Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Ismail Sensors21 21-11.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Mrazek Diagnostics21 11-7.png|240px]]</div>
'''"[[Journal:A scoping review of integrated blockchain-cloud architecture for healthcare: Applications, challenges, and solutions|A scoping review of integrated blockchain-cloud architecture for healthcare: Applications, challenges, and solutions]]"'''
'''"[[Journal:Laboratory demand management strategies: An overview|Laboratory demand management strategies: An overview]]"'''


[[Blockchain]] is a disruptive technology for shaping the next era of healthcare systems striving for efficient and effective patient care. This is thanks to its peer-to-peer, secure, and transparent characteristics. On the other hand, [[cloud computing]] made its way into the healthcare system thanks to its elasticity and cost-effective nature. However, cloud-based systems fail to provide a secured and private patient-centric cohesive view to multiple healthcare stakeholders. In this situation, blockchain provides solutions to address [[Cybersecurity|security]] and privacy concerns of the cloud because of its decentralization feature combined with [[Information security|data security]] and [[Information privacy|privacy]], while cloud provides solutions to the blockchain scalability and efficiency challenges. Therefore a novel paradigm of blockchain-cloud integration (BcC) emerges for the domain of healthcare ... ('''[[Journal:A scoping review of integrated blockchain-cloud architecture for healthcare: Applications, challenges, and solutions|Full article...]]''')<br />
Inappropriate [[laboratory]] test selection in the form of overutilization as well as underutilization, frequently occurs despite available guidelines. There is broad approval among laboratory specialists and clinicians that [[demand management]] (DM) strategies are useful tools to avoid this issue. Most of these tools are based on automated algorithms or other types of [[machine learning]]. This review summarizes the available DM strategies that may be adopted to local settings. We believe that [[artificial intelligence]] (AI) may help to further improve these available tools. ('''[[Journal:Laboratory demand management strategies: An overview|Full article...]]''')<br />
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''Recently featured'':
''Recently featured'':
{{flowlist |
{{flowlist |
* [[Journal:A scoping review of integrated blockchain-cloud architecture for healthcare: Applications, challenges, and solutions|A scoping review of integrated blockchain-cloud architecture for healthcare: Applications, challenges, and solutions]]
* [[Journal:Laboratory information management system for the biosafety laboratory: Safety and efficiency|Laboratory information management system for the biosafety laboratory: Safety and efficiency]]
* [[Journal:Laboratory information management system for the biosafety laboratory: Safety and efficiency|Laboratory information management system for the biosafety laboratory: Safety and efficiency]]
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}}
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Revision as of 15:57, 4 July 2022

Fig1 Mrazek Diagnostics21 11-7.png

"Laboratory demand management strategies: An overview"

Inappropriate laboratory test selection in the form of overutilization as well as underutilization, frequently occurs despite available guidelines. There is broad approval among laboratory specialists and clinicians that demand management (DM) strategies are useful tools to avoid this issue. Most of these tools are based on automated algorithms or other types of machine learning. This review summarizes the available DM strategies that may be adopted to local settings. We believe that artificial intelligence (AI) may help to further improve these available tools. (Full article...)

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