How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care

Abstract Background Much has been invested in big data and artificial intelligence-based solutions for healthcare. However, few applications have been implemented in clinical practice. Early economic evaluations can help to improve decision-making by developers of analytics underlying these solution...

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Autores principales:	Lytske Bakker, Jos Aarts, Carin Uyl-de Groot, Ken Redekop
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Analytics Artificial intelligence Big data Cost–benefit analysis Critical care Chronic lymphocytic leukaemia Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/956ad912a01b4be6992354531c8b9f12
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spelling	oai:doaj.org-article:956ad912a01b4be6992354531c8b9f122021-12-05T12:18:59ZHow can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care10.1186/s12911-021-01682-91472-6947https://doaj.org/article/956ad912a01b4be6992354531c8b9f122021-11-01T00:00:00Zhttps://doi.org/10.1186/s12911-021-01682-9https://doaj.org/toc/1472-6947Abstract Background Much has been invested in big data and artificial intelligence-based solutions for healthcare. However, few applications have been implemented in clinical practice. Early economic evaluations can help to improve decision-making by developers of analytics underlying these solutions aiming to increase the likelihood of successful implementation, but recommendations about their use are lacking. The aim of this study was to develop and apply a framework that positions best practice methods for economic evaluations alongside development of analytics, thereby enabling developers to identify barriers to success and to select analytics worth further investments. Methods The framework was developed using literature, recommendations for economic evaluations and by applying the framework to use cases (chronic lymphocytic leukaemia (CLL), intensive care, diabetes). First, the feasibility of developing clinically relevant analytics was assessed and critical barriers to successful development and implementation identified. Economic evaluations were then used to determine critical thresholds and guide investment decisions. Results When using the framework to assist decision-making of developers of analytics, continuing development was not always feasible or worthwhile. Developing analytics for progressive CLL and diabetes was clinically relevant but not feasible with the data available. Alternatively, developing analytics for newly diagnosed CLL patients was feasible but continuing development was not considered worthwhile because the high drug costs made it economically unattractive for potential users. Alternatively, in the intensive care unit, analytics reduced mortality and per-patient costs when used to identify infections (− 0.5%, − €886) and to improve patient-ventilator interaction (− 3%, − €264). Both analytics have the potential to save money but the potential benefits of analytics that identify infections strongly depend on infection rate; a higher rate implies greater cost-savings. Conclusions We present a framework that stimulates efficiency of development of analytics for big data and artificial intelligence-based solutions by selecting those applications of analytics for which development is feasible and worthwhile. For these applications, results from early economic evaluations can be used to guide investment decisions and identify critical requirements.Lytske BakkerJos AartsCarin Uyl-de GrootKen RedekopBMCarticleAnalyticsArtificial intelligenceBig dataCost–benefit analysisCritical careChronic lymphocytic leukaemiaComputer applications to medicine. Medical informaticsR858-859.7ENBMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Analytics Artificial intelligence Big data Cost–benefit analysis Critical care Chronic lymphocytic leukaemia Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Analytics Artificial intelligence Big data Cost–benefit analysis Critical care Chronic lymphocytic leukaemia Computer applications to medicine. Medical informatics R858-859.7 Lytske Bakker Jos Aarts Carin Uyl-de Groot Ken Redekop How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care
description	Abstract Background Much has been invested in big data and artificial intelligence-based solutions for healthcare. However, few applications have been implemented in clinical practice. Early economic evaluations can help to improve decision-making by developers of analytics underlying these solutions aiming to increase the likelihood of successful implementation, but recommendations about their use are lacking. The aim of this study was to develop and apply a framework that positions best practice methods for economic evaluations alongside development of analytics, thereby enabling developers to identify barriers to success and to select analytics worth further investments. Methods The framework was developed using literature, recommendations for economic evaluations and by applying the framework to use cases (chronic lymphocytic leukaemia (CLL), intensive care, diabetes). First, the feasibility of developing clinically relevant analytics was assessed and critical barriers to successful development and implementation identified. Economic evaluations were then used to determine critical thresholds and guide investment decisions. Results When using the framework to assist decision-making of developers of analytics, continuing development was not always feasible or worthwhile. Developing analytics for progressive CLL and diabetes was clinically relevant but not feasible with the data available. Alternatively, developing analytics for newly diagnosed CLL patients was feasible but continuing development was not considered worthwhile because the high drug costs made it economically unattractive for potential users. Alternatively, in the intensive care unit, analytics reduced mortality and per-patient costs when used to identify infections (− 0.5%, − €886) and to improve patient-ventilator interaction (− 3%, − €264). Both analytics have the potential to save money but the potential benefits of analytics that identify infections strongly depend on infection rate; a higher rate implies greater cost-savings. Conclusions We present a framework that stimulates efficiency of development of analytics for big data and artificial intelligence-based solutions by selecting those applications of analytics for which development is feasible and worthwhile. For these applications, results from early economic evaluations can be used to guide investment decisions and identify critical requirements.
format	article
author	Lytske Bakker Jos Aarts Carin Uyl-de Groot Ken Redekop
author_facet	Lytske Bakker Jos Aarts Carin Uyl-de Groot Ken Redekop
author_sort	Lytske Bakker
title	How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care
title_short	How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care
title_full	How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care
title_fullStr	How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care
title_full_unstemmed	How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care
title_sort	how can we discover the most valuable types of big data and artificial intelligence-based solutions? a methodology for the efficient development of the underlying analytics that improve care
publisher	BMC
publishDate	2021
url	https://doaj.org/article/956ad912a01b4be6992354531c8b9f12
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How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care

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