ABSTRACT
Artificial intelligence (AI) in healthcare describes algorithm-based computational techniques which manage and analyse large datasets to make inferences and predictions. There are many potential applications of AI in the care of older people, from clinical decision support systems that can support identification of delirium from clinical records to wearable devices that can predict the risk of a fall. We held four meetings of older people, clinicians and AI researchers. Three priority areas were identified for AI application in the care of older people. These included: monitoring and early diagnosis of disease, stratified care and care coordination between healthcare providers. However, the meetings also highlighted concerns that AI may exacerbate health inequity for older people through bias within AI models, lack of external validation amongst older people, infringements on privacy and autonomy, insufficient transparency of AI models and lack of safeguarding for errors. Creating effective interventions for older people requires a person-centred approach to account for the needs of older people, as well as sufficient clinical and technological governance to meet standards of generalisability, transparency and effectiveness. Education of clinicians and patients is also needed to ensure appropriate use of AI technologies, with investment in technological infrastructure required to ensure equity of access.
Subject(s)
Artificial Intelligence , Decision Support Systems, Clinical , Humans , Aged , Algorithms , Educational Status , Delivery of Health CareABSTRACT
PURPOSE: A systematic review to determine the effectiveness of intra-arterial anaesthetics on post- operative pain and opioid analgesia requirements in arterial embolisation procedures. MATERIALS AND METHODS: A systematic review of the literature was performed (Embase, PubMed, MEDLINE and the Cochrane Library) from inception to 10th August 2020. Randomised controlled trials (RCTs) and cohort studies that utilised intra-arterial anaesthesia during an embolisation procedure for the purposes of pain control were included. Eligibility was assessed by two investigators independently. RESULTS: Eight hundred fifty-nine candidate articles were identified, and 9 studies met the inclusion criteria (6 RCTs and 3 retrospective cohort studies). Four studies were of hepatic chemoembolisation and 5 were of uterine artery embolisation. Five hundred twenty-nine patients were treated in total. All studies used lidocaine as the anaesthetic with doses ranging from 20 to 200 mg, and the anaesthetic was delivered varyingly before, during or after embolisation. Pain intensity was converted to a numeric scale from 0 to 10, and opioid doses were converted to milligram morphine equivalent doses. A random-effects meta-analysis model was used to analyse the results of RCTs, and the results of cohort studies were summarised with a narrative synthesis. The meta-analyses suggested that pain scores were reduced by a mean of 1.02 (95% CI - 2.34 to 0.30; p = 0.13) and opioid doses were reduced by a mean of 7.35 mg (95% CI, - 14.77, 0.06; p = 0.05) in the intervention group however neither finding was statistically significant. No serious adverse events were reported. CONCLUSION: Intra-arterial anaesthetic may slightly reduce pain intensity and post-operative opioid consumption following embolisation, however the results are not statistically significant. There is very limited data available on the effect of anaesthetic on length of hospital admission. Whilst no serious adverse events were reported, there are some concerns regarding the effect of lidocaine on the technical success of embolisation procedures that preclude our recommendation for routine use in embolisation procedures. High quality randomised controlled trials are required to elucidate the dose-response effect of lidocaine on opioid consumption and pain following embolisation, particularly in the first few hours post-operatively, as well as effects on duration of hospital stay.
