“What About Automated Pain Recognition for Routine Clinical Use?” A Survey of Physicians and Nursing Staff on Expectations, Requirements, and Acceptance

Walter, Steffen; Gruss, Sascha; Frisch, Stephan; Liter, Joseph; Jerg-Bretzke, Lucia; Schick, Benedikt; Barth, Eberhard

doi:10.3389/fmed.2020.566278

Cited by 11 publications

(12 citation statements)

References 24 publications

(18 reference statements)

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“…Findings from research based on the BioVid ( 30 ) & X-ITE ( 24 ) datasets, the results of the present study, staff acceptance ( 29 ), and patient acceptance indicate that it would now be appropriate to develop and test an APR prototype.…”

Section: Discussionmentioning

confidence: 69%

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The impact of analgesic on EMG and other biosignals in a postoperative setting

Gruss

Schmid²,

Walter³

et al. 2023

Front. Med.

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BackgroundIn the clinical context, the assessment of pain in patients with inadequate communication skills is standardly performed externally by trained medical staff. Automated pain recognition (APR) could make a significant contribution here. Hereby, pain responses are captured using mainly video cams and biosignal sensors. Primary, the automated monitoring of pain during the onset of analgesic sedation has the highest relevance in intensive care medicine. In this context, facial electromyography (EMG) represents an alternative to recording facial expressions via video in terms of data security. In the present study, specific physiological signals were analyzed to determine, whether a distinction can be made between pre-and post-analgesic administration in a postoperative setting. Explicitly, the significance of the facial EMG regarding the operationalization of the effect of analgesia was tested.MethodsN = 38 patients scheduled for surgical intervention where prospectively recruited. After the procedure the patients were transferred to intermediate care. Biosignals were recorded and all doses of analgesic sedations were carefully documented until they were transferred back to the general ward.ResultsAlmost every biosignal feature is able to distinguish significantly between ‘before’ and ‘after’ pain medication. We found the highest effect sizes (r = 0.56) for the facial EMG.ConclusionThe results of the present study, findings from research based on the BioVid and X-ITE pain datasets, staff and patient acceptance indicate that it would now be appropriate to develop an APR prototype.

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Section: Discussionmentioning

confidence: 69%

“…This would be a key benefit for clinical staff. Walter et al (29) showed that the majority of clinical staff in the ICU would prefer a biosignal-based pain detection. In relation to this, facial EMG would have the benefit of replacing computer vision, in terms of cost effectiveness and data security.…”

Section: Discussionmentioning

confidence: 99%

The impact of analgesic on EMG and other biosignals in a postoperative setting

Gruss

Schmid²,

Walter³

et al. 2023

Front. Med.

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“…Two studies did not observe an effect 50 , 52 . Walter et al 53 found that 55.8% of younger participants claimed that they would use automated pain recognition. In the older age group, only 40.4% of respondents reported that they would use the system ( N = 102) 53 .…”

Section: Resultsmentioning

confidence: 99%

An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals

et al. 2023

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Artificial intelligence (AI) in the domain of healthcare is increasing in prominence. Acceptance is an indispensable prerequisite for the widespread implementation of AI. The aim of this integrative review is to explore barriers and facilitators influencing healthcare professionals’ acceptance of AI in the hospital setting. Forty-two articles met the inclusion criteria for this review. Pertinent elements to the study such as the type of AI, factors influencing acceptance, and the participants’ profession were extracted from the included studies, and the studies were appraised for their quality. The data extraction and results were presented according to the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The included studies revealed a variety of facilitating and hindering factors for AI acceptance in the hospital setting. Clinical decision support systems (CDSS) were the AI form included in most studies (n = 21). Heterogeneous results with regard to the perceptions of the effects of AI on error occurrence, alert sensitivity and timely resources were reported. In contrast, fear of a loss of (professional) autonomy and difficulties in integrating AI into clinical workflows were unanimously reported to be hindering factors. On the other hand, training for the use of AI facilitated acceptance. Heterogeneous results may be explained by differences in the application and functioning of the different AI systems as well as inter-professional and interdisciplinary disparities. To conclude, in order to facilitate acceptance of AI among healthcare professionals it is advisable to integrate end-users in the early stages of AI development as well as to offer needs-adjusted training for the use of AI in healthcare and providing adequate infrastructure.

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“…Automated pain recognition (APR) is an external observation method into which hardware and software components with artificial intelligence (AI) are integrated (Walter et al, 2020). Automated pain recognition systems may be based on pain behaviours, as facial expressions, vocalization and avoidance movements, and/or physiological signals (heart rate, pupillary diameter, …).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence to evaluate postoperative pain based on facial expression recognition

Fontaine

Vielzeuf

Genestier

et al. 2022

European Journal of Pain

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Funding informationThis work has been funded equally by the Centre Hospitalier Universitaire de Nice (CHUN) and Orange SA. The CHUN designed and sponsored the study, performed the inclusions and data collection, interpreted the analysis, wrote the report and decided to submit it. Orange SA was in charge of the image processing, image data analysis, deep learning system development and statistical analysis.

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“What About Automated Pain Recognition for Routine Clinical Use?” A Survey of Physicians and Nursing Staff on Expectations, Requirements, and Acceptance

Cited by 11 publications

References 24 publications

The impact of analgesic on EMG and other biosignals in a postoperative setting

The impact of analgesic on EMG and other biosignals in a postoperative setting

An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals

Artificial intelligence to evaluate postoperative pain based on facial expression recognition

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