调整成本是否制约了公共医疗服务提供者的技术效率？来自新西兰公共医疗系统的证据。
IF 2.3
3区 医学
Q2 HEALTH POLICY & SERVICES
Health Care Management Science
Pub Date : 2024-06-01
Epub Date: 2024-03-11
DOI:10.1007/s10729-024-09668-5
引用次数: 0
摘要
效率分析对于优化资源配置和提高患者疗效至关重要。然而，投入的及时调整可能会受到调整成本的阻碍，从而影响长期技术效率（LRTE）。为弥补医疗文献中的这一空白，本研究采用贝叶斯动态随机前沿模型来估计参数，并探索医疗效率随时间变化的动态变化。研究显示，新西兰地区卫生局（DHBs）的 LRTE 为 0.76，表明由于调整成本，投入利用率提高了约 32%。大多数区卫生局的短期运营效率保持一致，全国短期技术效率（SRTE）与 LRTE 非常接近。在三级医疗机构中，奥克兰和首都与海岸地区卫生局的运营效率低于 LRTE 水平，使其有别于其他三级医疗机构。同样，泰拉维蒂（Tairawhiti）和西海岸（West Coast）地区卫生局的 SRTE 分数也低于 LRTE 水平，这可能是受其独特的医疗环境和资源挑战的影响。这项研究通过纳入决策的时间动态并考虑调整成本，为政策讨论带来了新的视角。它强调了平衡短期和长期技术效率的必要性，突出了它们在促进新西兰可持续和高效医疗保健系统方面的共同重要性。本文章由计算机程序翻译，如有差异，请以英文原文为准。
Do adjustment costs constrain public healthcare providers' technical efficiency? Evidence from the New Zealand Public Healthcare System.
Efficiency analysis is crucial in healthcare to optimise resource allocation and enhance patient outcomes. However, the prompt adaptation of inputs can be hindered by adjustment costs, which impact Long-Run Technical Efficiency (LRTE). To bridge this gap in healthcare literature, this research employs a Bayesian Dynamic Stochastic Frontier Model to estimate parameters and explore healthcare efficiency dynamics over time. The study reveals the LRTE for New Zealand District Health Boards (DHBs) as 0.76, indicating around 32% more input utilisation due to adjustment costs. Most DHBs exhibit consistent short-run operational efficiency, with the national Short-Run Technical Efficiency (SRTE) very close to the LRTE. Among the tertiary providers, Auckland and Capital & Coast DHBs operate below the LRTE level, setting them apart from other tertiary providers. Similarly, Tairawhiti and West Coast DHBs also fall below the LRTE level, as indicated by their SRTE scores, potentially influenced by their unique healthcare settings and resource challenges. This research brings a new perspective to policy discussions by incorporating the temporal dynamics of decision-making and considering adjustment costs. It underscores the need to balance short-term and long-term technical efficiency, underlining their collective significance in fostering a sustainable and efficient healthcare system in New Zealand.
求助全文
通过发布文献求助，成功后即可免费获取论文全文。
去求助
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.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
