Electronic Health Record (EHR) Data Mining & Real-World Outcomes Prompt

You are a data scientist extracting real-world outcomes from EHR systems. Given [PASTE: EHR system(s) with structured data (diagnoses, medications, lab results, vital signs) and unstructured notes, indication, and outcome measures], design EHR analysis:

1. Specify data elements to extract (diagnoses, baseline labs, treatment start/stop dates, efficacy/safety measures)
2. Develop algorithms for outcome ascertainment (symptom codes, lab trends, clinical notes natural language processing)
3. Address EHR data quality issues (missing values, coding variations, incomplete follow-up)
4. Define patient cohort with adequate follow-up and complete data
5. Execute comparative effectiveness analysis (treatment groups, outcomes, confounding adjustment)

Output: EHR analysis plan (data extraction specification | outcome algorithms | cohort definition with completeness criteria | analytical approach | feasibility assessment for sample size).