PROLIFERATE_AI: A prediction modeling method to evaluate Artificial Intelligence in meeting end-user-centric goals around better cardiac care

Maria Alejandra Pinero de Plaza, Kristina Lambrakis, Fernando Marmolejo-Ramos, Alline Beleigoli, Robyn Clark, Penelope McMillan, Erin Morton, Ehsan Khan, Renuka Visvanathan, Derek Chew, Alison Kitson, Jeroen Hendriks, Carlos Barrera-Causil

Research output: Contribution to journalMeeting Abstractpeer-review

Abstract

Objective
Bring a method to predict the implementation impact of an Artificial Intelligence (AI) tool by modelling its end-users’ acceptance and effect (pre- and post-training about RAPIDx AI: (A set of computer algorithms in hospital emergency departments designed to help doctors provide better care for patients with symptoms that may relate to their heart) [1].

Methods
PROLIFERATE (An adaptable framework with tools to evaluate different processes, outputs, and products via participatory research) [2-4] is combined with Bayesian Statistics (Expert Knowledge Elicitation) [5] to estimate the parameters of the probability model that best fit the data via maximum likelihood, using the fitdist function of the fitdistrplus package of the R software.

Results
PROLIFERATE_AI, formulates a quantifiable framework (and an algorithm) for informing the implementation of RAPIDx_AI and its impact, providing 95% probability prediction via point and interval estimates about the proportion of people who would use, understand, enjoy, keep using, and prefer RAPIDx AI. This simulation compares trained versus untrained end-users (Figure 1).

Conclusions
PROLIFERATE_AI, considers the non-linear characteristics of complex and adaptive acute care environments from an end-user perspective. This predictive procedure will monitor real-world clinical settings to address end-user-centric goals around better cardiac care through AI utilisation within 12 emergency departments in Australia. It will measure AI fitness via person-centred parameters. The forthcoming non-simulated evaluation has received Ethics approval (SACHREC): OfR no.272.20.
Original languageEnglish
Pages (from-to)S364-S365
Number of pages2
JournalHeart, Lung and Circulation
Volume32
Issue numberSupplement 3
DOIs
Publication statusPublished - 5 Aug 2023
Event71st Annual Scientific Meeting of the Cardiac Society of Australia and New Zealand - Adelaide Convention Centre, Adelaide, Australia
Duration: 3 Aug 20236 Aug 2023
Conference number: 71st
https://tcc.eventsair.com/QuickEventWebsitePortal/csanz-annual-scientific-meeting-2023-and-anzet-meeting-2023/program/Speaker

Keywords

  • Digital Health
  • Evaluation
  • prediction models
  • impact assessment
  • Co-design
  • Implementation Science

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