scholarly journals Opportunities and Barriers for Adoption of a Decision-Support Tool for Alzheimer’s Disease

2021 ◽  
Vol 2 (4) ◽  
pp. 1-19
Author(s):  
Maura Bellio ◽  
Dominic Furniss ◽  
Neil P. Oxtoby ◽  
Sara Garbarino ◽  
Nicholas C. Firth ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Decision Support ◽  
Design Research ◽  
Clinical Care ◽  
Decision Support Tool ◽  
Added Value ◽  
Structured Interviews ◽  
Support Tool ◽  
Combination Of Methods

Clinical decision-support tools (DSTs) represent a valuable resource in healthcare. However, lack of Human Factors considerations and early design research has often limited their successful adoption. To complement previous technically focused work, we studied adoption opportunities of a future DST built on a predictive model of Alzheimer’s Disease (AD) progression. Our aim is two-fold: exploring adoption opportunities for DSTs in AD clinical care, and testing a novel combination of methods to support this process. We focused on understanding current clinical needs and practices, and the potential for such a tool to be integrated into the setting, prior to its development. Our user-centred approach was based on field observations and semi-structured interviews, analysed through workflow analysis, user profiles, and a design-reality gap model. The first two are common practice, whilst the latter provided added value in highlighting specific adoption needs. We identified the likely early adopters of the tool as being both psychiatrists and neurologists based in research-oriented clinical settings. We defined ten key requirements for the translation and adoption of DSTs for AD around IT, user, and contextual factors. Future works can use and build on these requirements to stand a greater chance to get adopted in the clinical setting.

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.

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 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 Physicians’ strategies for using family history data: having the data is not the same as using the data

JAMIA Open ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 378-385
Author(s):  
Peter Taber ◽  
Parveen Ghani ◽  
Joshua D Schiffman ◽  
Wendy Kohlmann ◽  
Rachel Hess ◽  
...  
Keyword(s):  
Decision Support ◽  
Family Health ◽  
Improve Patient Care ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Primary Care Providers ◽  
Genetic Services ◽  
Care Providers ◽  
Structured Interviews ◽  
Support Tool

Abstract Objective To identify needs in a clinical decision support tool development by exploring how primary care providers currently collect and use family health history (FHH). Design Survey questionnaires and semi-structured interviews were administered to a mix of primary and specialty care clinicians within the University of Utah Health system (40 surveys, 12 interviews). Results Three key themes emerged regarding providers’ collection and use of FHH: (1) Strategies for collecting FHH vary by level of effort; (2) Documentation practices extend beyond the electronic health record’s dedicated FHH module; and (3) Providers desire feedback from genetic services consultation and are uncertain how to refer patients to genetic services. Conclusion Study findings highlight the varying degrees of engagement that providers have with collecting FHH. Improving the integration of FHH into workflow, and providing decision support, as well as links and tools to help providers better utilize genetic counseling may improve patient care.

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.

Developing a decision support tool for responding to patients’ reported levels of information needs, family anxiety, depression, and breathlessness.

2014 ◽  
Vol 32 (31_suppl) ◽  
pp. 173-173
Author(s):  
Liesbeth van Vliet ◽  
Richard Harding ◽  
Claudia Bausewein ◽  
Sheila Payne ◽  
Irene J. Higginson ◽  
...  
Keyword(s):  
Palliative Care ◽  
Decision Support ◽  
Delphi Study ◽  
Information Needs ◽  
Clinical Care ◽  
Decision Support Tool ◽  
Psychosocial Interventions ◽  
Social Needs ◽  
Support Tool ◽  
Patient Reported

173 Background: Routine clinical use of Patient Reported Outcome Measures (PROMs) such as the Palliative Care Outcome Scale (POS) may be prevented by a lack of guidance on how to respond to reported symptoms. When using POS in clinical care, clinicians encounter the most difficulties with responding to information needs, depression and family anxiety while breathlessness remains a difficult to treat symptom. We aimed to create a Decision Support Tool (DST) on how to respond to different levels of these patient-reported symptoms. Methods: A systematic search for guidelines and systematic reviews on these topics was conducted (in Pubmed, Cochrane and York DARE databases, Googlescholar, NICE, National Guideline Clearinghouse, Canadian Medical Association, Google.com). In a two-round online Delphi study purposefully sampled international experts (clinicians, researchers, patient representatives) judged the appropriateness (1-9 scale + do not know option) of drafted recommendations for each POS answer category (0-4) and provided qualitative remarks. Recommendations with a median of 7-9 and <30% of scores between 1-3 and 7-9 were included in the DST. Quality was assessed using an adapted GRADE approach. Results: Twenty-five out of 38 (66%) experts participated in round 1, 23 out of 37 (62%) in round 2. Higher POS scores were related to more included recommendations. The DST consists of both a manual and flow-charts of included recommendations for each topic. Overall, psychosocial interventions were recommended for lower levels of depression and breathlessness than drug interventions (e.g., goal-setting/coping versus morphine for breathlessness). Good communication and emotional support were recommended for low family anxiety levels, but a social needs assessment only for higher levels. For information needs recommendations were least discriminative; almost all recommendations (e.g., assess patients’ understanding of information, show empathy) seemed always relevant. Conclusions: The developed DST can assist clinical responses to patient-reported symptoms in palliative care. Future work is needed to test the effect of using the DST on patients’ outcomes.

scholarly journals Evaluation of ML-Based Clinical Decision Support Tool to Replace an Existing Tool in an Academic Health System: Lessons Learned

2020 ◽  
Vol 10 (3) ◽  
pp. 104
Author(s):  
Myung Woo ◽  
Brooke Alhanti ◽  
Sam Lusk ◽  
Felicia Dunston ◽  
Stephen Blackwelder ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Care ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Learning Tools ◽  
Modest Improvement ◽  
Support Tool ◽  
Clinical Decision Support Tool

There is increasing application of machine learning tools to problems in healthcare, with an ultimate goal to improve patient safety and health outcomes. When applied appropriately, machine learning tools can augment clinical care provided to patients. However, even if a model has impressive performance characteristics, prospectively evaluating and effectively implementing models into clinical care remains difficult. The primary objective of this paper is to recount our experiences and challenges in comparing a novel machine learning-based clinical decision support tool to legacy, non-machine learning tools addressing potential safety events in the hospitals and to summarize the obstacles which prevented evaluation of clinical efficacy of tools prior to widespread institutional use. We collected and compared safety events data, specifically patient falls and pressure injuries, between the standard of care approach and machine learning (ML)-based clinical decision support (CDS). Our assessment was limited to performance of the model rather than the workflow due to challenges in directly comparing both approaches. We did note a modest improvement in falls with ML-based CDS; however, it was not possible to determine that overall improvement was due to model characteristics.

scholarly journals Integrating Sustainability Goals in Innovation Processes: Applying a Decision Support Tool in a Dual-Purpose Chicken Case Study

2019 ◽  
Vol 11 (14) ◽  
pp. 3761 ◽  
Author(s):  
Felix Zoll ◽  
Katharina Diehl ◽  
Rosemarie Siebert
Keyword(s):  
Decision Support ◽  
Value Chain ◽  
Decision Support Tool ◽  
Added Value ◽  
Poultry Production ◽  
Dual Purpose ◽  
Support Tool ◽  
Support Tools ◽  
Complex Decision ◽  
Chain Organization

The innovative dual-purpose chicken approach aims at contributing to the transition towards sustainable poultry production by avoiding the culling of male chickens. To successfully integrate sustainability aspects into innovation, goal congruency among actors and clearly communicating the added value within the actor network and to consumers is needed. The challenge of identifying common sustainability goals calls for decision support tools. The objectives of our research were to investigate whether the tool could assist in improving communication and marketing with respect to sustainability and optimizing the value chain organization. Three actor groups participated in the tool application, in which quantitative and qualitative data were collected. The results showed that there were manifold sustainability goals within the innovation network, but only some goals overlapped, and the perception of their implementation also diverged. While easily marketable goals such as ‘animal welfare’ were perceived as being largely implemented, economic goals were prioritized less often, and the implementation was perceived as being rather low. By visualizing congruencies and differences in the goals, the tool helped identify fields of action, such as improved information flows and prompted thinking processes. We conclude that the tool is useful for managing complex decision processes with several actors involved.

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