scholarly journals REAL-TIME DECISION SUPPORT TOOL INCREASED OUTPATIENT MEDICATION USE FOR MINORITY AND LOW-INCOME POPULATIONS WITH CORONARY HEART DISEASE/DIABETES: THE OFFICE-GUIDELINE APPLIED IN PRACTICE PROGRAM (OFFICE-GAP)

2012 ◽  
Vol 59 (13) ◽  
pp. E1854
Author(s):  
Venu Gourineni ◽  
Adesuwa Olomu ◽  
Steven Pierce ◽  
Nirzari Pandya ◽  
Kamesh Parashar ◽  
...  
Author(s):  
Pratima Saravanan ◽  
Michael Walker ◽  
Jessica Menold

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.


2021 ◽  
Author(s):  
Lluís Palma ◽  
Andrea Manrique ◽  
Llorenç Lledó ◽  
Andria Nicodemou ◽  
Pierre-Antoine Bretonnière ◽  
...  

<p>Under the context of the H2020 S2S4E project, industrial and research partners co-developed a fully-operational Decision Support Tool (DST) providing during 18 months near real-time subseasonal and seasonal  forecasts tailored to the specific needs of the renewable energy sector. The tool aimed to breach the last mile gap between climate information and the end-user by paying attention to the interaction with agents from the sector, already used to work with weather information, and willing to extend their forecasting horizon by incorporating climate predictions into their daily operations.</p><p>With this purpose, the tool gathered a heterogeneous dataset of seven different essential climate variables and nine energy indicators, providing for each of them bias-adjusted probabilistic information paired with a reference skill metric. To achieve this, data from state-of-the-art prediction systems and reanalysis needed to be downloaded and post-processed, fulfilling a set of quality requirements that ensure the proper functioning of the operational service. During the design, implementation, and testing phases, a wide range of scientific and technical choices had to be made, making clear the difficulties of transferring scientific research to a user-oriented real-time service. A brief showcase will be presented, exemplifying the different tools, methodologies, and best practices applied to the data workflow, together with a case study performed in Oracle’s cloud infrastructure. We expect that by making a clear description of the process and the problems encountered, we will provide a valuable experience for both, upcoming attempts of similar implementations, and the organizations providing data from climate models and reanalysis.</p>


2020 ◽  
Vol 75 (4) ◽  
pp. 524-531 ◽  
Author(s):  
Shelley L. McLeod ◽  
Joy McCarron ◽  
Tamer Ahmed ◽  
Keerat Grewal ◽  
Nicole Mittmann ◽  
...  

2013 ◽  
Vol 118 (4) ◽  
pp. 874-884 ◽  
Author(s):  
Bala G. Nair ◽  
Gene N. Peterson ◽  
Moni B. Neradilek ◽  
Shu Fang Newman ◽  
Elaine Y. Huang ◽  
...  

Abstract Background: Reduced consumption of inhalation anesthetics can be safely achieved by reducing excess fresh gas flow (FGF). In this study the authors describe the use of a real-time decision support tool to reduce excess FGF to lower, less wasteful levels. Method: The authors applied a decision support tool called the Smart Anesthesia Manager™ (University of Washington, Seattle, WA) that analyzes real-time data from an Anesthesia Information Management System to notify the anesthesia team if FGF exceeds 1 l/min. If sevoflurane consumption reached 2 minimum alveolar concentration-hour under low flow anesthesia (FGF < 2 l/min), a second message was generated to increase FGF to 2 l/min, to comply with Food and Drug Administration guidelines. To evaluate the tool, mean FGF between surgical incision and the end of procedure was compared in four phases: (1) a baseline period before instituting decision rules, (2) Intervention-1 when decision support to reduce FGF was applied, (3) Intervention-2 when the decision rule to reduce flow was deliberately inactivated, and (4) Intervention-3 when decision rules were reactivated. Results: The mean ± SD FGF reduced from 2.10 ± 1.12 l/min (n = 1,714) during baseline to 1.60 ± 1.01 l/min (n = 2,232) when decision rules were instituted (P < 0.001). When the decision rule to reduce flow was inactivated, mean FGF increased to 1.87 ± 1.15 l/min (n = 1,732) (P < 0.001), with an increasing trend in FGF of 0.1 l/min/month (P = 0.02). On reactivating the decision rules, the mean FGF came down to 1.59 ± 1.02 l/min (n = 1,845). Through the Smart Anesthesia Messenger™ system, the authors saved 9.5 l of sevoflurane, 6.0 l of desflurane, and 0.8 l isoflurane per month, translating to an annual savings of $104,916. Conclusions: Real-time notification is an effective way to reduce inhalation agent usage through decreased excess FGFs.


2021 ◽  
Vol 100 ◽  
pp. 41-64
Author(s):  
Anibal Galan ◽  
Cesar De Prada ◽  
Gloria Gutierrez ◽  
Daniel Sarabia ◽  
Rafael Gonzalez

2011 ◽  
Vol 25 (2) ◽  
pp. 227-239 ◽  
Author(s):  
Merlijn Sevenster ◽  
Rob van Ommering ◽  
Yuechen Qian

2020 ◽  
Vol 5 (11) ◽  
pp. e003587
Author(s):  
Benjamin J J McCormick ◽  
Peter Waiswa ◽  
Celia Nalwadda ◽  
Nelson K Sewankambo ◽  
Stacey L Knobler

In resource-constrained environments, priority setting is critical to making sustainable decisions for introducing new and underused vaccines and choosing among vaccine products. Donor organisations and national governments in low-income and middle-income countries (LMICs) recognise the need to support prioritisation of vaccine decisions driven by local health system capacity, epidemiology and financial sustainability.Successful efforts have supported the establishment of National Immunisation Technical Advisory Groups (NITAGs) to undertake evidence-informed decision making (EIDM) in LMICs. Now, attention is increasingly focused on supporting their function to leverage local expertise and priorities. EIDM and priority-setting functions are complex and dynamic processes. Here, we report a pilot of a web-based decision-support tool. Applying tenets of multicriteria decision analysis, SMART Vaccines 2.0 supported transparent, reproducible and evidence-informed priority setting with an easy-to-use interface and shareable outputs.The pilot was run by the Uganda NITAG who were requested by the Ministry of Health (MOH) in 2016 to produce recommendations on the prioritised introduction of five new vaccines. The tool was acceptable to the NITAG and supported their recommendations to the MOH. The tool highlighted sensitivity in the prioritisation process to the inherent biases of different stakeholders. This feature also enabled examination of the implications of data uncertainty. Feedback from users identified areas where the tool could more explicitly support evidence-to-recommendation frameworks, ultimately informing the next generation of the platform, PriorityVax.Country ownership and priority setting in vaccine decisions are central to sustainability. PriorityVax promotes auditable and rigorous deliberations; enables and captures the decision matrix of users; and generates shareable documentation of the process.


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