Kumar Sanjay, Dutta Anghusman, Gupta Manish, Singh Ran
Department of ENT, Command Hospital Airforce Bangalore, Rajiv Gandhi University of Health Sciences, Bangalore, India.
Department of Anaesthesia, Command Hospital Airforce, Rajiv Gandhi University of Health Sciences, Bangalore, India.
Indian J Otolaryngol Head Neck Surg. 2024 Dec;76(6):5799-5806. doi: 10.1007/s12070-024-05103-x. Epub 2024 Sep 25.
Introduction: Tonsillectomy is commonly associated with significant postoperative challenges such as pain management, complication monitoring, and patient recovery. Traditional care methods, while effective, often do not adequately address these issues, particularly in personalized care and remote monitoring. This study assesses the impact of Artificial Intelligence (AI)-assisted postoperative care on recovery outcomes in tonsillectomy patients compared to conventional care methods. Methods: Conducted at a tertiary care hospital's Otolaryngology Department from January to December 2023, this observational cohort study involved 100 elective tonsillectomy patients. Participants were divided into two cohorts: one receiving traditional care and the other AI-assisted care, which utilized machine learning for pain management, continuous symptom monitoring through wearable devices, and virtual assistance. Results: AI-assisted care significantly improved early postoperative pain management, reducing pain scores to 5.2 ± 1.1 from 6.5 ± 1.2 in traditional care ( = 0.01). Dehydration rates decreased from 6 to 1% ( = 0.05), and the average hospital stay was reduced to 2.8 ± 1.1 days from 3.5 ± 1.2 days. While no significant differences were found in readmission rates for haemorrhage and infection, patient satisfaction notably increased, with pain management improving to 4.4 ± 0.7 and overall satisfaction to 4.1 ± 0.6 ( = 0.03). Conclusion: AI-assisted care offers significant advantages over traditional methods in managing tonsillectomy recovery, optimizing surgical outcomes, and enhancing patient satisfaction. This study supports further exploration into AI's long-term outcomes and its application across various surgical fields.
The online version contains supplementary material available at 10.1007/s12070-024-05103-x.
引言：扁桃体切除术通常伴随着显著的术后挑战，如疼痛管理、并发症监测和患者康复。传统护理方法虽然有效，但往往不能充分解决这些问题，尤其是在个性化护理和远程监测方面。本研究评估了与传统护理方法相比，人工智能（AI）辅助术后护理对扁桃体切除患者康复结果的影响。方法：本观察性队列研究于2023年1月至12月在一家三级护理医院的耳鼻喉科进行，纳入100例择期扁桃体切除患者。参与者被分为两组：一组接受传统护理，另一组接受AI辅助护理，后者利用机器学习进行疼痛管理、通过可穿戴设备进行持续症状监测以及虚拟辅助。结果：AI辅助护理显著改善了术后早期疼痛管理，疼痛评分从传统护理中的6.5±1.2降至5.2±1.1（=0.01）。脱水率从6%降至1%（=0.05），平均住院天数从3.5±1.2天减少至2.8±1.1天。虽然出血和感染的再入院率没有显著差异，但患者满意度显著提高，疼痛管理改善至4.4±0.7，总体满意度改善至4.1±0.6（=0.03）。结论：在管理扁桃体切除术后康复、优化手术结果和提高患者满意度方面，AI辅助护理比传统方法具有显著优势。本研究支持进一步探索AI的长期结果及其在各个手术领域的应用。
在线版本包含可在10.1007/s12070-024-05103-x获取的补充材料。
