Decisions around pregnancy are personal, particularly when it comes to how a woman hopes to deliver her child. Some women prefer a vaginal birth, while others may prefer to have a C-section. Whatever the situation, Amino aims to give people information about how doctors vary in their practice, so we’ve calculated C-section rates for doctors across the country.
To help you understand how your doctor’s C-section rate compares to a typical doctor who delivers babies:
- We first measure the actual C-section rate for the pregnant patients your doctor treats.
- We then analyze the typical practices of all other doctors in the United States who deliver babies, using a data-driven model to predict the C-section rate for patients like the ones who see your doctor.
By comparing these two numbers, you can see whether your doctor’s C-section rate is lower than, similar to, or higher than predicted.
This prediction adjusts based on the types of patients each doctor sees, so a doctor who treats sicker patients may have a different predicted rate than a doctor who treats healthier patients.
Methodology Deep Dive
Amino compares the predicted rate of a procedure to the actual rate measured for a specific medical professional. Our predictive model incorporates data about every patient in our database who received the procedure, including the patients’ demographic, diagnostic, and place of care data. Therefore, we are able to calculate a risk-adjusted decision factor analysis on a per-provider basis.
To calculate this analysis for an individual doctor, we first pass data about that doctor’s patients to a model for prediction. The model produces an overall prediction of the decision factor rate we would expect to see for these patients if they went to a typical doctor in the US. The model that we use is a multivariate logistic regression, where the patient and facility information are features in the model used to predict the decision factor of interest.
It is important to note that we do not compare an individual doctor’s rate to the overall average rate for all doctors, as this would fail to account for the types of patients the doctor sees and how sick those patients are. Instead, we pass information like age, sex, and diagnosis to the statistical model so that these risk factors may be adjusted for in the prediction. This means that doctors who have higher-risk or sicker patients are not unfairly penalized in our analysis.
For each doctor about whom we report a decision factor, we compare our predicted rate to the actual rate measured and classify the doctor’s rate as lower than, similar to, or higher than predicted. We give a doctor a label of high or low if their predicted rate of C-section falls outside the 95% confidence interval around their actual rate of C-section.
In addition, we do not report a decision factors analysis for a doctor if we do not have enough data to be confident in our assessment. Our lower limit is 13 deliveries for the c-section decision factor.
The data we use to build our logistic regression model is determined by the health condition or medical procedure we are analyzing, and we use clinical expertise to identify the specific procedure codes relevant to what we are measuring.
In all our data analyses, Amino strives to responsibly present facts that help everyone make decisions about their care.