Derivation of a Triage Algorithm for Chest Radiography of Community‐acquired Pneumonia Patients in the Emergency Department

Kyriacou, Demetrios N.; Yarnold, Paul R.; Soltysik, Robert C.; Self, Wesley H.; Wunderink, Richard G.; Schmitt, Brian P.; Parada, Jorge P.; Adams, James G.

doi:10.1111/j.1553-2712.2007.00011.x

Cited by 13 publications

(5 citation statements)

References 10 publications

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“…The Joint Commission/CMS pneumonia guidelines, being proposed as a pay‐for‐performance measure with actual financial risk at stake, has led 100% of surveyed departments to create multiple operational changes to induce compliance 15 . Some specific individual attempts include creation of a decision rule by which a chest radiograph is ordered at triage or having a nonclinical physician (named a “pneumonia czar”) actively search for patients and ensure that they are receiving antibiotics expeditiously 16,17 …”

Section: Discussionmentioning

confidence: 99%

Weekly E‐mail Reminders Influence Emergency Physician Behavior: A Case Study Using the Joint Commission and Centers for Medicare and Medicaid Services Pneumonia Guidelines

Weiner

Brown

Goetz

et al. 2009

Academic Emergency Medicine

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Objectives: Improving physician compliance with evidence-based guidelines is challenging. The authors wanted to determine if weekly e-mail reminders to emergency department (ED) staff increase compliance with Joint Commission and the Centers for Medicare and Medicaid Services (CMS) communityacquired pneumonia quality measures. Methods:One nurse administrator reviewed records on a weekly basis for all adult patients admitted to the hospital from the ED with a working diagnosis of pneumonia. An e-mail was then sent to all ED staff indicating the percentage of patients with antibiotic timing less than 4 hours from arrival. The names of individuals who administered antibiotics in less than 1 hour were highlighted. This study compared the time to antibiotics for 11 months before and 11 months after commencing this intervention.Results: There were 281 patients in the control cohort, and 37 met exclusion criteria, leaving 244 for analysis. There were 342 patients in the intervention cohort, and 40 met exclusion criteria, leaving 302 for analysis. The median time from arrival to chest radiograph order decreased significantly from 61 to 47 minutes (p < 0.001). The median time interval from chest radiograph order to antibiotic administration did not change significantly (92 to 88 minutes, p = 0.294). The overall median time from arrival to antibiotic administration decreased significantly from 162 to 146 minutes (p = 0.018). The percentage of patients with antibiotic administration within 4 hours increased from 77.5% to 86.1% (p = 0.009).Conclusions: Weekly e-mail reminders listing performance on antibiotic administration recommendations are associated with increased compliance with a clinical guideline.ACADEMIC EMERGENCY MEDICINE 2009; 16:626-631 ª

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Section: Discussionmentioning

confidence: 99%

Weekly E‐mail Reminders Influence Emergency Physician Behavior: A Case Study Using the Joint Commission and Centers for Medicare and Medicaid Services Pneumonia Guidelines

Weiner

Brown

Goetz

et al. 2009

Academic Emergency Medicine

View full text Add to dashboard Cite

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“…[52] Temperature >100.4°F, heart rate >110 beats/min, and pulse oximetric saturation <96% have been found to be strong predictors of CAP. [53] However, no single characteristic is adequately sensitive and specific to accurately discriminate CAP from viral illness. [49] Also, respiratory and non-respiratory symptoms associated with a pneumonic illness are less commonly reported by older patients with pneumonia.…”

Section: Community-acquired Pneumoniamentioning

confidence: 99%

Guidelines for diagnosis and management of community-and hospital-acquired pneumonia in adults: Joint ICS/NCCP(I) recommendations

et al. 2012

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“…In the most conservative case assuming a proportion of discordant pairs of 10%, a sample size of 1152 patients provides 90% power to notice an OR of 0.53 [ 2 ] using a two-sided McNemar test at a significance level of .05. Each year, community-acquired pneumonia incurs 1.5 million ED patient visits [ 111 ], giving adequate power to test the hypothesis. To acquire the whole range of possible outcomes, we will do sensitivity analysis by changing the levels of the probabilities p 1 and p 2 , 30-day mortality rate, and rate reduction gained by machine learning.…”

Section: Methodsmentioning

confidence: 99%

Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods

et al. 2017

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BackgroundTo improve health outcomes and cut health care costs, we often need to conduct prediction/classification using large clinical datasets (aka, clinical big data), for example, to identify high-risk patients for preventive interventions. Machine learning has been proposed as a key technology for doing this. Machine learning has won most data science competitions and could support many clinical activities, yet only 15% of hospitals use it for even limited purposes. Despite familiarity with data, health care researchers often lack machine learning expertise to directly use clinical big data, creating a hurdle in realizing value from their data. Health care researchers can work with data scientists with deep machine learning knowledge, but it takes time and effort for both parties to communicate effectively. Facing a shortage in the United States of data scientists and hiring competition from companies with deep pockets, health care systems have difficulty recruiting data scientists. Building and generalizing a machine learning model often requires hundreds to thousands of manual iterations by data scientists to select the following: (1) hyper-parameter values and complex algorithms that greatly affect model accuracy and (2) operators and periods for temporally aggregating clinical attributes (eg, whether a patient’s weight kept rising in the past year). This process becomes infeasible with limited budgets.ObjectiveThis study’s goal is to enable health care researchers to directly use clinical big data, make machine learning feasible with limited budgets and data scientist resources, and realize value from data.MethodsThis study will allow us to achieve the following: (1) finish developing the new software, Automated Machine Learning (Auto-ML), to automate model selection for machine learning with clinical big data and validate Auto-ML on seven benchmark modeling problems of clinical importance; (2) apply Auto-ML and novel methodology to two new modeling problems crucial for care management allocation and pilot one model with care managers; and (3) perform simulations to estimate the impact of adopting Auto-ML on US patient outcomes.ResultsWe are currently writing Auto-ML’s design document. We intend to finish our study by around the year 2022.ConclusionsAuto-ML will generalize to various clinical prediction/classification problems. With minimal help from data scientists, health care researchers can use Auto-ML to quickly build high-quality models. This will boost wider use of machine learning in health care and improve patient outcomes.

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Derivation of a Triage Algorithm for Chest Radiography of Community‐acquired Pneumonia Patients in the Emergency Department

Abstract: Background: Community-acquired pneumonia (CAP) accounts for 1.5 million emergency department (ED) patient visits in the United States each year.

Cited by 13 publications

References 10 publications

Weekly E‐mail Reminders Influence Emergency Physician Behavior: A Case Study Using the Joint Commission and Centers for Medicare and Medicaid Services Pneumonia Guidelines

Weekly E‐mail Reminders Influence Emergency Physician Behavior: A Case Study Using the Joint Commission and Centers for Medicare and Medicaid Services Pneumonia Guidelines

Guidelines for diagnosis and management of community-and hospital-acquired pneumonia in adults: Joint ICS/NCCP(I) recommendations

Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods

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