I’m coordinating a post-implementation evaluation of a new digital health record that now has 600 active users. My goal is to understand their acceptance of the system and the factors that drive—or hinder—perceived usability. The plan is to gather fresh data through two structured approaches: an online survey distributed to the full user base and a series of short, semi-structured interviews with a representative subset. Once data collection is complete, I need the results fed into a predictive analytics workflow to surface the strongest determinants of acceptance (think logistic or multinomial regression, decision trees, random forests—whatever techniques best fit the data). A rapid turnaround is important because executive stakeholders want an evidence-based improvement roadmap as soon as possible. Clean methodology, transparent code (Python, R, or SPSS/SAS), and a concise write-up that a non-statistical audience can digest will be essential. Deliverables • Finalised survey and interview guides ready for deployment • Cleaned, de-identified dataset in CSV or Excel • Reproducible analysis script with comments • Summary report (≈10 pages) highlighting key predictors, visualisations, and actionable recommendations • Optional slide deck for leadership briefings If you have a track record in health informatics research and can dive in immediately, I’m ready to share the current project documentation and schedule the kickoff.