations appear in the table. We control for HRR fixed effects and year fixed effects, and we bootstrap the standard errors.
p < 0.001
p < 0.01
p<0.05.