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Digital health technology and trauma: development of an app to standardize care
Author(s) -
Hsu Jeremy M.
Publication year - 2015
Publication title -
anz journal of surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.426
H-Index - 70
eISSN - 1445-2197
pISSN - 1445-1433
DOI - 10.1111/ans.12945
Subject(s) - standardization , medicine , process (computing) , trauma care , health care , computer science , set (abstract data type) , best practice , medical emergency , operating system , management , economics , programming language , economic growth
Background Standardized practice results in less variation, therefore reducing errors and improving outcome. Optimal trauma care is achieved through standardization, as is evidenced by the widespread adoption of the A dvanced T rauma L ife S upport approach. The challenge for an individual institution is how does one educate and promulgate these standardized processes widely and efficiently? In today's world, digital health technology must be considered in the process. Aim The aim of this study was to describe the process of developing an app, which includes standardized trauma algorithms. The objective of the app was to allow easy, real‐time access to trauma algorithms, and therefore reduce omissions/errors. Method A set of trauma algorithms, relevant to the local setting, was derived from the best available evidence. After obtaining grant funding, a collaborative endeavour was undertaken with an external specialist app developing company. Results The process required 6 months to translate the existing trauma algorithms into an app. The app contains 32 separate trauma algorithms, formatted as a single‐page flow diagram. It utilizes specific smartphone features such as ‘pinch to zoom’, jump‐words and pop‐ups to allow rapid access to the desired information. Conclusion Improvements in trauma care outcomes result from reducing variation. By incorporating digital health technology, a trauma app has been developed, allowing easy and intuitive access to evidenced‐based algorithms.