Healthcare Analyst Data Analysis & Interpretation Flashcards

A healthcare analyst calculates a 95% confidence interval for average ED wait time as [42 min, 58 min]. Which statement is the correct interpretation?

Answer: If the study were repeated many times, 95% of such intervals would contain the true mean

A confidence interval is a frequentist construct meaning 95% of intervals built from repeated samples would capture the true population parameter.

An analyst is building a predictive model for hospital-acquired infections and wants to minimize false negatives (missed infections). Which metric should be prioritized?

Answer: Sensitivity (recall)

Sensitivity measures the proportion of true positive cases correctly identified, making it critical when missing a case has serious consequences.

Which visualization is most appropriate for showing the distribution of patient age across four different diagnosis-related groups (DRGs)?

Answer: Side-by-side box plots

Side-by-side box plots display the distribution, median, spread, and outliers for each group simultaneously, enabling direct comparison.

A healthcare analyst wants to assess whether a new care coordination program reduced average LOS after controlling for patient acuity. The best study design to establish causality would be:

Answer: A randomized controlled trial assigning patients to program vs. usual care

Randomized controlled trials use random assignment to eliminate confounding, providing the strongest evidence of causality.

An analyst finds that surgical complication rates differ between hospitals after unadjusted analysis, but differences disappear after risk adjustment. This finding most likely indicates:

Answer: Patient case-mix differences were driving the unadjusted disparity

When unadjusted differences vanish after risk adjustment, it suggests the apparent disparity was due to differences in patient populations (case mix), not true performance differences.

In the context of healthcare quality measurement, which statistical concept explains why hospitals with very few cases often appear to have extremely high or low complication rates?

Answer: Regression to the mean and small-sample instability

Small sample sizes produce high variance in rate estimates, making extreme values more likely by chance — a manifestation of small-sample instability and regression to the mean.

A healthcare analyst is using ICD-10 diagnosis codes to identify diabetic patients. Including only 'E11' (Type 2 diabetes) codes but missing 'E10' (Type 1) and combination codes would primarily affect which data quality dimension?

Answer: Completeness

Missing valid diabetic codes means the dataset does not capture all relevant cases, directly compromising completeness.