A randomized controlled trial of a novel artificial-intelligence based smartphone application to optimize the management of cancer-related pain.

医学 随机对照试验 生活质量(医疗保健) 简短疼痛清单 癌症疼痛 癌症 缓和医疗 焦虑 物理疗法 内科学 慢性疼痛 护理部 精神科
作者
Mihir Kamdar,Amanda Centi,Nils Fischer,Kamal Jethwani
出处
期刊:Journal of Clinical Oncology [American Society of Clinical Oncology]
卷期号:36 (34_suppl): 76-76 被引量:10
标识
DOI:10.1200/jco.2018.36.34_suppl.76
摘要

76 Background: Cancer pain affects 70-90% of advanced malignancy patients, resulting in impaired quality of life and increased healthcare utilization. Novel care delivery models are needed to optimize care for patients dealing with cancer-related pain in between clinic visits. ePAL is a smartphone application(app) that regularly monitors pain and uses artificial intelligence(AI) to differentiate urgent from non-urgent issues to intercede in real time. The purpose of this randomized controlled trial was to determine ePAL's impact on pain severity, attitudes toward cancer treatment, and healthcare utilization in patients with cancer pain. Methods: MGH Palliative Care Clinic Patients with pain from metastatic, solid-organ cancer (n=112) were recruited and randomized to either a control group (n=56) that received usual care or an intervention group (n=56) that used the ePAL app in addition to usual care for 8 weeks. The app assessed pain 3 times/week and questionnaires about pain (BPI), attitudes towards cancer treatment(BQ-II), and general anxiety(GAD-7) were given at 0, 4, and 8 weeks. A repeated measures mixed model approach assessed how outcome measures changed over time. Models controlled for baseline differences at enrollment and random slopes in addition to baseline depression score, age and sex(alpha=0.05). Results: Pain severity (BPI) and negative attitudes toward cancer pain treatment (BQ-II) decreased significantly for those using the app compared to controls(coeff. -0.09, 95% CI: -0.17, -0.007, p=0.034 and coeff. -0.037, 95% CI: -0.072, -0.001, p=0.042 respectively). Anxiety scores increased for those using ePAL compared to controls (coeff. 0.21, 95% CI: 0.039, 0.37, p=0.015). Over 8-weeks, ePAL users had 40% fewer inpatient hospital admissions compared to controls (n=15 vs. n=25, p=0.048). Conclusions: To our knowledge, this is the first mobile app to utilize AI and clinical algorithms to significantly decrease pain and reduce overall inpatient hospitalizations in patients with cancer-related pain. Clinical trial information: NCT02069743.

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