Journal ArticleDOI
Measuring quality and effectiveness of prehospital ems
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TLDR
Traditional efforts to assure quality in EMS systems are examined, while assessing the need to go beyond the traditional to establish measurable indicators of system quality.About:
This article is published in Prehospital Emergency Care.The article was published on 1999-01-01. It has received 99 citations till now. The article focuses on the topics: Health care & Emergency medical services.read more
Citations
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Journal ArticleDOI
Measuring quality in emergency medical services: a review of clinical performance indicators.
TL;DR: This paper aims at introducing emergency physicians and health care providers to quality initiatives in EMS and serves as a reference for tools that EMS medical directors can use to launch new or modify existing quality control programs in their systems.
Journal ArticleDOI
Evaluation of emergency medical services systems: a classification to assist in determination of indicators
C MacFarlane,C A Benn +1 more
TL;DR: This article attempts to assist in the development of valid EMS indicators of performance and effectiveness by categorising prehospital scenarios into a classification reflecting the reality of their conditions of practice.
Journal ArticleDOI
Eliminating errors in emergency medical services: realities and recommendations.
TL;DR: It is recommended that EMS medical directors consider specific error audits to decrease sources of errors and to be better able to identify EMS providers who would benefit from retraining.
Journal ArticleDOI
The Basics of Performance Measurement
TL;DR: The Basics of Performance Measurement as mentioned in this paper helped pioneer the science of performance measurement and continues to be one of the best-known performance measurement books in the world, although it was published in 1997.
Journal ArticleDOI
Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services
Da-Young Kang,Kyung-Jae Cho,Oyeon Kwon,Joon-myoung Kwon,Ki-Hyun Jeon,Hyunho Park,Yeha Lee,Jinsik Park,Byung Hee Oh +8 more
TL;DR: An artificial intelligence (AI) algorithm based on deep learning to predict the need for critical care of patients using information during EMS and outperformed the conventional triage tools and early warning scores.
References
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Book
The practice of social research
TL;DR: This chapter discusses the construction of Inquiry, the science of inquiry, and the role of data in the design of research.
Book
Health Measurement Scales: A Practical Guide to Their Development and Use
TL;DR: In this article, the authors propose three basic concepts: devising the items, selecting the items and selecting the responses, from items to scales, reliability and validity of the responses.
Journal ArticleDOI
The Quality of Care: How Can It Be Assessed?
TL;DR: Assessing quality depends on whether one assesses only the performance of practitioners or also the contributions of patients and of the health care system, on how broadly health and responsibility for health are defined, and on whether the maximally effective or optimally effective care is sought.
Journal ArticleDOI
Predicting survival from out-of-hospital cardiac arrest: a graphic model.
TL;DR: A graphic model that describes survival from sudden out-of-hospital cardiac arrest as a function of time intervals to critical prehospital interventions is developed and is useful in planning community EMS programs, comparing EMS systems, and showing how different arrival times within a system affect survival rate.
Related Papers (5)
Evaluation of emergency medical services systems: a classification to assist in determination of indicators
C MacFarlane,C A Benn +1 more