Healthcare Analytics Predictive Analytics & Trends Flashcards

A predictive model for diabetes complications shows high AUC-ROC (0.92) but performs poorly in a specific ethnic subgroup. This illustrates:

Answer: Algorithmic bias and health equity concerns

Strong overall AUC can mask poor performance in subgroups, revealing algorithmic bias that raises health equity concerns in clinical deployment.

Which technique reduces the complexity of a predictive model by eliminating features that contribute little to predictive power, improving generalizability?

Answer: Regularization

Regularization (L1/L2) penalizes model complexity by shrinking or eliminating low-importance feature coefficients, reducing overfitting.

In predictive healthcare analytics, what is 'survival analysis' primarily used to model?

Answer: Time until a clinical event occurs

Survival analysis models the time until an event (such as death, readmission, or disease progression) occurs, handling censored data from patients who haven't yet experienced the event.

Which of the following best describes the concept of 'precision medicine' as it relates to predictive analytics trends?

Answer: Tailoring interventions based on individual patient data including genomics and lifestyle

Precision medicine leverages patient-specific data—including genomic, environmental, and lifestyle factors—to personalize prediction and treatment decisions.

When a hospital's predictive model for hospital-acquired pneumonia is retrained monthly with new data, this practice is called:

Answer: Continuous model retraining

Continuous model retraining updates the model regularly with new data to adapt to changing patient populations and clinical practices, maintaining performance.

A healthcare analytics team uses synthetic patient data generated by a GAN to augment a small training dataset. What is the primary benefit of this approach?

Answer: Increasing training data volume while preserving patient privacy

Generative Adversarial Networks (GANs) create realistic synthetic data that can expand small datasets without exposing real patient information.

Which analytics trend involves embedding predictive model outputs directly into EHR workflows to trigger real-time clinical decision support alerts?

Answer: Point-of-care predictive analytics integration

Point-of-care integration delivers predictive model scores and alerts within EHR workflows at the moment of clinical decision-making, enabling timely interventions.