The question implies that the readmission data for the 2nd quarter has been reviewed, and specific units (e.g., 2 South and 3 North) likely show higher readmission rates, prompting the need to identify improvement opportunities. Health data analytics involves using data to identify trends, prioritize areas of concern, and drive targeted interventions. Further analysis of specific units with elevated readmissions is a logical next step to uncover root causes.

Option A (Take no further action because the data is not definitive): This option is inappropriate, as quality professionals are expected to act on data trends, even if preliminary, to drive improvement. Waiting for “definitive” data risks delaying interventions and contradicts CPHQ principles of proactive quality management.

Option B (Use a scattergram to look for an association between readmissions and unit): A scattergram (or scatter plot) is useful for exploring correlations between two variables (e.g., readmissions vs. staffing levels). However, the question suggests the data already highlights specific units (2 South and 3 North), so a scattergram is less relevant than analyzing those units directly.

Option C (Further analyze 2 South and 3 North to determine possible causes): This is the most appropriate action, as it focuses on the units with likely higher readmission rates. According to NAHQ study materials, quality professionals should use data to drill down into specific areas of concern, applying tools like root cause analysis or process mapping to identify underlying causes. This aligns with the CPHQ domain of Health Data Analytics, which emphasizes targeted data analysis to drive improvement.

Option D (Meet with the Quality Council to share the results for 4 North and 4 South): The question does not indicate that 4 North and 4 South are the units with high readmissions. Focusing on these units without evidence is premature, and meeting with the Quality Council is a later step after causes are identified.