scholarly journals Video decision support tool promoting values conversations in advanced care planning in cancer: protocol of a randomised controlled trial

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Natasha Michael ◽  
Clare O’Callaghan ◽  
Ekavi Georgousopoulou ◽  
Adelaide Melia ◽  
Merlina Sulistio ◽  
...  
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Cancer Patients ◽  
Personal Values ◽  
Decision Support Tool ◽  
Trial Registration ◽  
Randomised Control Trial ◽  
Care Planning ◽  
Advanced Directives ◽  
Support Tool

Abstract Background Views on advance care planning (ACP) has shifted from a focus solely on treatment decisions at the end-of-life and medically orientated advanced directives to encouraging conversations on personal values and life goals, patient-caregiver communication and decision making, and family preparation. This study will evaluate the potential utility of a video decision support tool (VDST) that models values-based ACP discussions between cancer patients and their nominated caregivers to enable patients and families to achieve shared-decisions when completing ACP’s. Methods This open-label, parallel-arm, phase II randomised control trial will recruit cancer patient-caregiver dyads across a large health network. Previously used written vignettes will be converted to video vignettes using the recommended methodology. Participants will be ≥18 years and be able to complete questionnaires. Dyads will be randomised in a 1:1 ratio to a usual care (UC) or VDST group. The VDST group will watch a video of several patient-caregiver dyads communicating personal values across different cancer trajectory stages and will receive verbal and written ACP information. The UC group will receive verbal and written ACP information. Patient and caregiver data will be collected individually via an anonymous questionnaire developed for the study, pre and post the UC and VDST intervention. Our primary outcome will be ACP completion rates. Secondarily, we will compare patient-caregiver (i) attitudes towards ACP, (ii) congruence in communication, and (iii) preparation for decision-making. Conclusion We need to continue to explore innovative ways to engage cancer patients in ACP. This study will be the first VDST study to attempt to integrate values-based conversations into an ACP intervention. This pilot study’s findings will assist with further refinement of the VDST and planning for a future multisite study. Trial registration Australian New Zealand Clinical Trials Registry No: ACTRN12620001035910. Registered 12 October 2020. Retrospectively registered.

Creation of a Community-Driven Decision Support Tool for Caregivers of Children With Developmental Concerns

2021 ◽  
pp. 1-11
Author(s):  
Dawn M. Magnusson ◽  
Irena Shwayder ◽  
Natalie J. Murphy ◽  
Lindsay Ollerenshaw ◽  
Michele Ebendick ◽  
...  
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Shared Decision Making ◽  
Service Use ◽  
Decision Support Tool ◽  
Cultural Humility ◽  
Shared Decision ◽  
Plain Language ◽  
Support Tool ◽  
Key Aspects

Purpose Despite increasing standardization of developmental screening and referral processes, significant early intervention service disparities exist. The aims of this article are to: (a) describe methods used to develop a decision support tool for caregivers of children with developmental concerns, (b) summarize key aspects of the tool, and (c) share preliminary results regarding the tool's acceptability and usability among key stakeholders. Method Content and design of the decision support tool was guided by a systematic process outlined by the International Patient Decision Aid Standards (IPDAS) Collaborative. Three focus group interviews were conducted with caregivers ( n = 7), early childhood professionals ( n = 28), and a mix of caregivers and professionals ( N = 20) to assess caregiver decisional needs. In accordance with the IPDAS, a prototype of the decision support tool was iteratively cocreated by a subset of caregivers ( n = 7) and early child health professionals ( n = 5). Results The decision support tool leverages images and plain language text to guide caregivers and professionals along key steps of the early identification to service use pathway. Participants identified four themes central to shared decision making: trust, cultural humility and respect, strength-based conversations, and information-sharing. End-users found the tool to be acceptable and useful. Conclusions The decision support tool described offers an individualized approach for exploring beliefs about child development and developmental delay, considering service options within the context of the family's values, priorities, and preferences, and outlining next steps. Additional research regarding the tool's effectiveness in optimizing shared decision-making and reducing service use disparities is warranted.

Multivariable Automatisierungsentscheidungen*/Multivariate automation decisions – A tool for cost-benefit assessments as a basis for decision-making

2019 ◽  
Vol 109 (03) ◽  
pp. 134-139
Author(s):  
P. Burggräf ◽  
J. Wagner ◽  
M. Dannapfel ◽  
K. Müller ◽  
B. Koke
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Environmental Conditions ◽  
Cost Benefit ◽  
Decision Support Tool ◽  
Decision Making Process ◽  
Support Tool ◽  
Cost And Benefit ◽  
Benefit Evaluation ◽  
Level Of Automation

Der wachsende Bedarf an Wandlungsfähigkeit führt zu einer höheren Frequenz in der Umplanung von Montagesystemen und erfordert eine kontinuierliche Überprüfung und Anpassung des Automatisierungsgrades. Um auch die komplexen Umgebungsbedingungen abzubilden, sollen nicht-monetäre Faktoren in den Entscheidungsprozess eingebunden werden. Um die Entscheidung zu unterstützen, stellt dieser Beitrag ein Tool zur Identifizierung und Bewertung von Automatisierungsszenarien mittels einer Nutzwert-Aufwand-Analyse vor.   The increasing need for adaptability in assembly leads to a higher planning frequency of the system and requires continuous checks and adaptations of the appropriate level of automation. To account for the complex environmental conditions, non-monetary factors are included in the decision-making process. This paper presents a decision support tool to identify and evaluate automation scenarios by means of cost and benefit evaluation.

Predictive data analytics for contract renewals: a decision support tool for managerial decision-making

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Serhat Simsek ◽  
Abdullah Albizri ◽  
Marina Johnson ◽  
Tyler Custis ◽  
Stephan Weikert
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Organizational Performance ◽  
Predictive Analytics ◽  
Design Science ◽  
Decision Support Tool ◽  
Managerial Decision ◽  
Managerial Decision Making ◽  
Content Type ◽  
Support Tool

PurposePredictive analytics and artificial intelligence are perceived as significant drivers to improve organizational performance and managerial decision-making. Hiring employees and contract renewals are instances of managerial decision-making problems that can incur high financial costs and long-term impacts on organizational performance. The primary goal of this study is to identify the Major League Baseball (MLB) free agents who are likely to receive a contract.Design/methodology/approachThis study used the design science research paradigm and the cognitive analytics management (CAM) theory to develop the research framework. A dataset on MLB's free agents between 2013 and 2017 was collected. A decision support tool was built using artificial neural networks.FindingsThere are clear links between a player's statistical performance and the decision of the player to sign a new offered contract. “Age,” “Wins above Replacement” and “the team on which a player last played” are the most significant factors in determining if a player signs a new contract.Originality/valueThis paper applied analytical modeling to personnel decision-making using the design science paradigm and guided by CAM as the kernel theory. The study employed machine learning techniques, producing a model that predicts the probability of free agents signing a new contract. Also, a web-based tool was developed to help decision-makers in baseball front offices so they can determine which available free agents to offer contracts.

scholarly journals Development of Decision Support Tool for Evaluation of Urban Water System Metabolism Efficiency

10.29007/r6xs ◽  
2018 ◽  
Author(s):  
Vladimir Nikolic ◽  
Darko Joksimovic
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Development Process ◽  
Decision Support Tool ◽  
Integrated Approach ◽  
Water Systems ◽  
Life Cycles ◽  
Urban Water ◽  
Support Tool ◽  
Sustainable Solutions

The revitalization of Toronto’s waterfront presents the largest urban redevelopment project currently underway in North America. With respect to planning the waterfront’s urban water systems (UWS), a number of studies considered a range of criteria in search for sustainable alternatives. However, a comprehensive assessment of the integrated source-drinking-wastewater-stormwater systems over their life cycles has not been developed. According to the main postulates of the integrated approach, hybrid water systems can offer potentially more sustainable solutions than traditional centralized systems. This paper discusses the development process of a decision support tool designed to facilitate evaluation of alternatives based on UWS metabolism concept while addressing some typical challenges of hydroinformatics. This decision-making support tool analyses and compares the sustainability performance of alternative decentralized solutions against a baseline conventional approach on a neighbourhood level. The tool uses a set of criteria, adopted by the large group of stakeholders involved in the development process, that are not typically considered in the decision-making process, such as energy savings, greenhouse gas (GHG) emissions, climate change resiliency, chemical use, and nutrient recovery.

scholarly journals Establishment of CORONET; COVID-19 Risk in Oncology Evaluation Tool to identify cancer patients at low versus high risk of severe complications of COVID-19 infection upon presentation to hospital

2020 ◽  
Author(s):  
R.J. Lee ◽  
C. Zhou ◽  
O. Wysocki ◽  
R. Shotton ◽  
A. Tivey ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Decision Support ◽  
Cancer Patients ◽  
Sensitivity And Specificity ◽  
Decision Support Tool ◽  
Superior Performance ◽  
Evaluation Tool ◽  
Support Tool ◽  
Entire Cohort ◽  
Severity Prediction

AbstractBackgroundCancer patients are at increased risk of severe COVID-19. As COVID-19 presentation and outcomes are heterogeneous in cancer patients, decision-making tools for hospital admission, severity prediction and increased monitoring for early intervention are critical.ObjectiveTo identify features of COVID-19 in cancer patients predicting severe disease and build a decision-support online tool; COVID-19 Risk in Oncology Evaluation Tool (CORONET)MethodData was obtained for consecutive patients with active cancer with laboratory confirmed COVID-19 presenting in 12 hospitals throughout the United Kingdom (UK). Univariable logistic regression was performed on pre-specified features to assess their association with admission (≥24 hours inpatient), oxygen requirement and death. Multivariable logistic regression and random forest models (RFM) were compared with patients randomly split into training and validation sets. Cost function determined cut-offs were defined for admission/death using RFM. Performance was assessed by sensitivity, specificity and Brier scores (BS). The CORONET model was then assessed in the entire cohort to build the online CORONET tool.ResultsTraining and validation sets comprised 234 and 66 patients respectively with median age 69 (range 19-93), 54% males, 46% females, 71% vs 29% had solid and haematological cancers. The RFM, selected for further development, demonstrated superior performance over logistic regression with AUROC predicting admission (0.85 vs. 0.78) and death (0.76 vs. 0.72). C-reactive protein was the most important feature predicting COVID-19 severity. CORONET cut-offs for admission and mortality of 1.05 and 1.8 were established. In the training set, admission prediction sensitivity and specificity were 94.5% and 44.3% with BS 0.118; mortality sensitivity and specificity were 78.5% and 57.2% with BS 0.364. In the validation set, admission sensitivity and specificity were 90.7% and 42.9% with BS 0.148; mortality sensitivity and specificity were 92.3% and 45.8% with BS 0.442. In the entire cohort, the CORONET decision support tool recommended admission of 99% of patients requiring oxygen and of 99% of patients who died.Conclusions and RelevanceCORONET, a decision support tool validated in hospitals throughout the UK showed promise in aiding decisions regarding admission and predicting COVID-19 severity in patients with cancer presenting to hospital. Future work will validate and refine the tool in further datasets.

Developing Training Tools for Clinicians in LICs: Using Hidden Markov Modeling to Study the Decision-Making Strategies of Expert and Novice Prosthetists

2020 ◽  
Author(s):  
Pratima Saravanan ◽  
Michael Walker ◽  
Jessica Menold
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Low Income ◽  
Decision Support Tool ◽  
Low Income Countries ◽  
Controlled Study ◽  
Clear Understanding ◽  
Care Providers ◽  
Support Tool ◽  
Expert And Novice

Abstract Approximately, 40 million amputees reside in the rural parts of Low-Income Countries (LICs), and 95% of this population do not have proper access to prosthetic devices and rehabilitation services. A proper prosthetic prescription requires a clear understanding of the patient’s ambulation, goals, cultural and societal norms, locally available prosthetic materials, etc., which can be accomplished only by a local prosthetist. However, due to the lack of prosthetic schools and training centers in LICs, the rural parts lack well-trained amputee care providers. Hence there is a need to educate the prosthetists and prosthetic technicians in the LIC, specifically in the rural regions. To accomplish this, the current research proposes a decision-support tool to aid decision-making during prescription and educate prosthetists. A controlled study was conducted with expert and novice prosthetists to compare effective decision-making strategies. Results suggest that experts leverage distinct decision-making strategies when prescribing prosthetic and orthotic devices; in comparison, novices exhibited less consistent patterns of decision-making tendencies. By modeling the decision-making strategies of expert prosthetists, this work lays the foundation to develop an automated decision support tool to support decision-making for prosthetists in LICs, improving overall amputee care.

Data Mining in Decision Support for Bioenergy Production

2010 ◽  
pp. 1091-1108
Author(s):  
Nasser Ayoub ◽  
Yuji Naka
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Tool ◽  
Geographical Information ◽  
Support Tool ◽  
Bioenergy Production ◽  
Decision Making Problem ◽  
Biomass Resources

This chapter presents Data Mining, DM, as a planning and decision support tool for biomass resources management to produce bioenergy. Furthermore, the decision making problem for bioenergy production is defined. A Decision Support System, DSS that utilizes a DM technique, e.g. clustering, integrated with other group of techniques and tools, such as Genetic Algorithms, GA, Life Cycle Assessment, Geographical Information System, GIS, etc, is presented. A case study that shows how to tackle the decision making problem is also shown.

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