专家对疼痛自动评估在临床常规应用中的可行性和应用达成共识。

IF 3.1
Marco Cascella, Alfonso Maria Ponsiglione, Vittorio Santoriello, Maria Romano, Valentina Cerrone, Dalila Esposito, Mario Montedoro, Roberta Pellecchia, Gennaro Savoia, Giuliano Lo Bianco, Massimo Innamorato, Silvia Natoli, Jonathan Montomoli, Federico Semeraro, Elena Giovanna Bignami, Valentina Bellini, Matteo Luigi Giuseppe Leoni, Felice Occhigrossi, Alessandro Vittori, Maria Caterina Pace, Pasquale Buonanno, Mauro Forte, Elisabetta Chinè, Roberta Carpenedo, Alessandro De Cassai, Alfonso Papa, Maurizio Marchesini, Gaetano Terranova, Fabrizio Micheli, Laura Demartini, Franco Marinangeli, William Raffaeli, Flaminia Coluzzi, Andrea Tinnirello, Roberto Arcioni, Angelo Marra, Mohammed Naveed Shariff, Federica Monaco, Gabriele Finco, Alessia Bramanti, Ornella Piazza
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引用次数: 0

摘要

背景:疼痛通常难以评估,特别是在非交流患者中。虽然基于人工智能(AI)的客观自动疼痛评估(APA)系统是一个很有前途的解决方案,但它们的临床实施提出了一些基本问题,主要是关于临床医生的接受程度。方法:对APA临床应用的可行性和应用率进行问卷调查。首先,指导委员会实施了樱桃指南,并为医疗保健专业人员设计了一份问卷。根据调查结果,26名疼痛医学专家被要求参与两轮共识,通过7分李克特量表对10项陈述进行评分。共识定义为≥75%的同意(“同意”或“完全同意”)。这两个阶段都是通过在线问卷收集数据,并进行定量分析。结果:在调查中,我们收集了628名医疗保健专业人员的回复。产出突出了对该技术的高度接受和对多维技术的偏好。经过两轮磋商,10项声明中有8项达成共识。专家们同意APA在支持医疗保健专业人员和实时疼痛监测方面的效用。强烈的共识(96.2%)支持有必要告知患者人工智能系统的使用和局限性。充分的员工培训是强制性的。此外,92.3%的受访者同意在整个APA生命周期中实施风险管理、数据质量控制和人工智能治理的重要性。专家们强调有必要进行内部和外部验证程序并定期更新,即使是为了研究目的。与会者还就涉及跨学科利益相关者和解决监管、伦理和社会影响的重要性达成了共识。推荐APA系统中的多模态输入(例如,生理信号、面部表情、语音和临床数据)。此外,APA系统应该能够分级疼痛水平(例如,通过NRS),而不仅仅是检测疼痛的存在。另一方面,两项声明没有达成共识:APA系统对急性和慢性疼痛的适用性及其改善治疗策略的潜力。结论:APA被认为是一种很有前途和潜在可行的临床疼痛评估技术,特别是在弱势群体中。需要进一步的研究来验证专用工具,确定在不同临床条件下的应用(例如,急性和慢性疼痛),并证明它们对疼痛管理的常规临床实践的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Expert consensus on feasibility and application of automatic pain assessment in routine clinical use.

Expert consensus on feasibility and application of automatic pain assessment in routine clinical use.

Expert consensus on feasibility and application of automatic pain assessment in routine clinical use.

Expert consensus on feasibility and application of automatic pain assessment in routine clinical use.

Background: Pain is often difficult to assess, particularly in non-communicative patients. While artificial intelligence (AI)-based objective Automatic Pain Assessment (APA) systems are a promising solution, their clinical implementation raises essential questions, primarily regarding clinician acceptance.

Methods: We conducted a survey-to-consensus investigation on the feasibility and application of APA for clinical use. Firstly, the steering committee implemented the CHERRIES guidelines and designed a questionnaire for healthcare professionals. Given the survey results, 26 experts in pain medicine were asked to participate in a two-round consensus by rating 10 statements through a 7-point Likert scale. Consensus was defined as ≥ 75% agreement ("agree" or "completely agree"). For both phases, data was collected through online questionnaires and analyzed quantitatively.

Results: For the survey, we collected responses from 628 healthcare professionals. The output highlighted excellent acceptance of the technology and a preference for multidimensional techniques. After two rounds, consensus was achieved on 8 out of 10 statements. Experts agreed on APA utility in supporting healthcare professionals and real-time pain monitoring. A strong consensus (96.2%) supported the need to inform patients about the use and limitations of AI systems. Adequate staff training is mandatory. Moreover, 92.3% agreed on the importance of implementing risk management, data quality control, and AI governance throughout the APA lifecycle. The experts stressed the need for internal and external validation processes and periodic updates, even for research purposes. Consensus was also reached about the importance of involving interdisciplinary stakeholders and addressing regulatory, ethical, and social implications. Multimodal inputs (e.g., physiological signals, facial expressions, speech, and clinical data) in APA systems are recommended. Additionally, APA systems should be capable of grading pain levels (e.g., via NRS), not just detecting the presence of pain. On the other hand, two statements did not reach consensus: the applicability of APA systems for acute and chronic pain conditions and their potential to improve therapeutic strategies.

Conclusion: APA is viewed as a promising and potentially feasible technology for clinical pain assessment, particularly in vulnerable populations. Further research is needed to validate the dedicated tools, define applications in different clinical conditions (e.g., acute and chronic pain), and demonstrate their impact on routine clinical practice for pain management.

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