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10.5455/aim.2020.28.209-213 http://scihub22266oqcxt.onion/10.5455/aim.2020.28.209-213 33417660!7780821!33417660     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Acta+Inform+Med 2020 ; 28 (3): 209-213 Nephropedia Template TP {{tp|p=33417660|t=2020. Rapid Development and Utilization of a Clinical Intelligence Dashboard for Frontline Clinicians to Optimize Critical Resources During Covid-19 |pdf=|usr=}}{{33417660}} gab.com Text Rapid Development and Utilization of a Clinical Intelligence Dashboard for Frontline Clinicians to Optimize Critical Resources During Covid-19 JO: Acta Inform Med http://www.kidney.de/pget.php?&tw=1&pmid=33417660 #FOAvip INTRODUCTION: The COVID-19 pandemic has created an unprecedented situation where sudden and prolonged surges of critically ill patients have disrupted healthcare systems worldwide A major concern for hospitals worldwide is how to best manage large numbers of COVID-19 infected and non-infected patients, while still maintaining high-quality clinical care. AIM: This manuscript describes the system development, collaborative efforts and the challenges encountered in developing an in-house clinical intelligence dashboard. METHODS: Through a longitudinal, interdepartmental collaboration, a COVID-19 clinical intelligence dashboard was created using Microsoft Power BI and Cerner Computer Language (CCL) to demonstrate clinical severity of patients and patient location in a single screen. A color-coding schema was applied to produce a red highlight for patients whose condition is deteriorating, whether due to increasing oxygen demand or worsening laboratory values. An additional function enabled users to drill down into the patient's clinical and laboratory parameters for the past 5 days, and ultimately to the respective patient chart for further assessment. RESULTS: The development of an in-house clinical intelligence dashboard is a feasible, effective tool to allow frontline clinicians to monitor patient status in multiple wards and proactively intervene as clinically necessary and transfer patients to the appropriate level of care. Comparing the 30 days before and 30 days after the implementation of the dashboard, the percentage of patients who required urgent intubation or cardiac resuscitation on the general medical ward, rather than a critical care setting, declined by over 50% (8 out of 34, 33% vs. 7 out of 55, 13%; two-tailed p < 0.05 by Fisher's exact test; OR 3.43; CI 1.07 to 10.95). CONCLUSION: The dashboard has enabled physicians to efficiently assess patient volumes and case severity to prioritize clinical care and appropriately allocate scarce resources. The dashboard can be replicated by developing healthcare systems that are continuing to grapple with the pandemic. Twit Text FOAVip Rapid Development and Utilization of a Clinical Intelligence Dashboard for Frontline Clinicians to Optimize Critical Resources During Covid-19 ????Acta Inform Med http://www.kidney.de/pget.php?&tw=1&pmid=33417660 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33417660 #FOAvip English Wikipedia <ref>{{Cite pmid|33417660}}</ref> Rapid Development and Utilization of a Clinical Intelligence Dashboard for Frontline Clinicians to Optimize Critical Resources During Covid-19 #MMPMID33417660 Ibrahim H; Sorrell S; Nair SC; Al Romaithi A; Al Mazrouei S; Kamour A Acta Inform Med 2020[Sep]; 28 (3): 209-213 PMID33417660show ga INTRODUCTION: The COVID-19 pandemic has created an unprecedented situation where sudden and prolonged surges of critically ill patients have disrupted healthcare systems worldwide A major concern for hospitals worldwide is how to best manage large numbers of COVID-19 infected and non-infected patients, while still maintaining high-quality clinical care. AIM: This manuscript describes the system development, collaborative efforts and the challenges encountered in developing an in-house clinical intelligence dashboard. METHODS: Through a longitudinal, interdepartmental collaboration, a COVID-19 clinical intelligence dashboard was created using Microsoft Power BI and Cerner Computer Language (CCL) to demonstrate clinical severity of patients and patient location in a single screen. A color-coding schema was applied to produce a red highlight for patients whose condition is deteriorating, whether due to increasing oxygen demand or worsening laboratory values. An additional function enabled users to drill down into the patient's clinical and laboratory parameters for the past 5 days, and ultimately to the respective patient chart for further assessment. RESULTS: The development of an in-house clinical intelligence dashboard is a feasible, effective tool to allow frontline clinicians to monitor patient status in multiple wards and proactively intervene as clinically necessary and transfer patients to the appropriate level of care. Comparing the 30 days before and 30 days after the implementation of the dashboard, the percentage of patients who required urgent intubation or cardiac resuscitation on the general medical ward, rather than a critical care setting, declined by over 50% (8 out of 34, 33% vs. 7 out of 55, 13%; two-tailed p < 0.05 by Fisher's exact test; OR 3.43; CI 1.07 to 10.95). CONCLUSION: The dashboard has enabled physicians to efficiently assess patient volumes and case severity to prioritize clinical care and appropriately allocate scarce resources. The dashboard can be replicated by developing healthcare systems that are continuing to grapple with the pandemic. äRapid Development and Utilization of a Clinical Intelligence Dashboard(epmc).pdf DeepDyve Pubget Overpricing