BACKGROUND: Linear programming is an analytic method that can be used to develop models for health care that optimize distribution of resources through mathematical means. STUDY DESIGN: The linear programming model contained objective, decision, and constraint elements. The objective was to optimize financial outcomes for both the hospital and physicians in the Department of Surgery. The decision concerns procedure mix or the number of each type of surgical procedure. Constraints apply to resources that are consumed during the course of the patient's surgical encounter. RESULTS: The optimal solution produced an increase in professional payments of 3.6% and an increase in hospital total margin of 16.1%. This solution favored surgical procedures that require inpatient care; these patients had greater comorbidity, reflected in a higher case-mix index of 3.74 compared to 2.97. Substantial differences were noted in use of general care and ICU days, and in consumption of preoperative, intraoperative, and recovery room time. CONCLUSIONS: Aligning quality surgical care with optimal financial performance may be assisted by mathematical models such as linear programming.
BACKGROUND: Linear programming is an analytic method that can be used to develop models for health care that optimize distribution of resources through mathematical means. STUDY DESIGN: The linear programming model contained objective, decision, and constraint elements. The objective was to optimize financial outcomes for both the hospital and physicians in the Department of Surgery. The decision concerns procedure mix or the number of each type of surgical procedure. Constraints apply to resources that are consumed during the course of the patient's surgical encounter. RESULTS: The optimal solution produced an increase in professional payments of 3.6% and an increase in hospital total margin of 16.1%. This solution favored surgical procedures that require inpatient care; these patients had greater comorbidity, reflected in a higher case-mix index of 3.74 compared to 2.97. Substantial differences were noted in use of general care and ICU days, and in consumption of preoperative, intraoperative, and recovery room time. CONCLUSIONS: Aligning quality surgical care with optimal financial performance may be assisted by mathematical models such as linear programming.