Artificial intelligence for surgical services in Australia and New Zealand: opportunities, challenges and recommendations

Kovoor, Joshua G; Bacchi, Stephen; Sharma, Prakriti; Sharma, Srishti; Kumawat, Medhir; Stretton, Brandon; Gupta, Aashray K; Chan, WengOnn; Abou‐Hamden, Amal; Maddern, Guy J

doi:10.5694/mja2.52225

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…While the study included questions about accountability for AI decisionmaking, 1 it is also important for future enquiry that the results on these questions are presented, as the greatest risks to patient outcomes arise where AI is perceived as being a decision-maker, rather than a part of the clinical toolkit, and it is vital that practitioners do not attribute more credibility to an AI recommendation than is due. 2 These findings are important, as clinical implementation trials are likely to become more frequent in the coming years for Australian AI tools such as the Adelaide Score, and methods of safe allergy delabelling. [3][4][5][6] However, while high-quality work such as that by Shinners et al has been done regarding barriers and facilitators before the implementation of AI, 1,3,4 few studies have been conducted investigating potential unforeseen consequences after implementation.…”

Section: E T T E R T O T H E E D I T O Rmentioning

confidence: 99%

“…Public hospitals must be cautious when purchasing technology from private entities with a profit motive, and strict adherence to governance and regulatory frameworks must always be adhered to, along with data transparency at all points of every agreement. 2 Once agreements are made, a scoping exercise is required to inform implementation. There must be multifaceted consideration of whether the system will experience a net loss, rather than net saving, in the long term, using either traditional or more novel economic measurement tools.…”

Section: E T T E R T O T H E E D I T O Rmentioning

confidence: 99%

See 1 more Smart Citation

No shortcuts: False economy prevention during artificial intelligence implementation in rural Australian health care

Kovoor,

Ittimani,

Godber

et al. 2024

Australian J Rural Health

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Section: E T T E R T O T H E E D I T O Rmentioning

confidence: 99%

Section: E T T E R T O T H E E D I T O Rmentioning

confidence: 99%

No shortcuts: False economy prevention during artificial intelligence implementation in rural Australian health care

Kovoor,

Ittimani,

Godber

et al. 2024

Australian J Rural Health

Self Cite

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Impact of frailty, malnutrition and socioeconomic status on perioperative outcomes

Stretton,

Booth,

Kovoor

et al. 2024

Age and Ageing

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Background Frailty, malnutrition and low socioeconomic status may mutually perpetuate each other in a self-reinforcing and interdependent manner. The intertwined nature of these factors may be overlooked when investigating impacts on perioperative outcomes. This study aimed to investigate the impact of frailty, malnutrition and socioeconomic status on perioperative outcomes. Methods A multicentre cohort study involving six Australian tertiary hospitals was undertaken. All consecutive surgical patients who underwent an operation were included. Frailty was defined by the Hospital Frailty Risk Score, malnutrition by the Malnutrition Universal Screening Tool (MUST) and low socioeconomic status by the Index of Relative Socioeconomic Disadvantage. Linear mixed-effects and binary logistic generalised estimated equation models were performed for the outcomes: inpatient mortality, length of stay, 30-day readmission and re-operation. Results A total of 21 976 patients were included. After controlling for confounders, malnutrition and socioeconomic status, patients at high risk of frailty have a mean hospital length of stay 3.46 times longer (mean ratio = 3.46; 95% confidence interval (CI): 3.20, 3.73; P value < .001), odds of 30-day readmission 2.4 times higher (odds ratio = 2.40; 95% CI: 2.19, 2.63; P value < .001) and odds of in-hospital mortality 12.89 times greater than patients with low risk of frailty (odds ratio = 12.89; 95% CI: 4.51, 36.69; P value < .001). Elevated MUST scores were also significantly associated with worse outcomes, but to a lesser extent. Socioeconomic status had no association with outcomes. Conclusion Perioperative risk evaluation should consider both frailty and malnutrition as separate, significant risk factors. Despite strong causal links with frailty and malnutrition, socioeconomic disadvantage is not associated with worse postoperative outcomes. Additional studies regarding the prospective identification of these patients with implementation of strategies to mitigate frailty and malnutrition and assessment of perioperative risk are required.

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“Leveraging AI Tools to Bridge the Healthcare Gap in Rural Areas in India”

Kerketta,

Balasundaram

2024

Preprint

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IntroductionDespite considerable progress in the healthcare sector, rural regions continue to grapple with healthcare deficiencies. However, the emergence of AI technology offers promising solutions to overcome these hurdles. Hence, the study explores the potential and efficacy of introducing artificial intelligence (AI) tools to address the healthcare disparity in rural India.MethodsThe research employed a literature review method and gathered data from various databases such as Science Direct, PubMed, and Google Scholar. The screening process was aided by the “Rayyan” electronic software. Articles published in English between January 2020 and December 2022 were selected, followed by a thematic analysis of the findings.ResultsResults indicate the potential of AI in rural healthcare settings, showing AI-driven solutions addressing healthcare access gaps and contributing to their bridging. The study also highlights hurdles related to AI tool adoption in rural healthcare and proposes collaborative efforts among policymakers, healthcare providers, and technology developers to integrate AI tools effectively. This necessitates advocating for digital infrastructure investments, capacity-building initiatives, and conducive regulatory frameworks for AI implementation.ConclusionThe study underscores AI’s transformative role in bridging the healthcare gap in rural India. By harnessing AI technologies, healthcare providers and policymakers can surmount barriers, empower local healthcare workers, and improve health outcomes for rural communities. The insights and recommendations contribute to the evolving knowledge base on leveraging AI for adequate healthcare delivery, guiding future initiatives in similar contexts.

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Artificial intelligence for surgical services in Australia and New Zealand: opportunities, challenges and recommendations

Cited by 8 publications

References 23 publications

No shortcuts: False economy prevention during artificial intelligence implementation in rural Australian health care

No shortcuts: False economy prevention during artificial intelligence implementation in rural Australian health care

Impact of frailty, malnutrition and socioeconomic status on perioperative outcomes

“Leveraging AI Tools to Bridge the Healthcare Gap in Rural Areas in India”

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