scholarly journals Bridging Science and Practice-Importance of Stakeholders in the Development of Decision Support: Lessons Learned

2021 ◽  
Vol 13 (10) ◽  
pp. 5744
Author(s):  
Innocent K. Tumwebaze ◽  
Joan B. Rose ◽  
Nynke Hofstra ◽  
Matthew E. Verbyla ◽  
Daniel A. Okaali ◽  
...  
Keyword(s):  
Decision Support ◽  
Stakeholder Participation ◽  
Scientific Data ◽  
Lessons Learned ◽  
Future Research ◽  
Decision Support Tools ◽  
Local Data ◽  
Funding Sources ◽  
Sanitation And Hygiene ◽  
Support Tools

User-friendly, evidence-based scientific tools to support sanitation decisions are still limited in the water, sanitation and hygiene (WASH) sector. This commentary provides lessons learned from the development of two sanitation decision support tools developed in collaboration with stakeholders in Uganda. We engaged with stakeholders in a variety of ways to effectively obtain their input in the development of the decision support tools. Key lessons learned included: tailoring tools to stakeholder decision-making needs; simplifying the tools as much as possible for ease of application and use; creating an enabling environment that allows active stakeholder participation; having a dedicated and responsive team to plan and execute stakeholder engagement activities; involving stakeholders early in the process; having funding sources that are flexible and long-term; and including resources for the acquisition of local data. This reflection provides benchmarks for future research and the development of tools that utilize scientific data and emphasizes the importance of engaging with stakeholders in the development process.

Decision support tools for oil spill response (OSR-DSTs): Approaches, challenges, and future research perspectives

2021 ◽  
Vol 167 ◽  
pp. 112313
Author(s):  
Zhaoyang Yang ◽  
Zhi Chen ◽  
Kenneth Lee ◽  
Edward Owens ◽  
Michel C. Boufadel ◽  
...  
Keyword(s):  
Decision Support ◽  
Oil Spill ◽  
Future Research ◽  
Decision Support Tools ◽  
Research Perspectives ◽  
Support Tools ◽  
Oil Spill Response

Crime linkage practice in New Zealand

2021 ◽  
Vol 7 (1) ◽  
pp. 63-76
Author(s):  
Matt Tonkin ◽  
Martin Joseph Weeks
Keyword(s):  
Decision Support ◽  
New Zealand ◽  
Future Research ◽  
Decision Support Tools ◽  
Residential Burglary ◽  
Content Type ◽  
Number Of Factors ◽  
Support Tools ◽  
Crime Linkage ◽  
Court Proceedings

Purpose The purpose of this paper is to understand (i) how crime linkage is currently performed with residential burglaries in New Zealand, (ii) the factors that promote/hinder accurate crime linkage and (iii)whether computerised decision-support tools might assist crime linkage practice. Design/methodology/approach A total of 39 New Zealand Police staff completed a questionnaire/interview/focus group relating to the process, challenges, products and uses of crime linkage with residential burglary in New Zealand. These data (alongside four redacted crime linkage reports) were subjected to thematic analysis. Findings The data clearly indicated wide variation in crime linkage process, methods and products (Theme 1). Furthermore, a number of factors were identified that impacted on crime linkage practice (Theme 2). Research limitations/implications Future research should develop computerised crime linkage decision-support tools and evaluate their ability to enhance crime linkage practice. Also, researchers should explore the use of crime linkage in court proceedings. Practical implications To overcome the barriers identified in the current study, greater training in and understanding of crime linkage is needed. Moreover, efforts to enhance the quality of crime data recorded by the police will only serve to enhance crime linkage practice. Social implications By enhancing crime linkage practice, opportunities to reduce crime, protect the public and deliver justice for victims will be maximised. Originality/value The practice of crime linkage is under-researched, which makes it difficult to determine if/how existing empirical research can be used to support ongoing police investigations. The current project fills that gap by providing a national overview of crime linkage practice in New Zealand, a country where crime linkage is regularly conducted by the police, but no published linkage research exists.

scholarly journals P122: The use of decision support tools in the implementation of the Prehospital Canadian Triage Acuity Score (Pre-CTAS)

CJEM ◽  
2016 ◽  
Vol 18 (S1) ◽  
pp. S119-S119
Author(s):  
K.E. Smith ◽  
K. Lobay ◽  
M. Bullard
Keyword(s):  
Decision Support ◽  
Point Of Care ◽  
Clinical Care ◽  
Future Research ◽  
Decision Support Tools ◽  
Triage Nurse ◽  
Kappa Score ◽  
Rater Reliability ◽  
Support Tools ◽  
The Impact

Introduction: The Prehospital Canadian Triage and Acuity Scale (Pre-CTAS) is based upon, and is consistent with, the CTAS (Canadian Triage and Acuity Scale). Paramedic-assigned Pre-CTAS scores using memory compared to Triage Nurse CTAS scores have previously demonstrated moderate inter-rater reliability. This is the first study to measure the effect of two different point-of-care decision support tools on the inter-rater reliability of paramedic assigned Pre-CTAS and Triage nurse CTAS scores. Methods: Paramedics were randomized to Pre-CTAS booklet or CTAS smartphone app during the one-year study period. Pre-CTAS scores assigned on arrival at hospital (AH) were compared with Triage Nurse CTAS scores and analyzed using Cohen’s Kappa. Paramedics were then surveyed to assess the perceived utility and satisfaction with the decision support tools. Results: For 1663 patient transports, the weighted kappa score for Paramedic AH vs. Triage Nurse CTAS was fair at 0.38 (95% CI 0.35-0.41). For patients whose initial on-scene and AH Pre-CTAS scores did not change (n= 1405, 85%), Paramedic-Triage Nurse agreement was moderate at 0.43 (95% CI 0.39-0.46). The survey revealed that tools, when employed, helped assign scores; however accessing the additional resource was cumbersome or time consuming, and scores were occasionally assigned post clinical encounter. Conclusion: Point-of-care external decision support tools did not affect Pre-CTAS and ED CTAS agreement. These tools may add complexity or be perceived to add time to documentation within the delivery of clinical care if not implemented with adequate support. Future research needs to evaluate the impact of clinical decision support embedded within an electronic patient care record consistent with many ED information systems.

scholarly journals Ten Simple Rules for Researchers Who Want to Develop Web Apps

2021 ◽  
Author(s):  
Sheila Saia ◽  
Natalie Nelson ◽  
Sierra Young ◽  
Stanton Parham ◽  
Micah Vandegrift
Keyword(s):  
Decision Support ◽  
Web Applications ◽  
Decision Support Tool ◽  
Lessons Learned ◽  
Decision Support Tools ◽  
Support Tool ◽  
Support Tools ◽  
Simple Rules ◽  
Web App ◽  
Scenario Testing

Growing interest in data-driven, decision-support tools across the life sciences and physical sciences has motivated development of web applications, also known as web apps. Web apps can help disseminate research findings and present research outputs in ways that are more accessible and meaningful to the general public--from individuals, to governments, to companies. Specifically, web apps enable exploration of scenario testing and policy analysis (i.e., to answer “what if?”) as well as co-evolution of scientific and public knowledge. However, the majority of researchers developing web apps receive little formal training or technical guidance on how to develop and evaluate the effectiveness of their web-based decision support tools. Take some of us for example. We (Saia and Nelson) are agricultural and environmental engineers with little experience in web app development, but we are interested in creating web apps to support sustainable aquaculture production in the Southeast. We had user (i.e., shellfish growers) interest, a goal in mind (i.e., develop a new forecast product and decision-support tool for shellfish aquaculturalists), and received funding to support this work. Yet, we experienced several unexpected hurdles from the start of our project that ended up being fairly common hiccups to the seasoned web app developers among us (Young, Parham). As a result, we share the following Ten Simple Rules, which highlight take home messages, including lessons learned and practical tips, of our experience as burgeoning web app developers. We hope researchers interested in developing web apps draw insights from our (in)experience as they set out on their decision support tool development journey.

scholarly journals Developing a Bayesian Network Decision Support tool for low back pain: a pilot and protocol (Preprint)

2020 ◽  
Author(s):  
Adele Hill ◽  
Christopher H Joyner ◽  
Chloe Keith-Jopp ◽  
Barbaros Yet ◽  
Ceren Tuncer Sakar ◽  
...  
Keyword(s):  
Low Back Pain ◽  
Decision Support ◽  
Bayesian Network ◽  
Clinical Reasoning ◽  
Lessons Learned ◽  
Decision Support Tools ◽  
Low Back ◽  
Face To Face ◽  
Internal Pilot ◽  
Support Tools

BACKGROUND Low back pain (LBP) is an increasingly burdensome condition for patients and health professionals alike, with increasing persistent pain and disability consistently demonstrated. Previous decision support tools for LBP management have focussed on a subset of factors due to time constraints and ease of use for the clinician. With the explosion of interest in machine learning tools and the commitment from Western governments to introduce this technology, there are opportunities to develop intelligent decision support tools. We will do this for LBP using a Bayesian Network, which will entail constructing a clinical reasoning model elicited from experts. OBJECTIVE This paper proposes a method for conducting a modified RAND Appropriateness procedure to elicit the knowledge required to construct a Bayesian Network from a group of domain experts in LBP, and reports the lessons learned from the internal pilot of the procedure. METHODS We propose to recruit expert clinicians with a special interest in LBP from across a range of medical specialisms e.g. orthopaedics, rheumatology, sports medicine. The procedure will consist of four stages. Stage 1 is an online elicitation of variables to be considered by the model, followed by a face to face workshop. Stage 2 is an online elicitation of the structure of the model, followed by a face to face workshop. Stage 3 consists of an online phase to elicit probabilities to populate the Bayesian Network. Stage 4 is a rudimentary validation of the Bayesian Network. RESULTS Ethical approval has been gained from the Research Ethics Committee at Queen Mary University of London. An internal pilot of the procedure has been run with clinical colleagues from the research team. This showed that an alternating process of 3 remote activities and 2 in-person meetings were required to complete the elicitation without overburdening participants. Lessons learned have included the need for a bespoke, online elicitation tool to run between face-to-face meetings and for careful operational definition of descriptive terms even if widely clinically used. Further, tools are required to remotely deliver training about self-identification of various forms of cognitive bias and explain the underlying principles of a BN. The use of the internal pilot was recognised as being a methodological necessity. CONCLUSIONS We have proposed a method to construct Bayesian Networks that are representative of expert clinical reasoning, in this case for a musculoskeletal condition. We have tested the method with an internal pilot to refine the process prior to deployment, which indicates the process can be successful. The internal pilot has also revealed the software support requirements for the elicitation process, in order that clinical reasoning can be modelled for a range of conditions. CLINICALTRIAL

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