Monitoring Perioperative Services Using 3D Multi-Objective Performance Frontiers

Elhajj, Andrea J.; Rizzo, Donna M.; An, Gary; Pandit, J. J.; Tsai, Mitchell H.

doi:10.1007/s10916-021-01713-y

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Previously, performance frontiers have been used to model the operational implications of changing release times, benchmark different anesthesia environments, assess the impact of Acute Care Surgery tactical allocation, and make recommendations concerning tactical decisions in operating room management [9,10]. Presumably, by visualizing OR management metrics along two axes, clinical directors and hospital administrative are able to compare before and after scenarios and make normative claims about their future operation [11,12].…”

Section: Introductionmentioning

confidence: 99%

Scaling performance frontiers across multiple perioperative services

Zhang,

Saffary,

Sharma

et al. 2024

Perioperative Care and Operating Room Management

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Section: Introductionmentioning

confidence: 99%

Scaling performance frontiers across multiple perioperative services

Zhang,

Saffary,

Sharma

et al. 2024

Perioperative Care and Operating Room Management

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“…Recently, Elhajj et al showed that multi-objective analyses can be implemented along >2 dimensions, thereby allowing organizations to adjust for multiple parameters and track operational implications throughout a perioperative system. 22–25…”

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confidence: 99%

Multivariable Cost Frontiers—Qualitative Financial Analyses Using Operational Metrics From the Implementation of a Surgery Fellowship

et al. 2023

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Objective: We expand the application of cost frontiers and introduce a novel approach using qualitative multivariable financial analyses. Summary Background Data: With the creation of a 5 + 2-year fellowship program in July 2016, the Division of Vascular Surgery at the University of Vermont Medical Center altered the underlying operational structure of its inpatient services. Method: Using WiseOR (Palo Alto, CA), a web-based OR management data system, we extracted the operating room metrics before and after August 1, 2016 service for each 4-week period spanning from September 2015 to July 2017. The cost per minute modeled after Childers et al’s inpatient OR cost guidelines was multiplied by the after-hours utilization to determine variable cost. Zones with corresponding cutoffs were used to graphically represent cost efficiency trends. Results: Caseload/FTE for attending surgeons increased from 11.54 cases per month to 13.02 cases per month (P = 0.0771). Monthly variable costs/FTE increased from $540.2 to $1873 (P = 0.0138). Monthly revenue/FTE increased from $61,505 to $70,277 (P = 0.2639). Adjusted monthly reve-nue/FTE increased from $60,965 to $68,403 (P = 0.3374). Average monthly percent of adjusted revenue/FTE lost to variable costs increased from 0.85% to 2.77% (P = 0.0078). Adjusted monthly revenue/case/FTE remained the same from $5309 to $5319 (P = 0.9889). Conclusion: In summary, we demonstrate that multivariable cost (or performance) frontiers can track a net increase in profitability associated with fellowship implementation despite diminishing returns at higher caseloads.

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Scaling Performance Frontiers Across Multiple Perioperative Services

Zhang,

Saffary,

Sharma

et al. 2023

Preprint

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Background: One of the primary principles governing operating room management includes maximizing clinical efficiency and optimizing the time used in the high-cost, high-revenue environments represented by operating rooms. Under-utilized and over-utilized times are elementary metrics that describe the operating room performance. Performance frontiers visualize the maximal efficiency of systems and their existing constraints. Methods: Monthly aggregated operating room metrics from services at the University of Vermont Medical Center (UVM), Stanford Hospital, and the University of Alabama (UAB) at Birmingham Hospital were extracted. Paired under- and over-utilized times were plotted against each other. Performance frontiers representing the optimal performance of each service were overlaid. Results: The Kolmogorov-Smirnov test for goodness-of-fit at 95% level of significance confirms that the performance frontiers representing UVM and Stanford (K-S = 0.9507, p < 0.0001), UVM and UAB (K-S = 0.9989, p < 0.0001), and Stanford and UAB (K-S = 0.9773, p < 0.0001), indicating each service is represented by a different performance frontier. Conclusions: Our analysis shows that the performance frontier defining the optimal efficiency of UVM is more efficient than that of Stanford and UAB. Differences in efficiency must be due in part to organizational differences between institutions, limited in scale due to the size of institutions; normative statements must be made in relation to the existing organizational structures of each institution and their specific capacity to make changes in tactical decisions. Systemic interventions should be implemented via qualitative analysis of more efficient services, defined by the relative positioning of relevant performance frontiers.

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Monitoring Perioperative Services Using 3D Multi-Objective Performance Frontiers

Cited by 3 publications

References 17 publications

Scaling performance frontiers across multiple perioperative services

Scaling performance frontiers across multiple perioperative services

Multivariable Cost Frontiers—Qualitative Financial Analyses Using Operational Metrics From the Implementation of a Surgery Fellowship

Scaling Performance Frontiers Across Multiple Perioperative Services

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