Leveraging Certified Nursing Assistant Documentation and Knowledge to Improve Clinical Decision Making: The On-Time Quality Improvement Program to Prevent Pressure Ulcers

2011 ◽  
Vol 24 (4) ◽  
pp. 189-190
Author(s):  
&NA;
BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e040361
Author(s):  
Amanda Klinger ◽  
Ariel Mueller ◽  
Tori Sutherland ◽  
Christophe Mpirimbanyi ◽  
Elie Nziyomaze ◽  
...  

RationaleMortality prediction scores are increasingly being evaluated in low and middle income countries (LMICs) for research comparisons, quality improvement and clinical decision-making. The modified early warning score (MEWS), quick Sequential (Sepsis-Related) Organ Failure Assessment (qSOFA), and Universal Vital Assessment (UVA) score use variables that are feasible to obtain, and have demonstrated potential to predict mortality in LMIC cohorts.ObjectiveTo determine the predictive capacity of adapted MEWS, qSOFA and UVA in a Rwandan hospital.Design, setting, participants and outcome measuresWe prospectively collected data on all adult patients admitted to a tertiary hospital in Rwanda with suspected infection over 7 months. We calculated an adapted MEWS, qSOFA and UVA score for each participant. The predictive capacity of each score was assessed including sensitivity, specificity, positive and negative predictive value, OR, area under the receiver operating curve (AUROC) and performance by underlying risk quartile.ResultsWe screened 19 178 patient days, and enrolled 647 unique patients. Median age was 35 years, and in-hospital mortality was 18.1%. The proportion of data missing for each variable ranged from 0% to 11.7%. The sensitivities and specificities of the scores were: adapted MEWS >4, 50.4% and 74.9%, respectively; qSOFA >2, 24.8% and 90.4%, respectively; and UVA >4, 28.2% and 91.1%, respectively. The scores as continuous variables demonstrated the following AUROCs: adapted MEWS 0.69 (95% CI 0.64 to 0.74), qSOFA 0.65 (95% CI 0.60 to 0.70), and UVA 0.71 (95% CI 0.66 to 0.76); there was no statistically significant difference between the discriminative capacities of the scores.ConclusionThree scores demonstrated a modest ability to predict mortality in a prospective study of inpatients with suspected infection at a Rwandan tertiary hospital. Careful consideration must be given to their adequacy before using them in research comparisons, quality improvement or clinical decision-making.


Author(s):  
Ali Sanaei ◽  
Mohammad Mehdi Sepehri

Background: Quality of Intensive care has got more attention in case of the high cost of healthcare and the potential for harm. Poor-quality care causes high cost and quality improvement initiatives in the ICU lead to an improvement in outcomes as well as a decrease in costs. One of the crucial tools that allow physicians and nurses to monitor change in a quality improvement effort is the development of an electronic database for data collection and reporting. The objective of Intensive Care Registries is to create a high-quality registry of patients through a collaboration of academic health centers performing uniform data collection with the purpose of improving the quality and accuracy of healthcare decisions and provide a data-driven clinical decision support system for critical care medicine. Methods: This article reviews real-world data sources in healthcare and considers registry as the main tool to address health services and outcomes research questions in critical care, and briefly describes objective, inputs and outputs of intensive care registries. As it can be comprehended from library research, the combination of patient clinical care data, quality parameters, and ICU operating costs, integrated into an electronic database, provides a valuable tool for quality improvement and overall efficiency of offered care. Results: Using Big Data effectively within ICUs for supporting clinical decision making can lead to predict numerous diseases and help to discover new patterns in healthcare. The ability to process multiple high-speed clinical data streams from multiple centers could dramatically improve both healthcare efficiency and patient outcomes. Conclusion: To gain this goal, developing reliable and standardized health analytics platforms as well as quality improvement processes that translate analytical results into new clinical guidelines, is recommended.


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