Digital Decision Support Tool Research Articles

Understanding Workflow Nuances That Affect the Use of a Laboratory Test Ordering Support Tool.

The "Emergency Department Pathology Order Support Tool" (ED-POST) is an electronic laboratory test ordering decision support tool that aims to decrease variation in test ordering practices. As part of a larger project on the co-design, development, and evaluation of ED-POST, this study aimed to explore the workflow nuances that might affect the intended use of the digital decision support tool. Semi-structured, in-depth interviews were conducted with 15 ED clinicians involved in the laboratory test ordering process across the development and evaluation phases of ED-POST. Participants identified the expanded role of registered nurses in test ordering and the practice of ordering tests that are outside the ED's scope as contextual characteristics that can affect the use and perceived utility of the proposed ED-POST tool. Reconciling "work-as-imagined" with "work-as-done" in the design and development of electronic interventions is important in achieving interventions to improve the safe and effective use of pathology tests.

Applying Engagement Marketing And Human-Centered Design to Cocreate a Digital Decision Support Tool for Research Participation with LGBTQIA+ Community Members.

We used engagement marketing and human-centered design principles to cocreate a digital decision support tool for research participation with LGBTQIA+ community members to help them make an informed decision about joining the All of Us Research Program. Building on results from the research phase, we conducted eight problem validation and solutioning workshops with 48 LGBTQIA+ community members. Community members validated barriers to engagement with All of Us and brainstormed 47 potential digital solutions. We developed potential solutions into 27 concepts (descriptive text and visual storyboards) and assessed acceptability, appropriateness, feasibility, and engagement in a set of 10 concept testing workshops with 57 community members. We developed one of the highest rated concepts, the "Decide Later Tool," into a prototype and tested it with 45 LGBTQIA+ community members and 14 community advisory group members to assess acceptability, appropriateness, feasibility, usability, and engagement. Prototype testing participants indicated that the tool provides information to help with decision making, provides a clear value or benefit to them, was designed for someone like them, provides the right amount of information, and is easy to use; they also offered constructive feedback to improve it. Across the design and development phases, community members indicated that the process of engaging them demonstrated integrity, competence, dependability, trust, and collaboration; fostered a sense of connection to All of Us; and will enhance future engagement with All of Us. Our next steps are to develop the prototype into a fully functioning web tool and pilot test it in community and health care settings.

Towards Building Decision Support Tools for Older Adults at Home: A Qualitative Analysis.

Older adults (and caregivers) face important health-related decisions which can have important consequences on their well-being, independence, and outcomes (e.g., where to live, how to stay safe, where to get care). There is a critical need for tools to help them make informed decisions that reflects what is most important to them. We report on a qualitative analysis of survey data collected from home care providers to inform the design and development of digital decision support tools for older adults.

A Supervised Machine Learning Monitoring System for Vehicle-Railway Bridge Collision

Vehicle collisions on bridges is an important issue for the transportation infrastructure management. This study explores the significance of bridge monitoring and the benefits of employing machine learning (ML) techniques to detect and classify vehicle-deck collisions on railway bridges. The ultimate goal is to transition from traditional bridge monitoring methods to a real-time monitoring system based on a machine learning approach, aiming to improve efficiency and accuracy in detecting bridge issues. Multiple supervised ML algorithms are evaluated to identify the most accurate model for collision detection and signal categorization. The selected ML model employs a distributed approach, enhancing its adaptability and integration into a comprehensive monitoring system for diverse bridge structures. The dataset comprises frequency, velocity, and displacement measurements collected over a one-year monitoring period from three distinct railway bridges. Additionally, a controlled experiment was conducted to identify signal patterns associated with collisions of different energy levels. The collected data underwent rigorous processing, including data cleaning, synchronization, pattern identification, and statistical analysis, to extract relevant features. The proposed model achieved an accuracy of 100% in detecting vehicle-deck collisions on railway bridges and demonstrated high accuracy in classifying other types of signals. The model provides bridge managers with a valuable digital decision support tool that aids in evaluating bridge conditions, minimizing maintenance costs, and ensuring train user safety. Furthermore, the developed approach aids in reducing disk storage and saving energy in embedded systems, enhancing its practicality and sustainability in real-world applications.

Protocol for a trial-based economic evaluation analysis of a complex digital health intervention including a computerised decision support tool: the iFraP intervention

Background Digital health interventions (DHI) are associated with significant promise. In recent years, the need to assess the value of these healthcare technologies has motivated a debate regarding the suitability of existing economic evaluation methods in the context of DHI evaluation. Some have argued that robust economic evaluation methods may not be capable of capturing relevant DHI’s characteristics. Others consider that assessing the value of DHI might not be feasible. This protocol paper challenges that view. More specifically, it describes early Health Technology Assessments (HTA) methods to rigorously assess the value for money of a complex intervention including a digital decision support tool i.e., Improving uptake of Fracture Prevention drug treatments (iFraP) as a tracer intervention. iFraP is a complex intervention consisting of a computerised decision support tool, a clinician training package, and information resources to facilitate shared decision-making, increase informed medicine initiation and reduce levels of medicine discontinuation. iFraP’s development was motivated by a view that good quality shared decision-making conversations have the potential to improve levels of osteoporosis medicine uptake. Methods An early economic evaluation of the iFraP intervention was designed to identify, measure, and evaluate the costs and health benefits of iFraP compared to usual practice in Fracture Liaison Services (FLSs). A within-trial cost-effectiveness from the perspective of the National Health Service and Personal Social Service in England will be conducted using patient’s self-reported health related quality of life (HRQoL) and resource use from the iFraP randomised controlled trial. Microanalysis will be used to estimate iFraP’s intervention cost. Finally, Bayesian Value of Information analysis will allow us to estimate an upper bound for the potential health benefits gained from reducing uncertainty on the impact of the iFraP intervention to support uptake and adherence with osteoporosis medicines. Trial registration ISRCTN10606407 - https://doi.org/10.1186/ISRCTN10606407

Digital Antibiotic Allergy Decision Support Tool Improves Management of β-Lactam Allergies

Frontline providers frequently make time-sensitive antibiotic choices, but many feel poorly equipped to handle antibiotic allergies. We hypothesized that a digital decision support tool could improve antibiotic selection and confidence when managing β-lactam allergies. A digital decision support tool was designed to guide non-allergist providers in managing patients with β-lactam allergy labels. Non-allergists were asked to make decisions in clinical test cases without the tool, and then with it. These decisions were compared using paired t tests. Users also completed surveys assessing their confidence in managing antibiotic allergies. The tool's algorithm was validated by confirming its recommendations aligned with that of five allergists. Non-allergist providers (n= 102) made antibiotic management decisions in test cases, both with and without the tool. Use of the tool increased the proportion of correct decisions from 0.41 to 0.67, a difference of 0.26 (95% CI, 0.22-0.30; P < .001). Users were more likely to give full-dose antibiotics in low-risk situations, give challenge doses in medium-risk situations, and avoid the antibiotic and/or consult allergy departments in high-risk situations. A total of 98 users (96%) said the tool would increase their confidence when choosing antibiotics for patients with allergies. A point-of-care clinical decision tool provides allergist-designed guidance for non-allergists and is a scalable system for addressing antibiotic allergies, irrespective of allergist availability. This tool encouraged appropriate antibiotic use in low- and medium-risk situations and increased caution in high-risk situations. A digital support tool should be considered in quality improvement and antibiotic stewardship efforts.

Evaluating the MyPath web-based reproductive decision support tool in VA primary care: Protocol for a pragmatic cluster randomized trial

BackgroundCounseling to identify and support individuals' desires for family formation is a key component of preventive health care that is often absent in primary care visits. This study evaluates a novel, web-based, person-centered intervention to increase the frequency and quality of communication about reproductive goals and healthcare needs in Veterans Health Administration (VA) primary care. MethodsWe describe a hybrid type 1 effectiveness-implementation cluster randomized controlled trial in seven VA healthcare systems testing a web-based reproductive health decision support tool (MyPath). VA primary care providers are enrolled and randomized to intervention or usual care arms. Veterans scheduled to see intervention-arm providers receive a text message inviting them to use MyPath ahead of their appointment; Veterans scheduled to see control-arm providers receive usual care. Target enrollment is 36 providers and 456 Veterans. Outcomes are assessed by Veteran self-report after the visit and at 3- and 6-months follow-up. The primary outcome is occurrence of reproductive health discussions involving shared decision making; secondary outcomes include measures of communication, knowledge, decision conflict, contraceptive utilization, and receipt of services related to prepregnancy health. Data on implementation barriers, facilitators and cost are collected. ResultsThe trial is ongoing with no results to report. We have enrolled 36 primary care providers across 7 VA healthcare systems and recruitment of Veterans is ongoing. ConclusionsResults will inform efforts to increase the quality and person-centeredness of reproductive healthcare delivery in primary care and to operationalize and scale up use of digital decision support tools in clinical settings.Trial Registration:http://ClinicalTrials.gov Identifier: NCT04584294Trial Status: Recruiting.

Progress in research on site-specific nutrient management for smallholder farmers in sub-Saharan Africa.

Increasing fertilizer access and use is an essential component for improving crop production and food security in sub-Saharan Africa (SSA). However, given the heterogeneous nature of smallholder farms, fertilizer application needs to be tailored to specific farming conditions to increase yield, profitability, and nutrient use efficiency. The site-specific nutrient management (SSNM) approach initially developed in the 1990 s for generating field-specific fertilizer recommendations for rice in Asia, has also been introduced to rice, maize and cassava cropping systems in SSA. The SSNM approach has been shown to increase yield, profitability, and nutrient use efficiency. Yield gains of rice and maize with SSNM in SSA were on average 24% and 69% when compared to the farmer practice, respectively, or 11% and 4% when compared to local blanket fertilizer recommendations. However, there is need for more extensive field evaluation to quantify the broader benefits of the SSNM approach in diverse farming systems and environments. Especially for rice, the SSNM approach should be expanded to rainfed systems, which are dominant in SSA and further developed to take into account soil texture and soil water availability. Digital decision support tools such as RiceAdvice and Nutrient Expert can enable wider dissemination of locally relevant SSNM recommendations to reach large numbers of farmers at scale. One of the major limitations of the currently available SSNM decision support tools is the requirement of acquiring a significant amount of farm-specific information needed to formulate SSNM recommendations. The scaling potential of SSNM will be greatly enhanced by integration with other agronomic advisory platforms and seamless integration of digital soil, climate and crop information to improve predictions of SSNM recommendations with reduced need for on-farm data collection. Uncertainty should also be included in future solutions, primarily to also better account for varying prices and economic outcomes.

Prospective evaluation of a clinical decision support system in psychological therapy.

Thus far, most applications in precision mental health have not been evaluated prospectively. This article presents the results of a prospective randomized-controlled trial investigating the effects of a digital decision support and feedback system, which includes two components of patient-specific recommendations: (a) a clinical strategy recommendation and (b) adaptive recommendations for patients at risk for treatment failure. Therapist-patient dyads (N = 538) in a cognitive behavioral therapy outpatient clinic were randomized to either having access to a decision support system (intervention group; n = 335) or not (treatment as usual; n = 203). First, treatment strategy recommendations (problem-solving, motivation-oriented, or a mix of both strategies) for the first 10 sessions were evaluated. Second, the effect of psychometric feedback enhanced with clinical problem-solving tools on treatment outcome was investigated. The prospective evaluation showed a differential effect size of about 0.3 when therapists followed the recommended treatment strategy in the first 10 sessions. Moreover, the linear mixed models revealed therapist symptom awareness and therapist attitude and confidence as significant predictors of an outcome as well as therapist-rated usefulness of feedback as a significant moderator of the feedback-outcome and the not on track-outcome associations. However, no main effects were found for feedback. The results demonstrate the importance of prospective studies and the high-quality implementation of digital decision support tools in clinical practice. Therapists seem to be able to learn from such systems and incorporate them into their clinical practice to enhance patient outcomes, but only when implementation is successful. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Institutional continuity and hidden changes in farm advisory services provision: evidence from farmers’ microAKIS observations in France

ABSTRACT Purpose The paper aims at better understanding the micro-foundations of current institutional changes in agricultural knowledge and innovation systems (AKIS). Design A survey of 98 farmers and interviews with 37 advice providers in south-western France were conducted to analyse the ways in which farmers combine different sources of advice (microAKIS). The farmers’ practices were observed for general farm management and for 3 types of innovation (new crop diversification, digital decision support tools, and labour outsourcing). Findings The results highlight poorly-known characteristics of microAKIS regarding the variety of sources of advice used by farmers, and the limited number of reliable resources on which farmers can draw at key stages of the innovation process. They provide evidence of bottom-up mechanisms of institutional changes such as the routinization of the use of certain service providers that are often overlooked in AKIS analyses (e.g. upstream industries). Practical implications These results can contribute to reducing the misalignments of stakeholders’ representations of AKIS and microAKIS, and therefore facilitate public debates and improve the efficiency of interventions in this area. Theoretical implications Studies of institutional changes resulting from the evolution of microAKIS are expected to complement analyses of increased pluralism of advice providers. Originality Linking the observation of microAKIS and the analysis of incremental institutional changes in AKIS allows the identification of transformations of the AKIS rationale that would otherwise remain partially invisible.

Improving Nitrogen Use Efficiency—A Key for Sustainable Rice Production Systems

Fertilizer use and genetic improvement of cereal crops contributed to increased yields and greater food security in the last six decades. For rice, however, fertilizer use has outpaced improvement in yield. Excess application of nutrients beyond crop needs, especially nitrogen (N), is associated with losses to the environment. Environmental pollution can be mitigated by addressing fertilizer overuse, improving N use efficiency, while maintaining or improving rice productivity and farmers' income. A promising approach is the site-specific nutrient management (SSNM), developed in the 1990s to optimize supply to meet demand of nutrients, initially for rice, but now extended to other crops. The SSNM approach has been further refined with the development of digital decision support tools such as Rice Crop Manager, Nutrient Expert, and RiceAdvice. This enables more farmers to benefit from SSNM recommendations. In this mini-review, we show how SSNM can foster sustainability in rice production systems through improved rice yields, profit, and N use efficiency while reducing N losses. Farmer adoption of SSNM, however, remains low. National policies and incentives, financial investments, and strengthened extension systems are needed to enhance scaling of SSNM-based decision support tools.

Implementing an innovative integrated multi-infection screening digital decision support tool (IS-MiHealth) in primary care: lessons from Spain

Abstract Dr Requena will share new data from a pilot randomised controlled trial of a novel integrated multi-infection screening digital decision support tool, to promote screening for multiple infections in primary care in Spain among presenting migrants.

Co-benefits of nutrient management tailored to smallholder agriculture

Plant nutrition plays a central role in the global challenges to produce sufficient and nutritious food, lessen rural poverty, and reducing the environmental footprint of farming. Site-specific nutrient management (SSNM) provides field-specific solutions for smallholder farmers, potentially creating co-benefits of increased productivity and sustainability. Here we perform the first meta-analysis comparing SSNM with farmers’ fertilizer practice for maize, rice and wheat using 61 published papers across 11 countries. Relative to the farmer practice, across all crops SSNM increased grain yield by 12% and profitability by 15% with 10% less fertilizer nitrogen applied, thereby improving nitrogen use efficiency and reducing nitrogen pollution to the environment. Delivering it to millions of smallholder farmers requires use of digital decision support tools, but also policy incentives, links with financial and input supply services, and enhancing public-private partnerships.

\u201cI must, and I can live with that\u201d: a thematic analysis of patients\u2019 perspectives on polypharmacy and a digital decision support system for GPs

BackgroundTo investigate patients’ perspectives on polypharmacy and the use of a digital decision support system to assist general practitioners (GPs) in performing medication reviews.MethodsQualitative interviews with patients or informal caregivers recruited from participants in a cluster-randomized controlled clinical trial (cRCT). The interviews were transcribed verbatim and analyzed using thematic analysis.ResultsWe conducted 13 interviews and identified the following seven themes: the patients successfully integrated medication use in their everyday lives, used medication plans, had both good and bad personal experiences with their drugs, regarded their healthcare providers as the main source of medication-related information, discussed medication changes with their GPs, had trusting relationships with them, and viewed the use of digital decision support tools for medication reviews positively. No unwanted adverse effects were reported.ConclusionsDespite drug-related problems, patients appeared to cope well with their medications. They also trusted their GPs, despite acknowledging polypharmacy to be a complex field for them. The use of a digital support system was appreciated and linked to the hope that reasons for selecting specific medication regimens would become more comprehensible. Further research with a more diverse sampling might add more patient perspectives.Trial registrationClinicalTrials.gov, NCT03430336. Registered on February 6, 2018.

Digital extension, price risk, and farm performance: experimental evidence from Nigeria

AbstractDespite decades of investment in agricultural extension, technology adoption among farmers and agricultural productivity growth in Sub‐Saharan Africa remain slow. Among other shortcomings, extension systems often make recommendations that do not account for price risk or spatial heterogeneity in farmers' growing conditions. However, little is known about the effectiveness of extension approaches for nutrient management that consider these issues. We analyze the impact of farmers' access to site‐specific nutrient management recommendations and to information on expected returns, provided through a digital decision support tool, for maize production. We implement a randomized controlled trial among smallholders in the maize belt of northern Nigeria. We use three waves of annual panel data to estimate immediate and longer term effects of two different extension treatments: site‐specific recommendations with and without complementary information about variability in output prices and expected returns. We find that site‐specific nutrient management recommendations improve fertilizer management practices and maize yields but do not necessarily increase fertilizer use. In addition, we find that recommendations that are accompanied by additional information about variability in expected returns induce larger fertilizer investments that persist beyond the first year. However, the magnitudes of these effects are small: we find only incremental increases in investments and net revenues over two treatment years.

Advanced Solid State Nano-Electrochemical Sensors and System for Agri 4.0 Applications.

Global food production needs to increase in order to meet the demands of an ever growing global population. As resources are finite, the most feasible way to meet this demand is to minimize losses and improve efficiency. Regular monitoring of factors like animal health, soil and water quality for example, can ensure that the resources are being used to their maximum efficiency. Existing monitoring techniques however have limitations, such as portability, turnaround time and requirement for additional reagents. In this work, we explore the use of micro- and nano-scale electrode devices, for the development of an electrochemical sensing platform to digitalize a wide range of applications within the agri-food sector. With this platform, we demonstrate the direct electrochemical detection of pesticides, specifically clothianidin and imidacloprid, with detection limits of 0.22 ng/mL and 2.14 ng/mL respectively, and nitrates with a detection limit of 0.2 µM. In addition, interdigitated electrode structures also enable an in-situ pH control technique to mitigate pH as an interference and modify analyte response. This technique is applied to the analysis of monochloramine, a common water disinfectant. Concerning biosensing, the sensors are modified with bio-molecular probes for the detection of both bovine viral diarrhea virus species and antibodies, over a range of 1 ng/mL to 10 µg/mL. Finally, a portable analogue front end electronic reader is developed to allow portable sensing, with control and readout undertaken using a smart phone application. Finally, the sensor chip platform is integrated with these electronics to provide a fully functional end-to-end smart sensor system compatible with emerging Agri-Food digital decision support tools.

Analysis for the Estimation of Harumanis Mango Ripeness Guide

Harumanis mango ripeness guide is hardly to reach to predict the ripening stages in a such this emerging artificial intelligence commencing technology. The use of digital support tool for selective fruits in predicting the ripening stages should be subdued, exercising to be accessible by directive users. Having those lacks, this preliminary project is a first step to analyse the ripeness stages of harumanis mango referring to firmness, pulp colours and total soluble sugar (TSS) for digitalization purposes. Twenty-five harumanis mangoes harvested at week tenth were used, which had an average of mass for 417.96 ± 163.24 g. Five samples randomly selected in each stage by settling them under a room temperature and two days interval period. Findings showed the lowest TSS content uncovered at stage 2 for 6.94 Brix and the highest found at stage 5 for 15.02 Brix. The highest firmness unfolded in stage 2 with value of 2.902 kgf and the lowest discovered in stage 5 for 0.810 kgf. The pulp colours showed reduction of blue values for 70, activated at stage 3. The results suggested that harumanis mango started deteriorating after six days of room temperature storing period, this followed by rapid degradation of firmness and increasing of TSS value. Moreover, combinations colour values of red, green and blue composed constructive predictive yellowish variants throughout stages, positively useable to development of digital decision support tool.

Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data.

Continuous Glucose Monitoring (CGM) has enabled important opportunities for diabetes management. This study explores the use of CGM data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction and compare the RNNs to conventional time-series forecasting using Autoregressive Integrated Moving Average (ARIMA). A prediction horizon up to 90 min into the future is considered. In this context, we evaluate both population-based and patient-specific RNNs and contrast them to patient-specific ARIMA models and a simple baseline predicting future observations as the last observed. We find that the population-based RNN model is the best performing model across the considered prediction horizons without the need of patient-specific data. This demonstrates the potential of RNNs for STBG prediction in diabetes patients towards detecting/mitigating severe events in the STBG, in particular hypoglycemic events. However, further studies are needed in regards to the robustness and practical use of the investigated STBG prediction models.

Supporting informed clinical trial decisions: Results from a randomized controlled trial evaluating a digital decision support tool for those with intellectual disability.

BackgroundInformed consent requires that individuals understand the nature of the study, risks and benefits of participation. Individuals with intellectual disabilities (ID) have cognitive and adaptive impairments that may affect their ability to provide informed consent. New treatments and clinical trials for fragile X syndrome, the most commonly known inherited cause of ID, necessitate the development of methods to improve the informed consent process. The goal of this study was to compare the efficacy of a digital decision support tool with that of standard practice for informed consent and to examine whether the tool can improve decisional capacity for higher functioning individuals.MethodsParticipants (N = 89; mean age = 21.2 years) were allocated to the experimental group (consenting information provided via the digital decision support tool), or the comparison group (information provided via standard practice). Participants were assessed on four aspects of decisional capacity (Understanding, Appreciating, Reasoning, and Expressing a choice). We used regression analyses to test the impact of the tool on each outcome, repeating the analyses on the higher functioning subsample.ResultsNo differences existed in any domain of decisional capacity for the sample in full. However, participants in the higher IQ subsample who used the tool scored better on Understanding after adjustment (β = 0.25, p = 0.04), but not on Appreciating or Reasoning. No differences by experimental group existed in the decision to join the hypothetical trial for the full sample or higher functioning subsample.ConclusionsA decision support tool shows promise for individuals with fragile X syndrome with higher cognitive abilities. Future studies should examine the level of cognitive ability needed for sufficient understanding, whether these findings can be translated to other clinical populations, and the impact of the tool in larger trials and on trial retention.

Development of a Digital Decision Support Tool to Aid Participation of Children With Disabilities in Pediatric Rehabilitation Services: Explorative Qualitative Study.

BackgroundBuilding a health care system in accordance with the rule of law requires child-centered care, where children and young people, regardless of ability, are allowed to participate in visits with their health care professionals. As part of an overall project focusing on developing and implementing a digital decision support tool to increase the participation of children with disabilities in pediatric rehabilitation, this study brings new knowledge as to how this specific patient group views participation.ObjectiveThe aim of this formative study was to explore the experiences of children and young people with disabilities concerning increasing their participation in the pediatric rehabilitation services.MethodsThe formative study had an explorative design, based on a latent qualitative content analysis with an inductive approach. Interviews were conducted with 20 children (6-17 years) and 8 young people (19-30 years) with disabilities about their experiences of participation in pediatric rehabilitation services.ResultsA total of 3 categories emerged reflecting the participants’ possibilities of participation in the pediatric rehabilitation services: to feel involved, to feel independent, and to work in partnership. To feel involved meant being listened to and being connected, to feel independent meant being admitted and being enabled, and to work in partnership meant being supported and being able to entrust others with the decision making. With the overall theme moving toward empowerment of children in pediatric rehabilitation, a true feeling of participation can be experienced.ConclusionsThe views of children and young people with disabilities are that children should be given the prerequisites for empowerment by being allowed to feel involved and independent as well as to work in partnership to experience true participation in the pediatric rehabilitation services. This finding is essential in the design of a digital decision support tool based on the children’s needs and perspectives.