Achieving Clinical Automation in Emergency Medicine with Machine Learning Medical Directives

2021 ◽  
Author(s):  
Devin Singh ◽  
Sujay Nagaraj ◽  
Pouria Mashouri ◽  
Erik Drysdale ◽  
Jason Fischer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Medicine ◽  
Medical Directives

The necessity of machine learning in clinical emergency medicine; a narrative review of the current literature

2021 ◽  
Author(s):  
Ratchana Rajendran ◽  
Bhagyalaxmi Singirikonda ◽  
Navpreet ◽  
Neetu Jain ◽  
Mohd Naved ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Medicine ◽  
Current Literature ◽  
Narrative Review ◽  
Clinical Emergency

Artificial intelligence and machine learning in emergency medicine

2018 ◽  
Vol 30 (6) ◽  
pp. 870-874 ◽  
Author(s):  
Jonathon Stewart ◽  
Peter Sprivulis ◽  
Girish Dwivedi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Emergency Medicine

scholarly journals Machine Learning and Precision Medicine in Emergency Medicine: The Basics

Cureus ◽  
2021 ◽  
Author(s):  
Sangil Lee ◽  
Samuel H Lam ◽  
Thiago Augusto Hernandes Rocha ◽  
Ross J Fleischman ◽  
Catherine A Staton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Medicine ◽  
Precision Medicine

Machine Learning in Emergency Medicine: Keys to Future Success

2021 ◽  
Author(s):  
R. Andrew Taylor ◽  
Adrian D. Haimovich
Keyword(s):  
Machine Learning ◽  
Emergency Medicine ◽  
Future Success

scholarly journals Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study

2021 ◽  
Author(s):  
Katie Walker ◽  
Jirayus Jiarpakdee ◽  
Anne Loupis ◽  
Chakkrit Tantithamthavorn ◽  
Keith Joe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Medicine ◽  
Prediction Models ◽  
Wait Time ◽  
Wait Times ◽  
List Type ◽  
Learning Models ◽  
Emergency Patient ◽  
Individual Site ◽  
Machine Learning Models

AbstractObjectivePatients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments.MethodsTwelve emergency departments provided three years of retrospective administrative data from Australia (2017-19). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020).ResultsThere were 1,930,609 patient episodes analysed and median site wait times varied from 24 to 54 minutes. Individual site model prediction median absolute errors varied from +/−22.6 minutes (95%CI 22.4,22.9) to +/− 44.0 minutes (95%CI 43.4,44.4). Global model prediction median absolute errors varied from +/−33.9 minutes (95%CI 33.4, 34.0) to +/−43.8 minutes (95%CI 43.7, 43.9). Random forest and linear regression models performed the best, rolling average models under-estimated wait times. Important variables were triage category, last-k patient average wait time, and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period.ConclusionsElectronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site specific factors.What is already known on this subject⍰Patients and families want to know approximate emergency wait times, which will improve their ability to manage their logistical, physical and emotional needs whilst waiting⍰There are a few small studies from a limited number of jurisdictions, reporting model methods, important predictor variables and accuracy of derived modelsWhat this study adds⍰Our study demonstrates that predicting wait times from simple, readily available data is complex and provides estimates that aren’t as accurate as patients would like, however rough estimates may still be better than no information⍰We present the most influential variables regarding wait times and advise against using rolling average models, preferring random forest or linear regression techniques⍰Emergency medicine machine learning models may be less generalisable to other sites than we hope for when we read manuscripts or buy commercial off-the-shelf models or algorithms. Models developed for one site lose accuracy at another site and global models built for whole systems may need customisation to each individual site. This may apply to data science clinical decision instruments as well as operational machine learning models.

Artificial Intelligence and machine learning in Emergency Medicine

2020 ◽  
Author(s):  
Kenneth Jian Wei Tang ◽  
Candice Ke En Ang ◽  
Constantinides Theodoros ◽  
V. Rajinikanth ◽  
U. Rajendra Acharya ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Emergency Medicine

scholarly journals Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study

2021 ◽  
pp. emermed-2020-211000
Author(s):  
Katie Walker ◽  
Jirayus Jiarpakdee ◽  
Anne Loupis ◽  
Chakkrit Tantithamthavorn ◽  
Keith Joe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Medicine ◽  
Model Prediction ◽  
Emergency Departments ◽  
Prediction Models ◽  
Wait Time ◽  
Wait Times ◽  
Learning Models ◽  
Emergency Patient ◽  
Machine Learning Models

ObjectivePatients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments.MethodsTwelve emergency departments provided 3 years of retrospective administrative data from Australia (2017–2019). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020).ResultsThere were 1 930 609 patient episodes analysed and median site wait times varied from 24 to 54 min. Individual site model prediction median absolute errors varied from±22.6 min (95% CI 22.4 to 22.9) to ±44.0 min (95% CI 43.4 to 44.4). Global model prediction median absolute errors varied from ±33.9 min (95% CI 33.4 to 34.0) to ±43.8 min (95% CI 43.7 to 43.9). Random forest and linear regression models performed the best, rolling average models underestimated wait times. Important variables were triage category, last-k patient average wait time and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period.ConclusionsElectronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors.

Emerging and Innovative Technologies

2021 ◽  
pp. 275-286
Author(s):  
Ahmad A. Aalam ◽  
Sam P. Tarassoli ◽  
Damien J. Drury ◽  
Elias G. Carayannis ◽  
Andrew C. Meltzer
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Emergency Medicine ◽  
Augmented Reality ◽  
Remote Patient Monitoring ◽  
The Core ◽  
Unscheduled Care ◽  
Near Term ◽  
Core Mission ◽  
Remote Patient

To provide acute unscheduled care 24 hours per day and 7 days per week is the core mission of emergency medicine. Emergency telehealth is evolving in scope and complexity, no longer constraining care by the walls of the emergency department (ED). Current audio- and video-based communications will advance to support a complex interplay between enhanced video communication, remote patient monitoring, augmented reality, and machine learning. Many of these technologies already exist or are under development for near-term implementation. For those deploying or planning the deployment of emergency telehealth services, this chapter highlights near-term technologies and applications to be considered.

scholarly journals Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview

2019 ◽  
Vol 20 (2) ◽  
pp. 219-227 ◽  
Author(s):  
Sangil Lee ◽  
Nicholas Mohr ◽  
Nicholas Street ◽  
Prakash Nadkarni
Keyword(s):  
Machine Learning ◽  
Emergency Medicine
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