Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania.
Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; American college of Physicians, Philadelphia, Pennsylvania.
Ann Surg. 2022 Jun 1;275(6):1085-1093. doi: 10.1097/SLA.0000000000004469. Epub 2020 Oct 19.
To model the financial impact of policies governing the scheduling of overlapping surgeries, and to identify optimal solutions that maximize operating efficiency that satisfy the fiduciary duty to patients.
Hospitals depend on procedural revenue to maintain financial health as the recent pandemic has revealed. Proposed policies governing the scheduling of overlapping surgeries may dramatically impact hospital revenue. To date, the potential financial impact has not been modeled.
A linear forecasting model based on a logic matrix decision tree enabled an analysis of surgeon productivity annualized over a fiscal year. The model applies procedural and operational variables to policy constraints limiting surgical scheduling. Model outputs included case and financial metrics modeled over 1000-surgeon-year simulations. case metrics included annual case volume, case mix, operating room (OR) utilization, surgeon utilization, idle time, and staff overtime hours. Financial outputs included annual revenue, expenses, and contribution margin.
The model was validated against surgical data. case and financial metrics decreased as a function of increasingly restrictive scheduling scenarios, with the greatest contribution margin loses ($1,650,000 per surgeon-year) realized with the introduction of policies mandating that a second patient could not enter the OR until the critical portion of the first surgery was completed. We identify an optimal scheduling scenario that maximizes surgeon efficiency, minimizes OR idle time and revenue loses, and satisfies ethical obligations to patients.
Hospitals may expect significant financial loses with the introduction of policies restricting OR scheduling. We identify an optimal solution that maximizes efficiency while satisfying ethical duty to patients. This forecast is immediately relevant to any hospital system that depends upon procedural revenue.
建立一个模型来模拟管理重叠手术排程的政策的财务影响，并确定能够最大限度地提高运营效率、同时满足对患者的受托责任的最优解决方案。
正如最近的大流行病所揭示的那样，医院依赖手术程序收入来维持财务健康。拟议的管理重叠手术排程的政策可能会对医院收入产生重大影响。迄今为止，尚未对潜在的财务影响进行建模。
基于逻辑矩阵决策树的线性预测模型使我们能够分析外科医生在一个财政年度内的年度生产力。该模型将手术程序和运营变量应用于限制手术排程的政策约束中。模型输出包括在 1000 名外科医生年模拟中建模的病例和财务指标。病例指标包括年度病例量、病例组合、手术室（OR）利用率、外科医生利用率、空闲时间和员工加班时间。财务输出包括年度收入、支出和贡献边际。
该模型通过手术数据进行了验证。病例和财务指标随着排程方案的限制越来越严格而呈下降趋势，在引入要求第二个患者必须在第一台手术的关键部分完成后才能进入 OR 的政策时，最大的贡献边际损失（每位外科医生每年 16.5 万美元）。我们确定了一个最优的排程方案，该方案可以最大限度地提高外科医生的效率，最小化 OR 空闲时间和收入损失，并满足对患者的道德义务。
医院可能会因限制 OR 排程的政策的引入而遭受重大财务损失。我们确定了一个最优的解决方案，该方案在满足对患者的道德责任的同时最大限度地提高效率。这个预测对任何依赖手术程序收入的医院系统都具有直接的相关性。
