Assessing the impact of PACS on patient care in a medical intensive care unit

1993 ◽  
Author(s):  
Peter E. Shile ◽  
Harold L. Kundel ◽  
Sridhar B. Seshadri ◽  
Bruce Carey ◽  
Inna Brikman ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Patient Care ◽  
Medical Intensive Care Unit ◽  
Medical Intensive Care ◽  
The Impact

What Is the Role of Patient Families? An Exploratory Study in a Medical Intensive Care Unit

2017 ◽  
Vol 61 (1) ◽  
pp. 550-554
Author(s):  
Elizabeth Lerner Papautsky ◽  
Ummesalmah Abdulbaseer ◽  
Anthony Faiola
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Patient Care ◽  
Exploratory Study ◽  
Ad Hoc ◽  
Medical Intensive Care Unit ◽  
Medical Decision ◽  
Medical Intensive Care ◽  
The Impact

Patient safety and quality efforts recently began to emphasize patient and family engagement in medical decision making. With measures primarily focused on satisfaction, challenges exist around understanding the impact of engagement on patient outcomes. Research is needed to understand the role of patient/family in safety and quality across clinical environments. The current exploratory study uses observations, ad hoc interviews, and questionnaires to capture the perspective of clinicians and staff on a Medical Intensive Care Unit regarding presence and engagement of families in patient care. Findings suggest that reported roles go beyond emotional support and further investigation is needed to explore the role of families in patient care from the safety and quality perspective.

scholarly journals Blind Spots in Methods Based on Cultivation and Metagenomic Sequencing for Surface Microbiomes in a Medical Intensive Care Unit

2020 ◽  
Vol 41 (S1) ◽  
pp. s141-s142
Author(s):  
Jiaxian Shen ◽  
Alexander McFarland ◽  
Ryan Blaustein ◽  
Mary Hayden ◽  
Vincent Young ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Sensitivity And Specificity ◽  
Medical Intensive Care Unit ◽  
Metagenomic Sequencing ◽  
Whole Cell ◽  
Medical Intensive Care ◽  
The Impact ◽  
Blind Spots

Background: Cultivation of targeted pathogens has been long recognized as a gold standard for healthcare surveillance. However, there is an emergent need to characterize all viable microorganisms in healthcare facilities to understand the role that both clinical and nonclinical microorganisms play in healthcare-associated infections. Metagenomic sequencing allows detection of entire microbial communities, in contrast to targeted identification by cultivation. Widespread application of metagenomic sequencing has been impeded in part because the sensitivity and specificity are unknown, which inhibits our ability to interpret results for risk assessment. To assess the impact of sample preparation methods on sensitivity and specificity, we compared several pretreatment steps followed by metagenomic sequencing, and we performed culture-based analyses. Methods: We collected 120 surface swabs from the medical intensive care unit at Rush University Medical Center, which we aggregated to create a representative microbiome sample. We then subjected aliquots to different processing methods (DNA extraction methods, internal standard addition, propidium monoazide (PMA) treatment, and whole-cell serial filtration). We evaluated the effects of these methods based on DNA yields and metagenomic sequencing outcomes. We also compared the metagenomic results to the microbial identifications obtained by cultivation using environmental microbiology methods and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Results: Our results demonstrate that bead-beating and heat lysis followed by liquid-liquid extraction is the optimal method for the identification of low-biomass surface-associated microbes, as opposed to widely used column-based and magnetic bead-based methods. For low-biomass surface-associated samples, ~590,000 reads per sample are sufficient for ≍90% coverage in metagenomic sequencing (Fig. 1). The ZymoBIOMICS microbial community standard is not appropriate for methods assessing membrane integrity. For the identification of putatively viable microorganisms, PMA treatment is promising, although elimination of signals from nonviable organisms will reduce the overall detectable signal. Combining PMA-treated metagenomic sequencing with cultivation yields the most comprehensive results, particularly for low-abundance taxa, despite high sequencing coverage (Fig. 2). To distribute more detection resources to bacteria, our target domain, we tried whole-cell filtration prior to extraction, attempting to isolate bacterial cells from eukaryotic cells and other particles. For low-biomass surface-associated samples, the sample loss and the difficulties in performing filtration outweigh the slight increase of bacterial signal. Conclusions: Despite optimization, we observed certain blind spots in both cultivation and metagenomic sequencing. This information is essential for informed risk assessment. Further research is needed to identify additional limitations to ensure that results from metagenomic sequencing can be interpreted in the context of healthcare-acquired infection prevention.Funding: This work was supported by the Centers for Disease Control and Prevention (BAA FY2018-OADS-01 Contract 02915).Disclosures: None

Development and Implementation of a Multicomponent Protocol to Promote Sleep and Reduce Delirium in a Medical Intensive Care Unit

2021 ◽  
pp. 106002802110432
Author(s):  
Adrienne Darby ◽  
Kalynn Northam ◽  
C. Adrian Austin ◽  
Lydia Chang ◽  
Stacy Campbell-Bright
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Medical Intensive Care Unit ◽  
Severity Of Illness ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Poor Sleep ◽  
Medical Intensive Care ◽  
The Impact

Background: Evidence suggests that poor sleep increases risk of delirium. Because delirium is associated with poor outcomes, institutions have developed protocols to improve sleep in critically ill patients. Objective: To assess the impact of implementing a multicomponent sleep protocol. Methods: In this prospective, preimplementation and postimplementation evaluation, adult patients admitted to the medical intensive care unit (ICU) over 42 days were included. Outcomes evaluated included median delirium-free days, median Richards-Campbell Sleep Questionnaire (RCSQ) score, median optimal sleep nights, duration of mechanical ventilation (MV), ICU and hospital length of stay (LOS), and in-hospital mortality. Results: The preimplementation group included 78 patients and postimplementation group, 84 patients. There was no difference in median delirium-free days (1 day [interquartile range, IQR, = 0-2.5] vs 1 day [IQR = 0-2]; P = 0.48), median RCSQ score (59.4 [IQR = 43.2-71.6] vs 61.2 [IQR = 49.9-75.5]; P = 0.20), median optimal sleep nights (1 night [IQR = 0-2] vs 1 night [IQR = 0-2]; P = 0.95), and in-hospital mortality (16.7% vs 17.9%, P = 1.00). Duration of MV (8 days [IQR = 4-10] vs 4 days [IQR = 2-7]; P = 0.03) and hospital LOS (13 days [IQR = 7-22.3] vs 8 days [IQR = 6-17]; P = 0.05) were shorter in the postimplementation group, but both were similar between groups after adjusting for age and severity of illness. Conclusions and Relevance: This report demonstrates that implementation of a multicomponent sleep protocol in everyday ICU care is feasible, but limitations exist when evaluating impact on measurable outcomes. Additional evaluations are needed to identify the most meaningful interventions and best practices for quantifying impact on patient outcomes.

The impact of do-not-resuscitate order on triage decisions to a medical intensive care unit

2009 ◽  
Vol 24 (2) ◽  
pp. 311-315 ◽  
Author(s):  
Rubin I. Cohen ◽  
Gita N. Lisker ◽  
Ann Eichorn ◽  
Alan S. Multz ◽  
Alan Silver
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Medical Intensive Care Unit ◽  
Do Not Resuscitate ◽  
Medical Intensive Care ◽  
The Impact ◽  
Triage Decisions
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