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Objectives: To determine whether undertriage affects the outcome for patients requiring direct admission to the intensive care unit (ICU) from the emergency department due to a medical condition. To identify factors associated with undertriage., Material and Methods: Retrospective review of patients treated in 2018 for medical emergencies requiring direct admission to the ICU from the emergency department. The cases were classified in 2 groups according to the assigned triage level. Underestimation was defined as a triage level of III or more. Independent variables were demographic and epidemiologic data and indicators of severity recorded in the emergency department. The main outcome was 30-day mortality. Secondary outcomes were in-hospital mortality, prolonged ICU stay, and prolonged hospital stay., Results: We included 470 patients with a median age of 68 years (first-third quartile range, 57-78 years); 61.1% were men, and 151 (32.1%) were undertriaged. Factors directly related to undertriage according to odds ratios (ORs) were age (OR = 1.017; 95% CI, 1.003-1.032), Quick Sequential Organ Failure Assessment score of 0 or 1 (OR = 1.761; 95% CI, 1.038-2.988), ratio of oxygen saturation to fraction of inspired oxygen greater than 300 (OR = 2.447; 95% CI, 1.418-4.223), a diagnosis of infection (OR = 5.003, 95% CI 2.727-9.188) whether respiratory (OR = 3.993, 95% CI 1.919-8.310) or other (OR = 1.980, 95% CI, 1.036-3.785) versus a diagnosis of cardiovascular disease. Factors inversely related to undertriage were admission during the afternoon (OR = 0.512, 95% CI, 0.306-0.856) and ambulance transfer (OR = 0.373; 95% CI, 0.232-0.600). Ninety patients (19.1%) died within 30 days. Undertriage was not related to 30-day mortality or the other outcomes analyzed., Conclusion: Undertriage was not associated with a worse outcome in patients requiring direct admission to the ICU for a medical emergency. The factors we found to be associated with undertriage, such as patient age and time of day admitted, merit special attention given that these factors should not be affecting the triage process.

Background: There are limited data on human immunodeficiency virus (HIV) quality indicators according to model of HIV care delivery. Comparing HIV quality indicators by HIV care model could help inform best practices because patients achieving higher levels of quality indicators may have a mortality benefit., Methods: Using the Partners HIV Cohort, we categorized 1565 patients into 3 HIV care models: infectious disease provider only (ID), generalist only (generalist), or infectious disease provider and generalist (ID plus generalist). We examined 12 HIV quality indicators used by 5 major medical and quality associations and grouped them into 4 domains: process, screening, immunization, and HIV management. We used generalized estimating equations to account for most common provider and multivariable analyses adjusted for prespecified covariates to compare composite rates of HIV quality indicator completion., Results: We found significant differences between HIV care models, with the ID plus generalists group achieving significantly higher quality measures than the ID group in HIV management (94.4% vs 91.7%, P = .03) and higher quality measures than generalists in immunization (87.8% vs 80.6%, P = .03) in multivariable adjusted analyses. All models achieved rates that equaled or surpassed previously reported quality indicator rates. The absolute differences between groups were small and ranged from 2% to 7%., Conclusions: Our results suggest that multiple HIV care models are effective with respect to HIV quality metrics. Factors to consider when determining HIV care model include healthcare setting, feasibility, and physician and patient preference., (Published by Oxford University Press on behalf of Infectious Diseases Society of America 2017. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Background: The purpose of the study was to measure clinical quality by doing an audit of clinical records and to compare the performance based on clinical quality indicators (CQI) for hypertension and type 2 diabetes across seven European countries: Estonia, Finland, Germany, Hungary, Italy, Lithuania and Spain., Methods: Two common chronic conditions in primary care (PC), hypertension and type 2 diabetes, were selected for audit. The assessment of CQI started with a literature review of different databases: Organization for Economic Co-operation and Development, World Health Organization, European Commission European Community Health Indicators, US National Library of Medicine. Data were collected from clinical records., Results: Although it was agreed to obtain the clinical indicators in a similar way from each country, the specific data collection process in every country varied greatly, due to different traditions in collecting and keeping the patients' data, as well as differences in regulation regarding access to clinical information. Also, there was a huge variability across countries in the level of compliance with the indicators., Conclusions: Measurement of clinical performance in PC by audit is methodologically challenging: different databases provide different information, indicators of quality of care have insufficient scientific proof and there are country-specific regulations. There are large differences not only in quality of health care across Europe but also in how it is measured., (© The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Purpose: Primary care is the cornerstone of the health care system and increasingly countries are developing indicators for assessing quality in primary care practices. The 'Quality Tool', developed in Ontario, Canada, provides a framework for assessing practices and consists of indicators and criteria. The purpose of this study was to validate the indicators and simplify the Quality Tool., Methods: This study involved a systematic comparison of indicators in the Quality Tool with those in other local and international tools to determine common indicators to include as valid in the Quality Tool. A Delphi process was used to help reach consensus for inclusion of any indicators that were not included in the comparison exercise., Setting: Primary care in Ontario, Canada., Subjects: Key informants were those with known expertise and experience in quality assessment in primary care., Main Outcome: Validated set of indicators for inclusion in an updated Quality Tool., Results: Twenty-three stakeholders participated in the Delphi panel. Forty-four indicators were included as valid after the systematic comparison of similar indicators in other assessment tools. Of the 63 indicators brought to the Delphi panel, 37 were included as valid, 15 were excluded and 11 became criteria for other included indicators., Conclusions: The study resulted in a set of 81 validated primary care indicators. The validation of the indicators provided a strong foundation for the next version of the Quality Tool and may be used for quality assessment in primary care., (© The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)