Health Systems and Policy Research

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Mini Review - (2022) Volume 9, Issue 8

Ayurvedic Health Care Delivery to Meet Society’s Needs

Deborah A Marshall Marshall*
Department of Community Health Sciences, University of Chicago, United States of America
*Correspondence: Deborah A Marshall Marshall, Department of Community Health Sciences, University of Chicago, United States of America, Email:

Received: 01-Aug-2022, Manuscript No. Iphspr- 22- 13023; Editor assigned: 05-Aug-2022, Pre QC No. Iphspr- 22- 13023; Reviewed: 19-Aug-2022, QC No. Iphspr- 22- 13023; Revised: 23-Aug-2022, Manuscript No. Iphspr- 22- 13023 (R); Published: 31-Aug-2022, DOI: 10.36648/2254-9137.22.9.139


Health care delivery systems are inherently complicated because they are made up of numerous tiers of interconnected subsystems and processes that respond nonlinearly to environmental changes. When used on complex health systems, traditional approaches for assessing and modelling health technology sometimes overlook the broader health system consequences that can be crucial for accomplishing desired health system goals. Researchers and decision-makers in the healthcare industry may either undervalue or neglect to take into account the interconnections between people, procedures, technology, and facility designs. Interventions in the healthcare delivery system must take into account the dynamism and complexity of the environment in which they are being given.


Decision making; Dynamic simulation Modeling; Health care delivery


This study gives a general overview of popular dynamic simulation modelling techniques and gives instances of health care system interventions where these techniques could be helpful. System dynamics, discrete event simulation, and agent-based modelling are the three dynamic simulation modelling approaches that are provided to assess system interventions for the provision of healthcare [1]. A dynamic systems approach, in contrast to standard evaluations, takes into account the system's complexity and anticipates the upstream and downstream effects of changes in complex health care delivery systems [2]. Through the use of an eight-point checklist known as the SIMULATE, this report aids researchers and decision-makers in determining if these simulation methodologies are appropriate to solve specific health system problems [3]. It serves as a primer for researchers and decision-makers in the health care delivery and implementation sciences who deal with difficult problems delivering high-quality, cost-effective care. After reading this report, readers should be able to determine whether these simulation modelling approaches are suitable for solving the issue they are posing as well as the ways in which they differ from other modelling approaches frequently utilised in health technology assessment applications [4]. Techniques and keywords ISPOR Emerging Good Practices for Outcomes Research Task Force should be established to focus on dynamic simulation modelling techniques that can be used in health care delivery research, according to a recommendation made to the ISPOR Board of Directors Experts in modelling, epidemiology, research, systems and industrial engineering, economics, and health technology assessment make up the task force leadership team [5]. Members of the task team were chosen to reflect a variety of viewpoints. They are employed by hospital health systems, research institutions, educational institutions, and the pharmaceutical sector [6]. The task committee also included representatives from outside, including Colombia, Canada, the Netherlands, and the United States. The task force had teleconference meetings roughly every five weeks to establish an overview and discuss topics for the report. Members of the task force also had face-to-face meetings at ISPOR conferences and European congresses [7]. Each member of the task force read through numerous revisions of the report and frequently offered input in writing and oral comments. At the ISPOR Annual European Congress in Amsterdam and the 2014 ISPOR Annual International Meeting in Montreal, findings and recommendations were given in forum and workshop presentations. Additionally, written comments were received following distribution of the first and final draught reports to the 190-member ISPOR Modeling Review Group [8]. The task force examined these comments during a day-long face-to-face consensus meeting and a series of teleconferences [9]. All feedback was taken into account, and the majority of were significant and productive. In later iterations of the report, comments were taken into consideration as necessary. All written comments are posted on the task force's webpage at the ISPOR website. During task force discussions, in response to specific reviewer comments and suggestions, and in reaction to developing concerns about It soon became clear that two task force reports would be required in order to be comprehensive, contain the crucial information, and still keep the report legible and understandable [10].


The content was divided into two articles with Value in Health's consent. This first article serves as an introduction to the use of dynamic simulation modelling techniques to address issues with the health system. It explains the principles and definitions and explains how dynamic simulation modelling approaches differ from conventional models used in economic evaluation and why they are important for studies on health care delivery. It includes a short explanation of each technique (e.g., discrete event simulation, agent-based modelling) and instructions on how to use the SIMULATE checklist created by the task force to determine whether these simulation techniques are applicable for a given problem [11]. The second paper will go into further detail, exploring the technical details pertaining to the three dynamic simulation modelling approaches. It will compare each approach methodically over a range of aspects and offer advice on how to perform dynamic simulation modelling research effectively [12]. The Value in Health issue for Marchâ??April 2015 will include this report. Governments, payers, and numerous providers are all involved in the delivery of health care services to patients in specific geographic areas, making health care delivery systems by their very nature complex and fragmented social systems [13]. People make decisions, interact with one another, and interact with other components of the system in a way that is interdependent, which distinguishes social systems from other systems. Planning health care services is challenging. Services in these kinds of complicated systems because people's choices and decisions are ever-changing. Customizing care to meet each patient's needs in the era of patient-centered care increases the complexity of health care delivery systems [14]. Because of the potential for emergent system behaviour, complexity makes it difficult for decision-makers to assess actions that can enhance the efficacy and efficiency of health care delivery. Despite being widely used as ways to analyse healthcare interventions, modelling techniques like decision trees and Markov models are insufficient for examining complicated health care delivery systems [15]. With recent developments in available computing power and data analytics that make it possible to simulate the impact of system actions, dynamic simulation modelling offers advantages. Without engaging in expensive and time-consuming direct experiments, on health care delivery systems. A novel system intervention's comparative efficacy and cost-effectiveness can be predicted using the outcomes of such simulation models. Methods for dynamic simulation modelling are presented in this task force study to assess system interventions for healthcare delivery. It serves as a primer for researchers and decision-makers who must navigate difficult obstacles in order to provide high-quality treatment. Three dynamic simulation modelling techniques are suitable for and frequently used to solve these kinds of issues, according to experience from the domains of industrial engineering and operations research: dynamic systems this article offers an overview of these dynamic simulation modelling techniques as well as illustrations of health care system actions that could benefit from them. Regarding system interventions for which such techniques might be helpful. Its goal is to help academics and decision-makers determine if these simulation techniques are suitable for dealing with certain health system issues. To determine whether these dynamic simulation modelling techniques are appropriate to tackle the topic of interest, an eight-point checklist known as the SIMULATE tool is included. In order to provide readers with further information on modelling system interventions in the developing field of health care delivery science and implementation, the study also provides links to additional resources. After reading this report, readers should be able to determine whether these dynamic simulation modelling techniques are suitable to solve the issue at hand and to distinguish between these techniques' differences. of these techniques from different modelling strategies. The complexity of a system affects its interactions and behaviour. Similarly, complexity is regarded as a system property rather than an intervention. Tasks in complex systems are relationally dependent occurrences with unpredictability.


A complex system is made up of other complex systems and can adapt to changes in its immediate surroundings. Additionally, the system's overall behaviour differs from that of its individual portions or components. A complex system can be understood in part by comprehending this emergent behaviour. Simple systems consist of tasks that can be solved in contrast to these complicated systems. Several intricate systems make up the health care industry. Complex systems in the provision of health care, for instance, include an overview of modelling techniques for dynamic simulation For the purpose of advancing our understanding of systems and processes, communicating findings, and informing management and policy design, dynamic simulation modelling techniques are used to create and refine mathematical models of how processes and systems operate. With the intention of combining science and engineering to pinpoint the fundamental challenges that affect the success or failure of organisations, Forrester established a lab at the Massachusetts Institute of Technology. His engagement with General Electric and the administrative issues the firm was experiencing had a significant impact on his work. Using manual simulations of the production plants' stock-flow-feedback systems, as well as the current corporate decision-making process for recruiting and firing, however, the various restrictions that health care systems face are not taken into consideration by health economic models. The health care system is subject to numerous constraints, including those imposed by provider budgets, patient out-of-pocket expenses, physical space and facility designs, staff sizes, delivery procedures, workflow productivity, access to technology, and time. It will be necessary for these new approaches to become prescriptive in character, which means that the models will suggest what actions/interventions to take on the basis of scenarios tested through experiments, in order to design health care systems that deliver value. Decision-makers can see the simultaneous consequences of interventions on various system components using simulation modelling.



Conflict of Interest



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Citation: Deborah A Marshall Marshall (2022) Ayurvedic Health Care Delivery to Meet Societyâ??s Needs. Health Sys Policy Res Vol. 09 No. Issue 08:139