Grey systems in the management of demand for palliative care services in Poland

Author:

Nieszporska SylwiaORCID

Abstract

Abstract Background The concept of care for people in a critical or even terminal health condition, who are in the last stage of their life, has become the mission of palliative care facilities. Therefore, the life of a sick patient poses a number of challenges for health care services to make sure that medical services are tailored to the trajectory of the disease, as well as the various needs, preferences and resources of patients and their families. Methods Health systems financed from public funds need to adopt new methods of management to meet the high and arising demand for a long-term care. There are several ways of assessing the demand for long-term care services. The method recommended by the author and presented in more detail in this paper is the one relying on grey systems, which enables the estimation of forecasting models and, finally, actual forecasts of the number of potential future patients. Results GST can be used to make predictions about the future behaviour of the system, which is why this article aims to present the possibility of using the first-order grey model GM (1,1) in predicting the number of patients of palliative care facilities in Poland. The analysis covers the data from 2014 to 2019, whereas the prediction of the number of patients has been additionally formulated for 2020. Conclusions Health systems, particularly publicly funded ones, are characterised by a certain kind of incompleteness and uncertainty of data on the structure and behaviour of its individual components (e.g. potential patients or payers). The present study aims to prove how simple and effective grey systems models are in the decision-making process.

Publisher

Springer Science and Business Media LLC

Subject

Health Policy

Reference44 articles.

1. Global Atlas of Palliative Care at the End of Life. In: Connor SR, Sepulveda Bermedo MC, (editors). WHO, Worldwide Palliative Care Alliance; 2014.

2. Elderly population. https://data.oecd.org/pop/elderly-population.htm (22.02.2021).

3. World Population Ageing 2017. Highlights, United Nations. New York: Department of Economic and Social Affairs; 2017.

4. Prentice T (2006). Health, history and hard choices: Funding dilemmas in a fast-changing world. Global Health Histories, World Health Organization, Health and Philanthropy: Leveraging change, University of Indiana, August 2006.

5. Life expectancyat birth. https://data.worldbank.org/indicator/SP.DYN.LE00.IN(18.08.2020).

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Prediction of Medical Health Human Resources in Changchun City Based on Grey Model;2023 13th International Conference on Information Technology in Medicine and Education (ITME);2023-11-24

2. Experiences of workplace violence among healthcare workers in home care settings: A qualitative systematic review;International Nursing Review;2022-12-29

3. Real-time forecasting of the COVID 19 using fuzzy grey Markov: a different approach in decision-making;Computational and Applied Mathematics;2022-07-13

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3