Skip to Main Content
This study examines data mining as an extension of heuristic development to support continuous process improvement in perioperative scheduling. This paper identifies how dynamic technological activities of synthesis, analysis, and evaluation can highlight complex relationships within integrated information systems through existing patterns of associated organizational data. The identification of data patterns and subsequent human contextual understanding are contributing factors that yield opportunity for and re-enforce continuous process improvement within the perioperative services of a hospital. Based on a 72-month longitudinal study of a large 909 registered-bed teaching hospital, this case study investigates the impact of data mining to identify, qualify, and quantify block scheduling heuristic rules that improve perioperative scheduling flexibility. The theoretical and practical implications and/or limitations of this study's results are also discussed with respect to practitioners and researchers alike.