Bickmore Timothy W, Mitchell Suzanne E, Jack Brian W, Paasche-Orlow Michael K, Pfeifer Laura M, Odonnell Julie
College of Computer and Information Science, Northeastern University 360 Huntington Ave, WVH202, Boston, MA 02115 USA.
Interact Comput. 2010 Jul 1;22(4):289-298. doi: 10.1016/j.intcom.2009.12.001.
Depression affects approximately 15% of the US population, and is recognized as an important risk factor for poor outcomes among patients with various illnesses. Automated health education and behavior change programs have the potential to help address many of the shortcomings in health care. However, the role of these systems in the care of patients with depression has been insufficiently examined. In the current study, we sought to evaluate how hospitalized medical patients would respond to a computer animated conversational agent that has been developed to provide information in an empathic fashion about a patient's hospital discharge plan. In particular, we sought to examine how patients who have a high level of depressive symptoms respond to this system. Therapeutic alliance-the trust and belief that a patient and provider have in working together to achieve a desired therapeutic outcome- was used as the primary outcome measure, since it has been shown to be important in predicting outcomes across a wide range of health problems, including depression. In an evaluation of 139 hospital patients who interacted with the agent at the time of discharge, all patients, regardless of depressive symptoms, rated the agent very high on measures of satisfaction and ease of use, and most preferred receiving their discharge information from the agent compared to their doctors or nurses in the hospital. In addition, we found that patients with symptoms indicative of major depression rated the agent significantly higher on therapeutic alliance compared to patients who did not have major depressive symptoms. We conclude that empathic agents represent a promising technology for patient assessment, education and counseling for those most in need of comfort and caring in the inpatient setting.
抑郁症影响着约15%的美国人口，并且被认为是各类疾病患者预后不良的一个重要风险因素。自动化健康教育和行为改变项目有潜力帮助解决医疗保健中的许多不足。然而，这些系统在抑郁症患者护理中的作用尚未得到充分研究。在当前的研究中，我们试图评估住院的内科患者对一个计算机动画对话代理的反应，该代理旨在以共情的方式提供有关患者出院计划的信息。特别是，我们试图研究具有高水平抑郁症状的患者对该系统的反应。治疗联盟——患者和医护人员共同努力实现预期治疗结果的信任和信念——被用作主要的结果指标，因为它已被证明在预测包括抑郁症在内的广泛健康问题的结果方面很重要。在对139名出院时与该代理互动的住院患者的评估中，所有患者，无论有无抑郁症状，在满意度和易用性方面对该代理的评价都很高，并且与医院的医生或护士相比，大多数患者更喜欢从该代理那里获取出院信息。此外，我们发现，与没有重度抑郁症状的患者相比，有重度抑郁症状的患者在治疗联盟方面对该代理的评价显著更高。我们得出结论，对于住院环境中最需要安慰和关怀的人来说，共情代理是一种很有前景的用于患者评估、教育和咨询的技术。
