智能手机聊天机器人应用程序优化老年癌症患者监测
A smartphone Chatbot application to optimize monitoring of older patients with cancer
BACKGROUND: Almost two thirds of patients diagnosed with cancer are age 65 years or older. In order to follow up on older patients with cancer receiving chemotherapy at home, we implemented remote phone monitoring conducted by skilled oncology nurses. However, given the rising number of patients assessed and the limited time that hospital professionals can spend on their patients after discharge, we needed to modernize this program. In this paper we present the preliminary results and the ongoing evaluation. METHOD: We implemented a semi-automated messaging application to upgrade the current follow-up procedures. The primary aim is to collect patient's key data over time and to free up nurses' time so that during phone calls they can focus on education and support. The Chatbot feasibility was assessed in a sub-sample of unselected patients before its wider dissemination and pragmatic evaluation. MAIN RESULTS: During the first deployment period, 9 unselected patients benefited from the Chatbot (mean 83 y.o.) with a total of 52 completed remote evaluations. Each participant answered 6 questionnaires over 7 weeks with an 86% compliance rate. The average completion time for the questionnaires was 3.5 min and the answer rate was 100%. The 'free text' field was used in 58% of the questionnaires. The Chatbot solution is currently proposed to all eligible patients thanks to the regional cancer network support. We are measuring acceptability, health outcomes and health network impact. DISCUSSION AND CONCLUSION: The results of this first phase are encouraging. The integration of the solution into the health care organization was feasible and acceptable. Moreover, the answers revealed serious health (e.g. fever) or adherence (e.g. blood test) issues that require timely interventions. The major strength of this solution is to rely on end-users' current knowledge of technologies (text-messaging), which allows a seamless integration into a complex clinical network.
pmid: 31160007 Int J Med Inform 影响因子: 2.731 发表日期: 20190801 官网 免费下载
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