Decision Support
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Klaus-Dieter Rest ◽  
Patrick Hirsch

AbstractHome health care (HHC) services are of vital importance for the health care system of many countries. Further increases in their demand must be expected and with it grows the need to sustain these services in times of disasters. Existing risk assessment tools and guides support HHC service providers to secure their services. However, they do not provide insights on interdependencies of complex systems like HHC. Causal-Loop-Diagrams (CLDs) are generated to visualize the impacts of epidemics, blackouts, heatwaves, and floods on the HHC system. CLDs help to understand the system design as well as cascading effects. Additionally, they simplify the process of identifying points of action in order to mitigate the impacts of disasters. In a case study, the course of the COVID-19 pandemic and its effects on HHC in Austria in spring 2020 are shown. A decision support system (DSS) to support the daily scheduling of HHC nurses is presented and applied to numerically analyze the impacts of the COVID-19 pandemic, using real-world data from a HHC service provider in Vienna. The DSS is based on a Tabu Search metaheuristic that specifically aims to deal with the peculiarities of urban regions. Various transport modes are considered, including time-dependent public transport.

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255261
Hee Yun Seol ◽  
Pragya Shrestha ◽  
Joy Fladager Muth ◽  
Chung-Il Wi ◽  
Sunghwan Sohn ◽  

Rationale Clinical decision support (CDS) tools leveraging electronic health records (EHRs) have been an approach for addressing challenges in asthma care but remain under-studied through clinical trials. Objectives To assess the effectiveness and efficiency of Asthma-Guidance and Prediction System (A-GPS), an Artificial Intelligence (AI)-assisted CDS tool, in optimizing asthma management through a randomized clinical trial (RCT). Methods This was a single-center pragmatic RCT with a stratified randomization design conducted for one year in the primary care pediatric practice of the Mayo Clinic, MN. Children (<18 years) diagnosed with asthma receiving care at the study site were enrolled along with their 42 primary care providers. Study subjects were stratified into three strata (based on asthma severity, asthma care status, and asthma diagnosis) and were blinded to the assigned groups. Measurements Intervention was a quarterly A-GPS report to clinicians including relevant clinical information for asthma management from EHRs and machine learning-based prediction for risk of asthma exacerbation (AE). Primary endpoint was the occurrence of AE within 1 year and secondary outcomes included time required for clinicians to review EHRs for asthma management. Main results Out of 555 participants invited to the study, 184 consented for the study and were randomized (90 in intervention and 94 in control group). Median age of 184 participants was 8.5 years. While the proportion of children with AE in both groups decreased from the baseline (P = 0.042), there was no difference in AE frequency between the two groups (12% for the intervention group vs. 15% for the control group, Odds Ratio: 0.82; 95%CI 0.374–1.96; P = 0.626) during the study period. For the secondary end points, A-GPS intervention, however, significantly reduced time for reviewing EHRs for asthma management of each participant (median: 3.5 min, IQR: 2–5), compared to usual care without A-GPS (median: 11.3 min, IQR: 6.3–15); p<0.001). Mean health care costs with 95%CI of children during the trial (compared to before the trial) in the intervention group were lower than those in the control group (-$1,036 [-$2177, $44] for the intervention group vs. +$80 [-$841, $1000] for the control group), though there was no significant difference (p = 0.12). Among those who experienced the first AE during the study period (n = 25), those in the intervention group had timelier follow up by the clinical care team compared to those in the control group but no significant difference was found (HR = 1.93; 95% CI: 0.82–1.45, P = 0.10). There was no difference in the proportion of duration when patients had well-controlled asthma during the study period between the intervention and the control groups. Conclusions While A-GPS-based intervention showed similar reduction in AE events to usual care, it might reduce clinicians’ burden for EHRs review resulting in efficient asthma management. A larger RCT is needed for further studying the findings. Trial registration Identifier: NCT02865967.

2021 ◽  
Olli Nevalainen ◽  
Olli Niemitalo ◽  
Istem Fer ◽  
Antti Juntunen ◽  
Tuomas Mattila ◽  

Abstract. Better monitoring, reporting and verification (MRV) of the amount, additionality and persistence of the sequestered soil carbon is needed to understand the best carbon farming practices for different soils and climate conditions, as well as their actual climate benefits or cost-efficiency in mitigating greenhouse gas emissions. This paper presents our Field Observatory Network (FiON) of researchers, farmers, companies and other stakeholders developing carbon farming practices. FiON has established a unified methodology towards monitoring and forecasting agricultural carbon sequestration by combining offline and near real-time field measurements, weather data, satellite imagery, modeling and computing networks. FiON’s first phase consists of two intensive research sites and 20 voluntary pilot farms testing carbon farming practices in Finland. To disseminate the data, FiON built a web-based dashboard called Field Observatory (v1.0, Field Observatory is designed as an online service for near real-time model-data synthesis, forecasting and decision support for the farmers who are able to monitor the effects of carbon farming practices. The most advanced features of the Field Observatory are visible on the Qvidja site which acts as a prototype for the most recent implementations. Overall, FiON aims to create new knowledge on agricultural soil carbon sequestration and effects of carbon farming practices, and provide an MRV tool for decision-support.

2021 ◽  
Todd Rusk ◽  
Ryan Siegel ◽  
Linda Larsen ◽  
Tim Lindsey ◽  

The Smart Energy Design Assistance Center assessed the administrative, technical, and economic aspects of feasibility related to the procurement and installation of photovoltaic solar systems on IDOT-owned buildings and lands. To address administrative feasibility, we explored three main ways in which IDOT could procure solar projects: power purchase agreement (PPA), direct purchase, and land lease development. Of the three methods, PPA and direct purchase are most applicable for IDOT. While solar development is not free of obstacles for IDOT, it is administratively feasible, and regulatory hurdles can be adequately met given suitable planning and implementation. To evaluate IDOT assets for solar feasibility, more than 1,000 IDOT sites were screened and narrowed using spatial analytic tools. A stakeholder feedback process was used to select five case study sites that allowed for a range of solar development types, from large utility-scale projects to small rooftop systems. To evaluate financial feasibility, discussions with developers and datapoints from the literature were used to create financial models. A large solar project request by IDOT can be expected to generate considerable attention from developers and potentially attractive PPA pricing that would generate immediate cash flow savings for IDOT. Procurement partnerships with other state agencies will create opportunities for even larger projects with better pricing. However, in the near term, it may be difficult for IDOT to identify small rooftop or other small on-site solar projects that are financially feasible. This project identified two especially promising solar sites so that IDOT can evaluate other solar site development opportunities in the future. This project also developed a web-based decision-support tool so IDOT can identify potential sites and develop preliminary indications of feasibility. We recommend that IDOT begin the process of developing at least one of their large sites to support solar electric power generation.

Land ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 808
Christian Sponagel ◽  
Andre Raichle ◽  
Martin Maier ◽  
Susanne Zhuber-Okrog ◽  
Ulrike Greifenhagen-Kauffmann ◽  

Many countries worldwide have developed guidelines for offsetting impacts on nature and landscape. Suitable locations are the prerequisite for the implementation of these measures, and this might lead to conflicts with agriculture. In addition, comprehensive planning is often lacking and potential added values for nature conservation are not exploited. Concepts such as the so-called production-integrated compensation (PIC) have been introduced to give farmers the opportunity to actively participate in the offsetting process and improve cooperation. However, up to now, PIC has only rarely been put into practice. Against this backdrop, we have developed a regional planning tool for the implementation of PIC in practice. Based on geodata such as soil data, agricultural structure, or natural conditions at the field and landscape level, the general suitability, and specific measure-based recommendations for each plot can be verified with the help of a decision support system. These factors are assessed from both a nature and an agricultural perspective. The goal here is to highlight synergy effects and increase the likelihood of the proposed measures being implemented. Our tool facilitates the integrated planning of biodiversity offsets at regional level. In this way, it can promote the bundling and networking of measures. However, on-site analyses should be undertaken to complement the implementation of measures.

2021 ◽  
Stephanie Chow Garbern ◽  
Eric Jorge Nelson ◽  
Sabiha Nasrin ◽  
Adama Mamby Keita ◽  
Ben J Brintz ◽  

Background: Diarrheal illness is a leading cause of antibiotic use for children in low- and middle-income countries. Determination of diarrhea etiology at the point-of-care without reliance on laboratory testing has the potential to reduce inappropriate antibiotic use. Methods: This prospective observational study aimed to develop and externally validate the accuracy of a mobile software application (App) for the prediction of viral-only etiology of acute diarrhea in children 0-59 months in Bangladesh and Mali. The App used previously derived and internally validated models using combinations of patient-intrinsic information (age, blood in stool, vomiting, breastfeeding status, and mid-upper arm circumference), pre-test odds using location-specific historical prevalence and recent patients, climate, and viral seasonality. Diarrhea etiology was determined with TaqMan Array Card using episode-specific attributable fraction (AFe) >0.5. Results: Of 302 children with acute diarrhea enrolled, 199 had etiologies above the AFe threshold. Viral-only pathogens were detected in 22% of patients in Mali and 63% in Bangladesh. Rotavirus was the most common pathogen detected (16% Mali; 60% Bangladesh). The viral seasonality model had an AUC of 0.754 (0.665-0.843) for the sites combined, with calibration-in-the-large=-0.393 (-0.455- -0.331) and calibration slope;=1.287 (1.207-1.367). By site, the pre-test odds model performed best in Mali with an AUC of 0.783 (0.705-0.86); the viral seasonality model performed best in Bangladesh with AUC 0.710 (0.595-0.825). Conclusion: The App accurately identified children with high likelihood of viral-only diarrhea etiology. Further studies to evaluate the Apps potential use in diagnostic and antimicrobial stewardship are underway.

2021 ◽  
Vol 4 (1) ◽  
pp. 26-34
Anis Nadhiroh ◽  
Dewi Agustianingsi ◽  
Diajeng Syahdania Syahdania ◽  
Dian Prasti M

Efforts Government provides social assistance in order to fulfill all the needs of the economy for the people, especially the business of Micro Small Medium Enterprises are exposed to the impact of the virus covid-19 turned out to be still considered not optimal. So many actors business who think that aid social who do not and have not been precisely targeted and The Government also recognizes the problem that, until the time of this Ministry of Social Affairs and the government is still updating the data in order to precisely target. The case is suspected to be due to data collection that is not in accordance with facts and is not real-time in each region. Inputting the data in manually in the District Paiton Probolinggo risk not the right target, the receiver doubles as well as there are elements – elements that utilize state of the. Be because the methods TOPSIS expected to be able to determine the criteria of Enterprises of Micro Small Medium Enterprises are entitled to receive the assistance of social COVID-19. Method of TOPSIS is a method that uses calculations or that provides the kinds of criteria specified which have a weight of up to the value end of the weight will be the decision final. Method of TOPSIS it refers to the benchmark Enterprises Micro Small Medium Enterprises or actors effort that deserves receive the corresponding data is relevant.

2021 ◽  
Willem B Bruin ◽  
Leif Oltedal ◽  
Hauke Bartsch ◽  
Christopher C Abbott ◽  
Miklos Argyelan ◽  

Electroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, mono-center studies indicate that both structural magnetic resonance imaging (MRI) and functional MRI biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers. Here, we used MRI data of 189 depressed patients from seven participating centers of the Global ECT-MRI Research Collaboration (GEMRIC) to develop and validate neuroimaging biomarkers for ECT outcome in a multi-center setting. We used multimodal data (i.e., clinical, structural MRI and resting-state functional MRI) and evaluated which data modalities or combinations thereof could provide the best predictions for treatment response (≥50% symptom reduction) or remission (minimal symptoms after treatment) using a support vector machine (SVM) classifier. Remission classification using a combination of gray matter volume with functional connectivity led to good performing models with 0.82-0.84 area under the curve (AUC) when trained and tested on samples coming from all centers, and remained acceptable when validated on other centers with 0.71-0.73 AUC. These results show that multimodal neuroimaging data is able to provide good prediction of remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. This suggests that these biomarkers are robust, indicating that future development of a clinical decision support tool applying these biomarkers may be feasible.

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