Context: Utilization of operating theaters is a major cost driver in hospitals. Optimizing this variable through optimized surgery schedules may significantly reduce costs and simultaneously improve medical outcomes. Previous studies proposed various complex models to predict the duration of procedures, the key ingredient to optimal schedules. They did so perusing large amounts of data. Goals: We aspire to create an effective and efficient model to predict operation durations based on only a small amount of data. Ideally, our model is also simpler in structure, and thus easier to use. Methods: Following a mixed-methods approach, we immerse ourselves in the application domain to leverage practitioners expertise, to make the best use of our limited supply of clinical data, and to conduct our data analysis in a theory-guided way. We perform a combined factor analysis and develop regression models to predict the duration of the perioperative process. Findings: We found simple methods of central tendency to perform on a par with much more complex methods proposed in the literature. In fact, they sometimes outperform them. We conclude that combining expert knowledge with data analysis may improve both data quality and model performance, allowing for more accurate forecasts. Conclusion: We yield better results than previous researchers by integrating conventional data science methods with qualitative studies of clinical settings and process structure. Thus, we are able to leverage even small datasets.