Predicting the implementation impact of RAPIDx AI in South Australian emergency departments

Maria Alejandra Pinero de Plaza, Kristina Lambrakis, Carlos Javier Barrera Causil, Fernando Marmolejo Ramos, Derek Chew, Alline Beleigoli, Michael Lawless, Mandy Archibald, Alexandra Mudd, Penelope McMillan, Erin Morton, Rachel Ambagtsheer, Ehsan Khan, Robyn Clark, Renuka Visvanathan, Lalit Yadav, Alison Kitson

Research output: Contribution to conferencePosterpeer-review


Background: There were 75,900 presentations to Australian public hospital Emergency Departments (EDs) with a principal diagnosis of Coronary Heart Disease in 2020–21. RAPIDx AI is a randomised controlled trial to test whether computer algorithms in hospital EDs can help doctors provide better care for patients with symptoms that may be due to their heart.

Objective: To develop an evaluation and prediction method to measure stakeholders' perspectives about the implementation impact of RAPIDx AI. This methodological innovation is necessary because person-centred healthcare services require effective technology integration within clinical workflows to provide better patient care while considering the needs of all end-users involved and affected by such types of tech/practices/service changes.

Methods: We introduce an evaluation framework and method based on complexity science and participatory action research (PROLIFERATE). Using Bayesian statistics, we created a protocol and produced computer-simulated results to demonstrate the evaluation and prediction capabilities of the method concerning RAPIDx AI. Ethical approval was granted by the Southern Adelaide Human Research Ethics Committee (SACHREC): OfR no.272.20

Results: Our methodological innovation is informed by 95% probability prediction and credible intervals on these domains of stakeholders' perspectives: Comprehension, Emotional response; Uptake barriers; Motivation and Optimisation. Computer-simulated responses to a PROLIFERATE online survey predicted an Average Impact for RAPIDx AI. The simulation results imply that motivational and emotional knowledge-translation strategies must be implemented for clinicians and the community to improve RAPIDx AI sustainability. (PROLIFERATE constructs benchmarked at 50%—algorithm and data analysis developed in R.)

Conclusion: PROLIFERATE considers the non-linear characteristics of complex and adaptive workflows of acute care environments from an end-user perspective. It can monitor real-world clinical settings, research outcomes, and technological products by assessing their fitness via person-centred parameters and a transdisciplinary approach.

Original languageEnglish
Number of pages1
Publication statusPublished - 28 Oct 2022
EventSouth Australian Cardiovascular Showcase - Adelaide - SAHMRI, Adelaide , Australia
Duration: 28 Oct 202228 Oct 2022
Conference number: 2


OtherSouth Australian Cardiovascular Showcase
Abbreviated titleSA Cardiovascular Showcase
Internet address


  • artificial intelligence (AI)
  • Evaluation
  • Participatory Action Research
  • Implementation Science
  • Bayesian analysis
  • Heart disease
  • Emergency Care


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