Developing a Planning Team

2010 ◽  
Author(s):  
Michael Fagel
Keyword(s):  
Planning Team

scholarly journals Exploring the Use of Ecosystem Services Conceptual Models to Account for the Benefits of Public Lands: An Example from National Forest Planning in the United States

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 267
Author(s):  
Lydia Olander ◽  
Katie Warnell ◽  
Travis Warziniack ◽  
Zoe Ghali ◽  
Chris Miller ◽  
...  
Keyword(s):  
Ecosystem Services ◽  
Ecosystem Service ◽  
Management Strategies ◽  
Public Lands ◽  
The United States ◽  
Forest Planning ◽  
National Forest ◽  
Planning Team ◽  
National Forest Planning

A shared understanding of the benefits and tradeoffs to people from alternative land management strategies is critical to successful decision-making for managing public lands and fostering shared stewardship. This study describes an approach for identifying and monitoring the types of resource benefits and tradeoffs considered in National Forest planning in the United States under the 2012 Planning Rule and demonstrates the use of tools for conceptualizing the production of ecosystem services and benefits from alternative land management strategies. Efforts to apply these tools through workshops and engagement exercises provide opportunities to explore and highlight measures, indicators, and data sources for characterizing benefits and tradeoffs in collaborative environments involving interdisciplinary planning teams. Conceptual modeling tools are applied to a case study examining the social and economic benefits of recreation on the Ashley National Forest. The case study illustrates how these types of tools facilitate dialog for planning teams to discuss alternatives and key ecosystem service outcomes, create easy to interpret visuals that map details in plans, and provide a basis for selecting ecosystem service (socio-economic) metrics. These metrics can be used to enhance environmental impact analysis, and help satisfy the goals of the National Environmental Policy Act (NEPA), the 2012 Planning Rule, and shared stewardship initiatives. The systematic consideration of ecosystem services outcomes and metrics supported by this approach enhanced dialog between members of the Forest planning team, allowed for a more transparent process in identification of key linkages and outcomes, and identified impacts and outcomes that may not have been apparent to the sociologist who is lacking the resource specific expertise of these participants. As a result, the use of the Ecosystem Service Conceptual Model (ESCM) process may result in reduced time for internal reviews and greater comprehension of anticipated outcomes and impacts of proposed management in the plan revision Environmental Impact Statement amongst the planning team.

The Contribution of Obeya for Business Intelligence

2021 ◽  
pp. 244-269
Author(s):  
Gonçalo Sousa ◽  
José Carlos Sá ◽  
Gilberto Santos ◽  
Francisco J. G. Silva ◽  
Luís Pinto Ferreira
Keyword(s):  
Decision Making ◽  
Business Intelligence ◽  
Performance Indicators ◽  
Paradigm Shift ◽  
Normal Functioning ◽  
Important Data ◽  
Planning Team ◽  
Weekly Meeting ◽  
Data Update ◽  
A Company

The main objective of the study is to minimize interdepartmental communication, potentiation of fast and efficient decision making, and computerization of data. Using software such as MS Excel® and MS Power BI®, a Power BI® tool was conceived to be capable of incorporating, for the entire company, the dashboards that collect the main KPIs of each department. After the tool was implemented, the company's paradigm shift was noticeable. Quickly, the weekly meeting of the planning team began to take place using the MS Power BI® dashboard. In this way, processes were automated and the important data for the normal functioning of the company became accessible to all departments, thus minimizing interdepartmental communication. The chapter shows an Obeya Digital that was implemented in a company in which all the performance indicators of each department are incorporated. In this way, information becomes accessible to all employees and manual data update processes are minimized.

scholarly journals The Emergence of Artificial Intelligence within Radiation Oncology Treatment Planning

Oncology ◽  
2020 ◽  
pp. 1-11
Author(s):  
Tucker J. Netherton ◽  
Carlos E. Cardenas ◽  
Dong Joo Rhee ◽  
Laurence E. Court ◽  
Beth M. Beadle
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Treatment Planning ◽  
Radiation Oncology ◽  
Radiation Treatment ◽  
Medical Physics ◽  
External Beam Radiotherapy ◽  
Radiation Treatment Planning ◽  
Planning Team ◽  
The Future

<b><i>Background:</i></b> The future of artificial intelligence (AI) heralds unprecedented change for the field of radiation oncology. Commercial vendors and academic institutions have created AI tools for radiation oncology, but such tools have not yet been widely adopted into clinical practice. In addition, numerous discussions have prompted careful thoughts about AI’s impact upon the future landscape of radiation oncology: How can we preserve innovation, creativity, and patient safety? When will AI-based tools be widely adopted into the clinic? Will the need for clinical staff be reduced? How will these devices and tools be developed and regulated? <b><i>Summary:</i></b> In this work, we examine how deep learning, a rapidly emerging subset of AI, fits into the broader historical context of advancements made in radiation oncology and medical physics. In addition, we examine a representative set of deep learning-based tools that are being made available for use in external beam radiotherapy treatment planning and how these deep learning-based tools and other AI-based tools will impact members of the radiation treatment planning team. <b><i>Key Messages:</i></b> Compared to past transformative innovations explored in this article, such as the Monte Carlo method or intensity-modulated radiotherapy, the development and adoption of deep learning-based tools is occurring at faster rates and promises to transform practices of the radiation treatment planning team. However, accessibility to these tools will be determined by each clinic’s access to the internet, web-based solutions, or high-performance computing hardware. As seen by the trends exhibited by many technologies, high dependence on new technology can result in harm should the product fail in an unexpected manner, be misused by the operator, or if the mitigation to an expected failure is not adequate. Thus, the need for developers and researchers to rigorously validate deep learning-based tools, for users to understand how to operate tools appropriately, and for professional bodies to develop guidelines for their use and maintenance is essential. Given that members of the radiation treatment planning team perform many tasks that are automatable, the use of deep learning-based tools, in combination with other automated treatment planning tools, may refocus tasks performed by the treatment planning team and may potentially reduce resource-related burdens for clinics with limited resources.

Decentralized Nurse Stations: A Methodology for Using Research to Guide Design Decisions

2019 ◽  
Vol 12 (4) ◽  
pp. 8-21
Author(s):  
Joyce Durham ◽  
Ann Kenyon
Keyword(s):  
Design Process ◽  
The Body ◽  
Second Phase ◽  
Facility Planning ◽  
Relevant Research ◽  
Summary Table ◽  
Design Decisions ◽  
Planning Team ◽  
Research Findings ◽  
Negative Attributes

Purpose: The purpose of this methodology is to define a process for facility planning teams to use to ensure research findings are used to guide decision making in the design process. Background: Over the past decade and a half, research in health facility design has developed and the body of knowledge has grown significantly, but at the same time, the process for incorporating these findings into the design process has been less defined. This methodology evolved out of the desire to develop a structured process to integrate recent research findings into the planning and programming process at the user group and planning team level. Method: This two-phase methodology consists of, first, reviewing recent, relevant research on the topic, classifying the findings into positive and negative attributes and, then, summarizing the attributes by category on a summary table and in a brief narrative. The second phase consists of reviewing the research to identify operational and facility strategies that can be used to mitigate the inconsistent and negative attributes identified. Results: In the case study, as a result of this process, one inconsistent attribute and three negative attributes were identified. In the second phase, potential research-based operational and facility strategies were identified to mitigate the inconsistent and negative attributes identified. This information served as the basis for making design decisions. Conclusions: This methodology presents an organized, efficient process for organizing and providing relevant research findings to a facility planning team to use in evaluating a new healthcare design concept and making research-based design decisions.

Imagination Into Action: A School–University Collaboration With a Design Thinking Imperative

2020 ◽  
pp. 155545892097544
Author(s):  
William L. Sterrett ◽  
Sabrina Hill-Black ◽  
John B. Nash
Keyword(s):  
Middle School ◽  
Problem Solving ◽  
Design Thinking ◽  
Creative Problem Solving ◽  
Planning Team ◽  
Urban Middle School ◽  
Collaborative Environment ◽  
Creative Problem

An urban middle school goes through the transformation of becoming a university-supported lab school. Drawing upon design thinking principles, the planning team cultivates a sense of shared empathy, creative problem-solving, and an ethos of curiosity and learning in a collaborative environment.

Infant deaths in Inner London: a health care planning team study

1980 ◽  
Vol 2 (2) ◽  
pp. 102
Author(s):  
S. R. Palmer ◽  
R. D. Wiggins ◽  
Beulah R. Bewley
Keyword(s):  
Health Care ◽  
Care Planning ◽  
Health Care Planning ◽  
Planning Team ◽  
Infant Deaths

THE PLANNING TEAM:

1972 ◽  
Vol 2 (5) ◽  
pp. 70-74
Author(s):  
Willis F. Fry ◽  
Joan Lauer
Keyword(s):  
Planning Team
Sign in / Sign up

Export Citation Format

Share Document