Enhancing patient accessibility of primary care: the redesign of Italian territorial medicine.
IF 2
3区 医学
Q2 HEALTH POLICY & SERVICES
引用次数: 0
Abstract
Ensuring widespread accessibility of healthcare services is a crucial policy objective. Accordingly, the Italian National Recovery and Resilience Plan (NRRP) has prioritized territorial medicine, channeling post-pandemic investments toward the restructuring of primary care services. A notable change is the establishment of Community Healthcare Centers (CHCs). This paper investigates how CHCs contribute to the accessibility of healthcare in urban and rural areas. By leveraging a comprehensive dataset of general practitioners' availability and estimating future demand-and-supply scenarios, we examine the impact of CHCs under two different capacity allocation strategies. Strategy 1-Capacity expansion-involves allocating additional service hours of general practitioners to CHCs in order to maximize accessibility. Strategy 2-Capacity redistribution-accounts for the persistent shortage of healthcare professionals faced by Italy in the recent years by reallocating a portion of general practitioners' current services from their existing workplace locations to CHCs. Our results indicate that CHCs have the potential to maintain current accessibility levels and also enhance them in the years to come. Moreover, we demonstrate that simply redistributing the current capacity can improve future accessibility. Finally, we show that a mix of the capacity expansion and redistribution strategies (Strategy 3) can maximize accessibility in the future, limiting the need for new professional staff.
提高病人获得初级保健的机会：意大利领土医学的重新设计。
确保广泛获得保健服务是一项重要的政策目标。因此，意大利国家恢复和复原力计划（nrp）优先考虑地方医疗，将大流行后的投资用于初级保健服务的重组。一个显著的变化是建立了社区卫生保健中心（CHCs）。本文调查了CHCs如何促进城市和农村地区医疗保健的可及性。通过利用全科医生可用性的综合数据集并估计未来的需求和供应情景，我们研究了两种不同容量分配策略下CHCs的影响。策略1——容量扩展——包括分配全科医生到CHCs的额外服务时间，以最大限度地提高可及性。战略2——能力再分配——通过将部分全科医生目前的服务从他们现有的工作地点重新分配到CHCs，来解决意大利近年来面临的医疗保健专业人员持续短缺的问题。我们的研究结果表明，CHCs有可能保持当前的可达性水平，并在未来几年提高可达性水平。此外，我们证明了简单地重新分配当前容量可以改善未来的可达性。最后，我们表明，容量扩张和再分配策略（策略3）的组合可以最大限度地提高未来的可达性，限制对新的专业人员的需求。
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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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