Application of business planning processes to health care

2002 ◽  
Vol 4 (4) ◽  
pp. 185-187
Author(s):  
Geoff Makinson
Keyword(s):  
Health Care ◽  
Business Planning ◽  
Planning Processes

Optimization of adaptive network economic and mathematical modeling of business planning process control

2021 ◽  
Vol 57 (3) ◽  
pp. 110
Author(s):  
Andrey Shorikov
Keyword(s):  
Mathematical Modeling ◽  
Adaptive Control ◽  
Execution Time ◽  
Planning Process ◽  
Numerical Algorithms ◽  
Implementation Process ◽  
New Method ◽  
Function Evaluation ◽  
Business Planning ◽  
Planning Processes

The article is devoted to the application of economic and mathematical models of business planning management based on the use of the feedback principle. As the objective function (evaluation toolkit) of the task, the value of the execution time of the entire business project, which must be minimized, is considered. To solve this problem, it is proposed to form a class of admissible strategies for optimal adaptive control of the implementation process; as well as a specific business project using network economic and mathematical modeling is worked out. Within the limits of these strategies, the method of achieving optimal self-adjusting control of business planning processes is determined, the optimal execution time and the optimal timetable for the implementation of the project are determined. The main feature of the proposed new method is the ability to take into account the real conditions for the implementation works of the concrete project, which makes it possible to timely adjust the process of management of business planning and prevent disruptions in its implementation. This method also serves as the basis for constructing numerical algorithms for the development and creating the automated systems for realization of optimal adaptive control of business planning processes. The results obtained are illustrated on a specific business project for opening a public catering enterprise and show a high degree of efficiency in using the new method.

scholarly journals Predicting health care utilization using CIHI's Population Grouping Methodology

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Yvonne Rosehart
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Health Care Utilization ◽  
Healthcare Utilization ◽  
Capacity Planning ◽  
Disease Surveillance ◽  
Health Condition ◽  
Care Utilization ◽  
Term Care ◽  
Planning Processes

IntroductionCIHI’s Population Grouping Methodology uses data from multiple sectors to create clinical profiles and to predict the entire population’s current and future morbidity burden and healthcare utilization. Outputs from the grouper can be applied to healthcare decision making and planning processes. Objectives and ApproachThe population grouping methodology starts with everyone who is eligible for healthcare, including those who haven’t interacted with the healthcare system, providing a true picture of the entire population. The grouper uses diagnosis information over a 2-year period to create health profiles and predict individuals’ future morbidity and expected use of primary care, emergency department and long-term care services. Predictive models were developed using age, sex, health conditions and the most influential health condition interactions as the predictors. These models produce predictive indicators for the concurrent period as well as one year into the future. ResultsThe power of the model lies in the user’s ability to aggregate the data by population segments and compare healthcare resource utilization by different geographic regions, health sectors and health status. The presentation will focus on how CIHI’s population grouping methodology helps client’s monitor population health and conduct disease surveillance. It assists clients with population segmentation, health profiling, predicting health care utilization patterns and explaining variation in health care resource use. It can be used for risk adjustment of populations for inter-jurisdictional analysis, for capacity planning and it can also be used as a component in funding models. Conclusion/ImplicationsCIHI’s population grouping methodology is a useful tool for profiling and predicting healthcare utilization, with key applications for health policy makers, planners and funders. The presentation will focus on how stakeholders can apply the outputs to aid in their decision-making and planning processes.

THINKING AND SLACKING OR DOING AND FEELING? GENDER AND THE INTERPLAY OF COGNITION AND AFFECT IN NEW VENTURE PLANNING

2011 ◽  
Vol 16 (02) ◽  
pp. 213-226 ◽  
Author(s):  
CRAIG E. ARMSTRONG
Keyword(s):  
Entrepreneurial Intentions ◽  
New Venture ◽  
Behavior Model ◽  
Business Planning ◽  
Entrepreneurial Opportunity ◽  
Sense Of Ownership ◽  
Entrepreneurial Opportunities ◽  
New Business ◽  
Planning Processes ◽  
The Relationship

Studies of the relationship between gender and entrepreneurship have shown that men are significantly more likely to start a new business than women. Because an individual's entrepreneurial intentions are shaped by the perceived feasibility and desirability of an entrepreneurial opportunity, these results have generally emphasized how men perceive themselves as more capable of pursuing entrepreneurial opportunities than women. In this study, men have a higher level of self-efficacy than do women regarding entrepreneurial abilities. At the same time, the higher levels of involvement in business planning processes caused women to have a higher sense of ownership in the plan than did men. This sense of ownership is positively and significantly related to the perceived likelihood of success of the new venture. The findings of this study suggest women adopt certain roles and affects in the development of entrepreneurial opportunities that provide alternative explanations to the beliefs-attitudes-intentions-behavior model of intentionality. The roles and affects women adopt during new venture planning may give them superior insights into the likelihood of success of the new venture.

scholarly journals Intelligent software system for optimizing adaptive control of business planning processes

2020 ◽  
Vol 15 (89) ◽  
pp. 9-28
Author(s):  
Andrey F. Shorikov ◽  
◽  
Elena V. Butsenko ◽  
Keyword(s):  
Mathematical Modeling ◽  
Decision Making ◽  
Adaptive Control ◽  
Business Plan ◽  
Optimal Time ◽  
Software System ◽  
Business Planning ◽  
Intelligent Software ◽  
The Face ◽  
Planning Processes

The article describes the functionality developed by the authors of an intelligent software system for optimizing adaptive control of business planning processes in the face of uncertainty. The results are based on a new method for optimizing adaptive project management using network economic and mathematical modeling. Based on this method, a methodology has been developed for solving the problem of optimizing adaptive control of business planning processes, which in the proposed intelligent software decision support system uses a block containing an adaptive control optimization model. As the objective function (evaluation functional) in the method used, the value of the length of the time period for the execution of the business plan, which needs to be minimized, is considered. The method used allows you to create a class of acceptable strategies for adaptive control of the implementation process for the business plan in question. Within the framework of this class of strategies, an optimal adaptive control strategy for the implementation of business planning processes is formed, the optimal time for its implementation and the optimal schedule for implementing the business plan as a whole, and the corresponding optimal adaptive control strategies are calculated. Application of the proposed new method in an intelligent software system allows for feedback and optimal time for the implementation of the business project as a whole. The developed intelligent system is designed to automate the modeling of business planning processes and optimize adaptive decision-making control during their implementation on the basis of network economic and mathematical modeling, as well as methods and tools for developing intelligent soft systems. The created system takes into account the existing specific technical and economic conditions and information support. The results obtained in this work can serve as the basis for creating intelligent instrumental systems for supporting managerial decision-making in the implementation of business planning processes in the face of information uncertainty and risks.

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