scholarly journals Identifying and selecting implementation theories, models and frameworks: a qualitative study to inform the development of a decision support tool

2020 ◽  
Author(s):  
Lisa Strifler ◽  
Jan M. Barnsley ◽  
Michael Hillmer ◽  
Sharon E. Straus
Keyword(s):  
Decision Support ◽  
Decision Support Tool ◽  
The United States ◽  
Theory Model ◽  
User Preferences ◽  
Tool Development ◽  
End User ◽  
Structured Interviews ◽  
Implementation Theory ◽  
Support Tool

Abstract Background: Implementation theories, models and frameworks offer guidance when implementing and sustaining healthcare evidence-based interventions. However, selection can be challenging given the myriad of potential options. We propose to develop a decision support tool to facilitate the appropriate selection of an implementation theory, model or framework in practice. To inform tool development, this study aimed to explore barriers and facilitators to identifying and selecting implementation theories, models and frameworks in research and practice, as well as end-user preferences for features and functions of the proposed tool.Methods: We used an interpretive descriptive approach to conduct semi-structured interviews with implementation researchers and practitioners in Canada, the United States and Australia. Audio recordings were transcribed verbatim. Data were inductively coded by a single investigator with a subset of 20% coded independently by a second investigator and analyzed using thematic analysis.Results: Twenty-four individuals participated in the study. Categories of barriers/facilitators, to inform tool development, included characteristics of the individual or team conducting implementation and characteristics of the implementation theory, model or framework. Major barriers to selection included inconsistent terminology, poor fit with the implementation context and limited knowledge about and training in existing theories, models and frameworks. Major facilitators to selection included the importance of clear and concise language and evidence that the theory, model or framework was applied in a relevant health setting or context. Participants were enthusiastic about the development of a decision support tool that is user-friendly, accessible and practical. Preferences for tool features included key questions about the implementation intervention or project (e.g., purpose, stage of implementation, intended target for change) and a comprehensive list of relevant theories, models and frameworks to choose from along with a glossary of terms and the contexts in which they were applied.Conclusions: An easy to use decision support tool that addresses key barriers to selecting an implementation theory, model or framework in practice may be beneficial to individuals who facilitate implementation practice activities. Findings on end-user preferences for tool features and functions will inform tool development and design through a user-centered approach.

scholarly journals Identifying and selecting implementation theories, models and frameworks: a qualitative study to inform the development of a decision support tool

2020 ◽  
Author(s):  
Lisa Strifler ◽  
Jan M. Barnsley ◽  
Michael Hillmer ◽  
Sharon E. Straus
Keyword(s):  
Decision Support ◽  
Decision Support Tool ◽  
The United States ◽  
Theory Model ◽  
User Preferences ◽  
Tool Development ◽  
End User ◽  
Structured Interviews ◽  
Implementation Theory ◽  
Support Tool

Abstract Background: Implementation theories, models and frameworks offer guidance when implementing and sustaining healthcare evidence-based interventions. However, selection can be challenging given the myriad of potential options. We propose to inform a decision support tool to facilitate the appropriate selection of an implementation theory, model or framework in practice. To inform tool development, this study aimed to explore barriers and facilitators to identifying and selecting implementation theories, models and frameworks in research and practice, as well as end-user preferences for features and functions of the proposed tool.Methods: We used an interpretive descriptive approach to conduct semi-structured interviews with implementation researchers and practitioners in Canada, the United States and Australia. Audio recordings were transcribed verbatim. Data were inductively coded by a single investigator with a subset of 20% coded independently by a second investigator and analyzed using thematic analysis.Results: Twenty-four individuals participated in the study. Categories of barriers/facilitators, to inform tool development, included characteristics of the individual or team conducting implementation and characteristics of the implementation theory, model or framework. Major barriers to selection included inconsistent terminology, poor fit with the implementation context and limited knowledge about and training in existing theories, models and frameworks. Major facilitators to selection included the importance of clear and concise language and evidence that the theory, model or framework was applied in a relevant health setting or context. Participants were enthusiastic about the development of a decision support tool that is user-friendly, accessible and practical. Preferences for tool features included key questions about the implementation intervention or project (e.g., purpose, stage of implementation, intended target for change) and a comprehensive list of relevant theories, models and frameworks to choose from along with a glossary of terms and the contexts in which they were applied.Conclusions: An easy to use decision support tool that addresses key barriers to selecting an implementation theory, model or framework in practice may be beneficial to individuals who facilitate implementation practice activities. Findings on end-user preferences for tool features and functions will inform tool development and design through a user-centered approach.

scholarly journals Identifying and selecting implementation theories, models and frameworks: a qualitative study to inform the development of a decision support tool

2019 ◽  
Author(s):  
Lisa Strifler ◽  
Jan M. Barnsley ◽  
Michael Hillmer ◽  
Sharon E. Straus
Keyword(s):  
Decision Support ◽  
Decision Support Tool ◽  
The United States ◽  
Theory Model ◽  
User Preferences ◽  
Tool Development ◽  
End User ◽  
Structured Interviews ◽  
Implementation Theory ◽  
Support Tool

Abstract Background Implementation theories, models and frameworks offer guidance when implementing and sustaining healthcare evidence-based interventions. However, selection can be challenging given the myriad of potential options. We propose to develop a decision support tool to facilitate the appropriate selection of an implementation theory, model or framework in practice. To inform tool development, this study aimed to explore barriers and facilitators to identifying and selecting implementation theories, models and frameworks in research and practice, as well as end-user preferences for features and functions of the proposed tool.Methods We used an interpretive descriptive approach to conduct semi-structured interviews with implementation researchers and practitioners in Canada, the United States and Australia. Audio recordings were transcribed verbatim. Data were inductively coded by a single investigator with a subset of 20% coded independently by a second investigator and analyzed using thematic analysis.Results A total of 24 participants identified characteristics of the individual or team conducting implementation and characteristics of the implementation theory, model or framework as major categories of barriers/facilitators to inform tool development. Major barriers to selection included inconsistent terminology, poor fit with the implementation context and limited knowledge about and training in existing theories, models and frameworks. Major facilitators to selection included the importance of clear and concise language and evidence that the theory, model or framework was applied in a relevant health setting or context. Participants were enthusiastic about the development of a decision support tool that is user-friendly, accessible and practical. Preferences for tool features included key questions about the implementation intervention or project (e.g., purpose, stage of implementation, intended target for change) and a comprehensive list of relevant theories, models and frameworks to choose from along with a glossary of terms and the contexts in which they were applied.Conclusions An easy to use decision support tool that addresses key barriers to selecting an implementation theory, model or framework in practice may be beneficial to individuals who facilitate implementation practice activities. Findings on end-user preferences for tool features and functions will inform tool design and development through a user-centered approach.

An online decision support tool to evaluate ecological weed management strategies

Weed Science ◽  
2019 ◽  
Vol 67 (4) ◽  
pp. 463-473
Author(s):  
Douglas Bessette ◽  
Robyn Wilson ◽  
Christian Beaudrie ◽  
Clayton Schroeder
Keyword(s):  
Decision Support ◽  
Soil Quality ◽  
Weed Management ◽  
Decision Support Tool ◽  
The United States ◽  
Support Tool ◽  
Organic Farmers ◽  
Ecological Weed Management ◽  
On Farm

AbstractWeeds remain the most commonly cited concern of organic farmers. Without the benefit of synthetic herbicides, organic farmers must rely on a host of ecological weed management (EWM) practices to control weeds. Despite EWM’s ability to improve soil quality, the perceived rate of integrated EWM strategy adoption remains low. This low adoption is likely a result of the complexity in designing and evaluating EWM strategies, the tendency for outreach to focus on the risks of EWM strategies rather than their benefits, and a lack of quantitative measures linking the performance of EWM strategies to farmers’ on-farm objectives and practices. Here we report on the development and deployment of an easy-to-use online decision support tool (DST) that aids organic farmers in identifying their on-farm objectives, characterizing the performance of their practices, and evaluating EWM strategies recommended by an expert advisory panel. Informed by the principles of structured decision making, the DST uses multiple choice tasks to help farmers evaluate the short- and long-term trade-offs of EWM strategies, while also focusing their attention on their most important objectives. We then invited organic farmers across the United States, in particular those whose email addresses were registered on the USDA’s Organic Research Integrity Database, to engage the DST online. Results show considerable movement in participants’ (n = 45) preferences from practices focused on reducing weeding costs and labor in the short term to EWM strategies focused on improving soil quality in the long term. Indeed, nearly half of those farmers (48%) who initially ranked a strategy composed of their current practices highest ultimately preferred a better-performing EWM strategy focused on eliminating the weed seedbank over 5 yr.

scholarly journals Toward a User-Centered Design of a Weather Forecasting Decision-Support Tool

2017 ◽  
Vol 98 (2) ◽  
pp. 373-382 ◽  
Author(s):  
Elizabeth M. Argyle ◽  
Jonathan J. Gourley ◽  
Zachary L. Flamig ◽  
Tracy Hansen ◽  
Kevin Manross
Keyword(s):  
Decision Support ◽  
Weather Forecasting ◽  
Decision Support Tool ◽  
The United States ◽  
Usability Evaluation ◽  
Error Rates ◽  
Decision Support Tools ◽  
User Centered Design ◽  
Support Tool ◽  
Support Tools

ABSTRACT Hazard Services is a software toolkit that integrates information management, hazard alerting, and communication functions into a single user interface. When complete, National Weather Service forecasters across the United States will use Hazard Services for operational issuance of weather and hydrologic alerts, making the system an instrumental part of the threat management process. As a new decision-support tool, incorporating an understanding of user requirements and behavior is an important part of building a system that is usable, allowing users to perform work-related tasks efficiently and effectively. This paper discusses the Hazard Services system and findings from a usability evaluation with a sample of end users. Usability evaluations are frequently used to support software and website development and can provide feedback on a system’s efficiency of use, effectiveness, and learnability. In the present study, a user-testing evaluation assessed task performance in terms of error rates, error types, response time, and subjective feedback from a questionnaire. A series of design recommendations was developed based on the evaluation’s findings. The recommendations not only further the design of Hazard Services, but they may also inform the designs of other decision-support tools used in weather and hydrologic forecasting. Incorporating usability evaluation into the iterative design of decision-support tools, such as Hazard Services, can improve system efficiency, effectiveness, and user experience.

Decision support for additive manufacturing deployment in remote or austere environments

2016 ◽  
Vol 27 (7) ◽  
pp. 898-914 ◽  
Author(s):  
Nicholas A. Meisel ◽  
Christopher B. Williams ◽  
Kimberly P. Ellis ◽  
Don Taylor
Keyword(s):  
Decision Support ◽  
Additive Manufacturing ◽  
Decision Support Tool ◽  
Spare Parts ◽  
Raw Material ◽  
Structured Interviews ◽  
Material Source ◽  
Content Type ◽  
Support Tool ◽  
Austere Environments

Purpose Additive manufacturing (AM) can reduce the process supply chain and encourage manufacturing innovation in remote or austere environments by producing an array of replacement/spare parts from a single raw material source. The wide variety of AM technologies, materials, and potential use cases necessitates decision support that addresses the diverse considerations of deployable manufacturing. The paper aims to discuss these issues. Design/methodology/approach Semi-structured interviews with potential users are conducted in order to establish a general deployable AM framework. This framework then forms the basis for a decision support tool to help users determine appropriate machines and materials for their desired deployable context. Findings User constraints are separated into process, machine, part, material, environmental, and logistical categories to form a deployable AM framework. These inform a “tiered funnel” selection tool, where each stage requires increased user knowledge of AM and the deployable context. The tool can help users narrow a database of candidate machines and materials to those appropriate for their deployable context. Research limitations/implications Future work will focus on expanding the environments covered by the decision support tool and expanding the user needs pool to incorporate private sector users and users less familiar with AM processes. Practical implications The framework in this paper can influence the growth of existing deployable manufacturing endeavors (e.g. Rapid Equipping Force Expeditionary Lab – Mobile, Army’s Mobile Parts Hospital, etc.) and considerations for future deployable AM systems. Originality/value This work represents novel research to develop both a framework for deployable AM and a user-driven decision support tool to select a process and material for the deployable context.

scholarly journals Improving patient prioritization during hospital-homecare transition: A protocol of a mixed-methods study of a clinical decision support tool implementation (Preprint)

2020 ◽  
Author(s):  
Maryam Zolnoori ◽  
Margaret McDonald ◽  
Kenrick Cato ◽  
Paulina Sockolow ◽  
Nicole Onorato ◽  
...  
Keyword(s):  
Decision Support ◽  
Mixed Methods ◽  
Patient Outcomes ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
The United States ◽  
Support Tool ◽  
Propensity Score Matching Analysis ◽  
Healthcare Needs ◽  
Nursing Visit

BACKGROUND Homecare settings across the United States provide care to more than 5 million patients every year. About one in five homecare patients are rehospitalized during the homecare episode, with up to two-thirds of these rehospitalizations occurring within the first two weeks of services. Timely alloca-tion of homecare services might prevent a significant portion of these rehospitalizations. The first homecare nursing visit is one of the most critical steps of the homecare episode. This visit includes an assessment of the patient's capacity for self-care, medication reconciliation, an examination of the home environment, and a discussion regarding whether a caregiver is present. Hence, appro-priate timing of the first visit is crucial, especially for patients with urgent healthcare needs. However, nurses often have limited and inaccurate information about incoming patients and patient priority decisions vary significantly between nurses. We developed an innovative decision support tool called “Priority for the First Nursing Visit Tool” (PREVENT) to assist nurses in prioritizing patients in need of immediate first homecare nursing visits. OBJECTIVE To evaluate the effectiveness of the PREVENT tool on process and patient outcomes; and to exam-ine aspects of PREVENT’s reach, adoption, and implementation. METHODS Employing a pre post design, and survival analysis and logistic regression with propensity score matching analysis we will test the following hypotheses: Compared to not using the tool in the pre-intervention phase, when homecare clinicians use the PREVENT tool, high risk patients in the inter-vention phase will: a) receive more timely first homecare visits and b) have decreased incidence of rehospitalization and have decreased emergency department (ED) use within 60 days. Reach, adoption, and implementationwill be assessed using mixed methods including homecare admis-sion staff interviews, think-aloud observations, and analysis of staffing and other relevant data. RESULTS The study research protocol was approved by the institutional review board in October 2019. PRE-VENT is currently being integrated into the electronic health records at the participating study sites. Data collection is planned to start in early 2021. CONCLUSIONS Mixed methods will enable us to gain in-depth understanding of complex socio-technological as-pects of the hospital to homecare transition. The results have the potential to (1) influence the standardization and individualization of nurse decision making thru the use of cutting-edge technol-ogy and (2) improve patient outcomes in the understudied homecare setting. CLINICALTRIAL ClinicalTrials.gov Identifier: NCT04136951, https://clinicaltrials.gov/ct2/show/NCT04136951?term=Maxim+Topaz&id=NCT04136951&draw=2&rank=1

A novel toolkit to streamline Land Use Land Cover change assessment in the SWAT+ model to enhance flood management and infrastructure decisions

2021 ◽  
Author(s):  
Alex Rigby ◽  
Sopan Patil ◽  
Panagiotis Ritsos
Keyword(s):  
Land Use ◽  
Decision Support ◽  
Swat Model ◽  
Decision Support Tool ◽  
Flood Management ◽  
Land Use Land Cover ◽  
End User ◽  
Support Tool ◽  
Lulc Changes ◽  
Hydrological Impacts

<p>Land Use Land Cover (LULC) change is widely recognised as one of the most important factors impacting river basin hydrology.  It is therefore imperative that the hydrological impacts of various LULC changes are considered for effective flood management strategies and future infrastructure decisions within a catchment.  The Soil and Water assessment Tool (SWAT) has been used extensively to assess the hydrological impacts of LULC change.  Areas with assumed homogeneous hydrologic properties, based on their LULC, soil type and slope, make up the basic computational units of SWAT known as the Hydrologic Response Units (HRUs).  LULC changes in a catchment are typically modelled by SWAT through alterations to the input files that define the properties of these HRUs.  However, to our knowledge at least, the process of making such changes to the SWAT input files is often cumbersome and non-intuitive.  This affects the useability of SWAT as a decision support tool amongst a wider pool of applied users (e.g., engineering teams in environmental regulatory agencies and local authorities).  In this study, we seek to address this issue by developing a user-friendly toolkit that will: (1) allow the end user to specify, through a Graphical User Interface (GUI), various types of LULC changes at multiple locations within their study catchment, (2) run the SWAT+ model (the latest version of SWAT) with the specified LULC changes, and (3) enable interactive visualisation of the different SWAT+ output variables to quantify the hydrological impacts of these scenarios.  Importantly, our toolkit does not require the end user to have any operational knowledge of the SWAT+ model to use it as a decision support tool.  Our toolkit will be trialled at 15 catchments in Gwynedd county, Wales, which has experienced multiple occurrences of high flood events, and consequent economic damage, in the recent past.  We anticipate this toolkit to be a valuable addition to the decision-making processes of Gwynedd County Council for the planning and development of future flood alleviation schemes as well as other infrastructure projects.</p>

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