Dataset Information

Design and Implementation of a Cloud Computing Adoption Decision Tool: Generating a Cloud Road.

ABSTRACT: Migrating to cloud computing is one of the current enterprise challenges. This technology provides a new paradigm based on "on-demand payment" for information and communication technologies. In this sense, the small and medium enterprise is supposed to be the most interested, since initial investments are avoided and the technology allows gradual implementation. However, even if the characteristics and capacities have been widely discussed, entry into the cloud is still lacking in terms of practical, real frameworks. This paper aims at filling this gap, presenting a real tool already implemented and tested, which can be used as a cloud computing adoption decision tool. This tool uses diagnosis based on specific questions to gather the required information and subsequently provide the user with valuable information to deploy the business within the cloud, specifically in the form of Software as a Service (SaaS) solutions. This information allows the decision makers to generate their particular Cloud Road. A pilot study has been carried out with enterprises at a local level with a two-fold objective: to ascertain the degree of knowledge on cloud computing and to identify the most interesting business areas and their related tools for this technology. As expected, the results show high interest and low knowledge on this subject and the tool presented aims to readdress this mismatch, insofar as possible.

SUBMITTER: Bildosola I

PROVIDER: S-EPMC4521817 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Design and Implementation of a Cloud Computing Adoption Decision Tool: Generating a Cloud Road.

Bildosola Iñaki I Río-Belver Rosa R Cilleruelo Ernesto E Garechana Gaizka G

PloS one 20150731 7

Migrating to cloud computing is one of the current enterprise challenges. This technology provides a new paradigm based on "on-demand payment" for information and communication technologies. In this sense, the small and medium enterprise is supposed to be the most interested, since initial investments are avoided and the technology allows gradual implementation. However, even if the characteristics and capacities have been widely discussed, entry into the cloud is still lacking in terms of pract ...[more]

PMID: 26230400

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Project description:BackgroundThe emergency department is a critical juncture in the trajectory of care of patients with serious, life-limiting illness. Implementation of a clinical decision support (CDS) tool automates identification of older adults who may benefit from palliative care instead of relying upon providers to identify such patients, thus improving quality of care by assisting providers with adhering to guidelines. The Primary Palliative Care for Emergency Medicine (PRIM-ER) study aims to optimize the use of the electronic health record by creating a CDS tool to identify high risk patients most likely to benefit from primary palliative care and provide point-of-care clinical recommendations.MethodsA clinical decision support tool entitled Emergency Department Supportive Care Clinical Decision Support (Support-ED) was developed as part of an institutionally-sponsored value based medicine initiative at the Ronald O. Perelman Department of Emergency Medicine at NYU Langone Health. A multidisciplinary approach was used to develop Support-ED including: a scoping review of ED palliative care screening tools; launch of a workgroup to identify patient screening criteria and appropriate referral services; initial design and usability testing via the standard System Usability Scale questionnaire, education of the ED workforce on the Support-ED background, purpose and use, and; creation of a dashboard for monitoring and feedback.ResultsThe scoping review identified the Palliative Care and Rapid Emergency Screening (P-CaRES) survey as a validated instrument in which to adapt and apply for the creation of the CDS tool. The multidisciplinary workshops identified two primary objectives of the CDS: to identify patients with indicators of serious life limiting illness, and to assist with referrals to services such as palliative care or social work. Additionally, the iterative design process yielded three specific patient scenarios that trigger a clinical alert to fire, including: 1) when an advance care planning document was present, 2) when a patient had a previous disposition to hospice, and 3) when historical and/or current clinical data points identify a serious life-limiting illness without an advance care planning document present. Monitoring and feedback indicated a need for several modifications to improve CDS functionality.ConclusionsCDS can be an effective tool in the implementation of primary palliative care quality improvement best practices. Health systems should thoughtfully consider tailoring their CDSs in order to adapt to their unique workflows and environments. The findings of this research can assist health systems in effectively integrating a primary palliative care CDS system seamlessly into their processes of care.Trial registrationClinicalTrials.gov Identifier: NCT03424109. Registered 6 February 2018, Grant Number: AT009844-01.

Project description:OBJECTIVE:Closed-loop implantable neural stimulators are an exciting treatment option for patients with medically refractory epilepsy, with a number of new devices in or nearing clinical trials. These devices must accurately detect a variety of seizure types in order to reliably deliver therapeutic stimulation. While effective, broadly-applicable seizure detection algorithms have recently been published, these methods are too computationally intensive to be directly deployed in an implantable device. We demonstrate a strategy that couples devices to cloud computing resources in order to implement complex seizure detection methods on an implantable device platform. APPROACH:We use a sensitive gating algorithm capable of running on-board a device to identify potential seizure epochs and transmit these epochs to a cloud-based analysis platform. A precise seizure detection algorithm is then applied to the candidate epochs, leveraging cloud computing resources for accurate seizure event detection. This seizure detection strategy was developed and tested on eleven human implanted device recordings generated using the NeuroVista Seizure Advisory System. MAIN RESULTS:The gating algorithm achieved high-sensitivity detection using a small feature set as input to a linear classifier, compatible with the computational capability of next-generation implantable devices. The cloud-based precision algorithm successfully identified all seizures transmitted by the gating algorithm while significantly reducing the false positive rate. Across all subjects, this joint approach detected 99% of seizures with a false positive rate of 0.03?h-1. SIGNIFICANCE:We present a novel framework for implementing computationally intensive algorithms on human data recorded from an implanted device. By using telemetry to intelligently access cloud-based computational resources, the next generation of neuro-implantable devices will leverage sophisticated algorithms with potential to greatly improve device performance and patient outcomes.

Dataset Information

Design and Implementation of a Cloud Computing Adoption Decision Tool: Generating a Cloud Road.

Publications

Design and Implementation of a Cloud Computing Adoption Decision Tool: Generating a Cloud Road.

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