医院药房在供应链中断的情况下适应需求中断的好处（或坏处）。
IF 2.3
3区 医学
Q2 HEALTH POLICY & SERVICES
引用次数: 0
摘要
供应链中断和需求中断使医院药房经理在确定库存量时面临挑战。库存不足会导致药品短缺，而库存过剩则会造成药品浪费。为了减少药品短缺和浪费，医院药房经理可以实施考虑到供应链中断的库存政策，并随着时间的推移调整这些库存政策，以应对需求中断。在 Covid-19 大流行期间，需求中断现象十分普遍。然而，药品短缺与浪费的权重（即对短缺的关注与对浪费的关注）以及供应链中断的持续时间和间隔时间如何影响适应需求中断的益处（或害处），目前仍不清楚。我们开发了一个自适应库存系统（即库存政策随时间而改变），并利用密歇根大学中央药房的实际需求数据进行了广泛的数值分析，以解决这一研究问题。在供应链中断的平均持续时间和平均间隔时间固定的情况下，我们发现药品的短缺-浪费权重决定了适应性库存政策的收益（或损失）大小。我们创建了一个排序程序，该程序提供了一种辨别哪些药品最值得关注的方法，并说明了在可更新的库存政策数量有限的情况下，应更新哪些政策。将我们的框架应用于 300 多种药物时，我们发现决策者只需在任何时间点更新很小一部分药物（例如 5%），就能从适应性库存政策中获得最大收益。本文章由计算机程序翻译，如有差异，请以英文原文为准。
The benefits (or detriments) of adapting to demand disruptions in a hospital pharmacy with supply chain disruptions.
Supply chain disruptions and demand disruptions make it challenging for hospital pharmacy managers to determine how much inventory to have on-hand. Having insufficient inventory leads to drug shortages, while having excess inventory leads to drug waste. To mitigate drug shortages and waste, hospital pharmacy managers can implement inventory policies that account for supply chain disruptions and adapt these inventory policies over time to respond to demand disruptions. Demand disruptions were prevalent during the Covid-19 pandemic. However, it remains unclear how a drug's shortage-waste weighting (i.e., concern for shortages versus concern for waste) as well as the duration of and time between supply chain disruptions influence the benefits (or detriments) of adapting to demand disruptions. We develop an adaptive inventory system (i.e., inventory policies change over time) and conduct an extensive numerical analysis using real-world demand data from the University of Michigan's Central Pharmacy to address this research question. For a fixed mean duration of and mean time between supply chain disruptions, we find a drug's shortage-waste weighting dictates the magnitude of the benefits (or detriments) of adaptive inventory policies. We create a ranking procedure that provides a way of discerning which drugs are of most concern and illustrates which policies to update given that a limited number of inventory policies can be updated. When applying our framework to over 300 drugs, we find a decision-maker needs to update a very small proportion of drugs (e.g., ) at any point in time to get the greatest benefits of adaptive inventory policies.
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来源期刊
Health Care Management Science
HEALTH POLICY & SERVICES-
CiteScore
7.20
自引率
5.60%
发文量
40
期刊介绍：
Health Care Management Science publishes papers dealing with health care delivery, health care management, and health care policy. Papers should have a decision focus and make use of quantitative methods including management science, operations research, analytics, machine learning, and other emerging areas. Articles must clearly articulate the relevance and the realized or potential impact of the work. Applied research will be considered and is of particular interest if there is evidence that it was implemented or informed a decision-making process. Papers describing routine applications of known methods are discouraged.
Authors are encouraged to disclose all data and analyses thereof, and to provide computational code when appropriate.
Editorial statements for the individual departments are provided below.
Health Care Analytics
Departmental Editors:
Margrét Bjarnadóttir, University of Maryland
Nan Kong, Purdue University
With the explosion in computing power and available data, we have seen fast changes in the analytics applied in the healthcare space. The Health Care Analytics department welcomes papers applying a broad range of analytical approaches, including those rooted in machine learning, survival analysis, and complex event analysis, that allow healthcare professionals to find opportunities for improvement in health system management, patient engagement, spending, and diagnosis. We especially encourage papers that combine predictive and prescriptive analytics to improve decision making and health care outcomes.
The contribution of papers can be across multiple dimensions including new methodology, novel modeling techniques and health care through real-world cohort studies. Papers that are methodologically focused need in addition to show practical relevance. Similarly papers that are application focused should clearly demonstrate improvements over the status quo and available approaches by applying rigorous analytics.
Health Care Operations Management
Departmental Editors:
Nilay Tanik Argon, University of North Carolina at Chapel Hill
Bob Batt, University of Wisconsin
The department invites high-quality papers on the design, control, and analysis of operations at healthcare systems. We seek papers on classical operations management issues (such as scheduling, routing, queuing, transportation, patient flow, and quality) as well as non-traditional problems driven by everchanging healthcare practice. Empirical, experimental, and analytical (model based) methodologies are all welcome. Papers may draw theory from across disciplines, and should provide insight into improving operations from the perspective of patients, service providers, organizations (municipal/government/industry), and/or society.
Health Care Management Science Practice
Departmental Editor:
Vikram Tiwari, Vanderbilt University Medical Center
The department seeks research from academicians and practitioners that highlights Management Science based solutions directly relevant to the practice of healthcare. Relevance is judged by the impact on practice, as well as the degree to which researchers engaged with practitioners in understanding the problem context and in developing the solution. Validity, that is, the extent to which the results presented do or would apply in practice is a key evaluation criterion. In addition to meeting the journal’s standards of originality and substantial contribution to knowledge creation, research that can be replicated in other organizations is encouraged. Papers describing unsuccessful applied research projects may be considered if there are generalizable learning points addressing why the project was unsuccessful.
Health Care Productivity Analysis
Departmental Editor:
Jonas Schreyögg, University of Hamburg
The department invites papers with rigorous methods and significant impact for policy and practice. Papers typically apply theory and techniques to measuring productivity in health care organizations and systems. The journal welcomes state-of-the-art parametric as well as non-parametric techniques such as data envelopment analysis, stochastic frontier analysis or partial frontier analysis. The contribution of papers can be manifold including new methodology, novel combination of existing methods or application of existing methods to new contexts. Empirical papers should produce results generalizable beyond a selected set of health care organizations. All papers should include a section on implications for management or policy to enhance productivity.
Public Health Policy and Medical Decision Making
Departmental Editors:
Ebru Bish, University of Alabama
Julie L. Higle, University of Southern California
The department invites high quality papers that use data-driven methods to address important problems that arise in public health policy and medical decision-making domains. We welcome submissions that develop and apply mathematical and computational models in support of data-driven and model-based analyses for these problems.
The Public Health Policy and Medical Decision-Making Department is particularly interested in papers that:
Study high-impact problems involving health policy, treatment planning and design, and clinical applications;
Develop original data-driven models, including those that integrate disease modeling with screening and/or treatment guidelines;
Use model-based analyses as decision making-tools to identify optimal solutions, insights, recommendations.
Articles must clearly articulate the relevance of the work to decision and/or policy makers and the potential impact on patients and/or society. Papers will include articulated contributions within the methodological domain, which may include modeling, analytical, or computational methodologies.
Emerging Topics
Departmental Editor:
Alec Morton, University of Strathclyde
Emerging Topics will handle papers which use innovative quantitative methods to shed light on frontier issues in healthcare management and policy. Such papers may deal with analytic challenges arising from novel health technologies or new organizational forms. Papers falling under this department may also deal with the analysis of new forms of data which are increasingly captured as health systems become more and more digitized.
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