CPHIMS Data Analytics and Reporting Questions and Answers Flashcards

Which approach to healthcare reporting delivers information to decision-makers at scheduled intervals without requiring them to query the system?

Answer: Push reporting (scheduled distribution)

Push reporting automatically delivers pre-built reports to stakeholders on a scheduled basis, ensuring leadership receives key metrics without having to actively request them.

Natural Language Processing (NLP) in healthcare analytics is MOST valuable for:

Answer: Extracting structured information from unstructured clinical narrative text

NLP enables systems to parse unstructured clinical notes, discharge summaries, and radiology reports to extract coded, analyzable data elements.

A hospital's population health management program relies MOST on which type of data integration?

Answer: Aggregation of clinical, claims, social determinants, and patient-generated data across the care continuum

Population health management requires aggregating diverse data—clinical, claims, social determinants, and patient-generated—to identify and manage health needs across a defined population.

When building a healthcare analytics report, stratifying patient outcomes by age, gender, and race PRIMARILY supports:

Answer: Health equity analysis to identify and address disparate outcomes

Stratifying outcomes by demographic factors allows organizations to identify disparities in care and take targeted action to promote health equity.

In a healthcare data governance program, a 'data steward' is PRIMARILY responsible for:

Answer: Managing the quality, definitions, and appropriate use of data within a specific domain

A data steward is a domain expert accountable for maintaining data quality, definitions, and appropriate usage standards within their area (e.g., clinical, financial, or operational data).

Which metric is MOST commonly used to evaluate the performance of a predictive model in healthcare analytics?

Answer: Area Under the ROC Curve (AUC-ROC)

AUC-ROC measures a classification model's ability to distinguish between positive and negative cases across all decision thresholds, making it a standard model performance metric.