Objective The purpose of this study was to utilize a multicenter dataset to elucidate whether socioeconomic factors were associated with access to cleft lip surgery, treatment by higher-volume providers, and family choice for higher-volume centers. Design Retrospective cohort study. Setting Hospitals participating in the Pediatric Health Information System. Patients Primary cleft lip repair performed in the United States between 2010 and 2020. Outcomes Travel distance, hospital volume, hospital choice. Results During the study interval, 8954 patients underwent unilateral (78.4%, n = 7021) or bilateral (21.6%, n = 1933) primary cleft lip repair. Patients with unilateral cleft lip were repaired significantly earlier if they were White ( P < .001) and significantly later if they lived in an urban community ( P = .043). Similarly, patients with bilateral cleft lip were repaired significantly earlier if they were White ( P < .001). Patients from above-median income households ( P = .011) and living in urban communities ( P < .001) were significantly more likely to be treated at high-volume hospitals, whereas those living in underserved communities ( P < .001) were significantly less likely to be treated at high-volume hospitals. White patients were significantly more likely to be treated by high-volume surgeons ( P < .001). Patients with White race were significantly more likely to choose a higher-volume hospital than the one most locally available ( P < .001). Conclusions Patients with White race are more likely to travel farther and be treated by high-volume surgeons although at smaller hospitals. Patients from underserved areas travel significantly farther for cleft care and are treated at lower-volume hospitals. Patients in urban communities have shorter travel distances and are treated at higher-volume hospitals.
Background: As the cost of health care continues to rise, the role of medical providers has evolved to include the duties of an operations manager. Two theories of operations management can be readily applied to health care—lean management, the process of identifying and eliminating waste; and Little’s law, the idea that throughput is maximized by changing the capacity to host patients or the time they spend in the system. Equipped with theories of operations management, providers are better able to identify and address flow limitations in their own practices. Methods: Operations flow data were collected from three areas of care—clinic, surgical booking, and the operating room—for one provider. Variables of interest included visit or procedure characteristics and operations flow characteristics, such as different time points involved in the sector of care. Results: Clinic data were collected from 48 patients. Variables with a significant relationship to total clinic visit time included afternoon appointments (p = 0.0080) and visit type (p = 0.0114). Surgical booking data were collected for 127 patients. Shorter estimated procedure length (p = 0.0211) decreased time to surgery. Operating room data were collected for 65 cases. Variables with a significant relationship to total operating room time were patient age (p = 0.0325), Charlson Comorbidity Index (p = 0.0039), flap type (p = 0.0153), and number of flaps (p < 0.0001). Conclusions: This brief single-provider study provides examples of how to apply operations management theories to each point of care within one’s own practice. Although longitudinal data following patients through each point of care are the next step in operations flow analysis, this work lays the foundation for evaluation at each time point with the goal of developing practical strategies to improve throughput in one’s practice.
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