PDF(Pubmed)
Abstract：
BACKGROUND: Throughout the COVID-19 pandemic, multiple policies and guidelines were issued and updated for health care personnel (HCP) for COVID-19 testing and returning to work after reporting symptoms, exposures, or infection. The high frequency of changes and complexity of the policies made it difficult for HCP to understand when they needed testing and were eligible to return to work (RTW), which increased calls to Occupational Health Services (OHS), creating a need for other tools to guide HCP. Chatbots have been used as novel tools to facilitate immediate responses to patients\' and employees\' queries about COVID-19, assess symptoms, and guide individuals to appropriate care resources.
OBJECTIVE: This study aims to describe the development of an RTW chatbot and report its impact on demand for OHS support services during the first Omicron variant surge.
METHODS: This study was conducted at Mass General Brigham, an integrated health care system with over 80,000 employees. The RTW chatbot was developed using an agile design methodology. We mapped the RTW policy into a unified flow diagram that included all required questions and recommendations, then built and tested the chatbot using the Microsoft Azure Healthbot Framework. Using chatbot data and OHS call data from December 10, 2021, to February 17, 2022, we compared OHS resource use before and after the deployment of the RTW chatbot, including the number of calls to the OHS hotline, wait times, call length, and time OHS hotline staff spent on the phone. We also assessed Centers for Disease Control and Prevention data for COVID-19 case trends during the study period.
RESULTS: In the 5 weeks post deployment, 5575 users used the RTW chatbot with a mean interaction time of 1 minute and 17 seconds. The highest engagement was on January 25, 2022, with 368 users, which was 2 weeks after the peak of the first Omicron surge in Massachusetts. Among users who completed all the chatbot questions, 461 (71.6%) met the RTW criteria. During the 10 weeks, the median (IQR) number of daily calls that OHS received before and after deployment of the chatbot were 633 (251-934) and 115 (62-167), respectively (U=163; P<.001). The median time from dialing the OHS phone number to hanging up decreased from 28 minutes and 22 seconds (IQR 25:14-31:05) to 6 minutes and 25 seconds (IQR 5:32-7:08) after chatbot deployment (U=169; P<.001). Over the 10 weeks, the median time OHS hotline staff spent on the phone declined from 3 hours and 11 minutes (IQR 2:32-4:15) per day to 47 (IQR 42-54) minutes (U=193; P<.001), saving approximately 16.8 hours per OHS staff member per week.
CONCLUSIONS: Using the agile methodology, a chatbot can be rapidly designed and deployed for employees to efficiently receive guidance regarding RTW that complies with the complex and shifting RTW policies, which may reduce use of OHS resources.
OBJECTIVE: This study aims to describe the development of an RTW chatbot and report its impact on demand for OHS support services during the first Omicron variant surge.
METHODS: This study was conducted at Mass General Brigham, an integrated health care system with over 80,000 employees. The RTW chatbot was developed using an agile design methodology. We mapped the RTW policy into a unified flow diagram that included all required questions and recommendations, then built and tested the chatbot using the Microsoft Azure Healthbot Framework. Using chatbot data and OHS call data from December 10, 2021, to February 17, 2022, we compared OHS resource use before and after the deployment of the RTW chatbot, including the number of calls to the OHS hotline, wait times, call length, and time OHS hotline staff spent on the phone. We also assessed Centers for Disease Control and Prevention data for COVID-19 case trends during the study period.
RESULTS: In the 5 weeks post deployment, 5575 users used the RTW chatbot with a mean interaction time of 1 minute and 17 seconds. The highest engagement was on January 25, 2022, with 368 users, which was 2 weeks after the peak of the first Omicron surge in Massachusetts. Among users who completed all the chatbot questions, 461 (71.6%) met the RTW criteria. During the 10 weeks, the median (IQR) number of daily calls that OHS received before and after deployment of the chatbot were 633 (251-934) and 115 (62-167), respectively (U=163; P<.001). The median time from dialing the OHS phone number to hanging up decreased from 28 minutes and 22 seconds (IQR 25:14-31:05) to 6 minutes and 25 seconds (IQR 5:32-7:08) after chatbot deployment (U=169; P<.001). Over the 10 weeks, the median time OHS hotline staff spent on the phone declined from 3 hours and 11 minutes (IQR 2:32-4:15) per day to 47 (IQR 42-54) minutes (U=193; P<.001), saving approximately 16.8 hours per OHS staff member per week.
CONCLUSIONS: Using the agile methodology, a chatbot can be rapidly designed and deployed for employees to efficiently receive guidance regarding RTW that complies with the complex and shifting RTW policies, which may reduce use of OHS resources.
摘要：
背景：在整个COVID-19大流行期间，为医护人员（HCP）发布并更新了多项政策和指南，以进行COVID-19检测并在报告症状后重返工作岗位，暴露,或感染。政策的高频率变化和复杂性使得HCP很难理解他们何时需要测试并且有资格重返工作岗位（RTW）。这增加了对职业健康服务(OHS)的呼叫，需要其他工具来指导HCP。聊天机器人已被用作新颖的工具，以促进对患者和员工关于COVID-19的查询的即时反应，评估症状，并引导个人获得适当的护理资源。
目的：本研究旨在描述RTW聊天机器人的开发，并报告其在首次Omicron变体激增期间对OHS支持服务需求的影响。
方法：这项研究是在MassGeneralBrigham进行的，拥有超过8万名员工的综合医疗保健系统。RTW聊天机器人是使用敏捷设计方法开发的。我们将RTW策略映射到统一的流程图中，其中包括所有必需的问题和建议，然后使用MicrosoftAzureHealthbot框架构建和测试聊天机器人。使用2021年12月10日至2022年2月17日的聊天机器人数据和OHS呼叫数据，我们比较了RTW聊天机器人部署前后的OHS资源使用情况。包括打给OHS热线的次数,等待时间,呼叫长度,以及OHS热线工作人员在电话上花费的时间。我们还评估了疾病控制和预防中心在研究期间的COVID-19病例趋势数据。
结果：在部署后的5周内，5575名用户使用RTW聊天机器人，平均交互时间为1分17秒。参与度最高的是2022年1月25日，有368个用户，这是在马萨诸塞州第一次Omicron激增达到峰值后的两周。在完成所有聊天机器人问题的用户中，461（71.6%）符合RTW标准。在这10周内,在部署聊天机器人之前和之后，OHS每天收到的电话中位数（IQR）分别为633（251-934）和115（62-167），分别为(U=163；P<.001)。从拨打OHS电话号码到挂断电话的中位时间从28分22秒（IQR25:14-31:05）减少到6分25秒（IQR5:32-7:08）部署聊天机器人（U=169；P<.001）。在这10周内,OHS热线工作人员在电话上花费的平均时间从每天3小时11分钟（IQR2:32-4:15）下降到47分钟（IQR42-54）（U=193；P<.001），每个OHS员工每周节省约16.8小时。
结论：使用敏捷方法，可以为员工快速设计和部署聊天机器人，以有效地接收有关RTW的指导，该指导符合复杂且不断变化的RTW政策，这可能会减少OHS资源的使用。
目的：本研究旨在描述RTW聊天机器人的开发，并报告其在首次Omicron变体激增期间对OHS支持服务需求的影响。
方法：这项研究是在MassGeneralBrigham进行的，拥有超过8万名员工的综合医疗保健系统。RTW聊天机器人是使用敏捷设计方法开发的。我们将RTW策略映射到统一的流程图中，其中包括所有必需的问题和建议，然后使用MicrosoftAzureHealthbot框架构建和测试聊天机器人。使用2021年12月10日至2022年2月17日的聊天机器人数据和OHS呼叫数据，我们比较了RTW聊天机器人部署前后的OHS资源使用情况。包括打给OHS热线的次数,等待时间,呼叫长度,以及OHS热线工作人员在电话上花费的时间。我们还评估了疾病控制和预防中心在研究期间的COVID-19病例趋势数据。
结果：在部署后的5周内，5575名用户使用RTW聊天机器人，平均交互时间为1分17秒。参与度最高的是2022年1月25日，有368个用户，这是在马萨诸塞州第一次Omicron激增达到峰值后的两周。在完成所有聊天机器人问题的用户中，461（71.6%）符合RTW标准。在这10周内,在部署聊天机器人之前和之后，OHS每天收到的电话中位数（IQR）分别为633（251-934）和115（62-167），分别为(U=163；P<.001)。从拨打OHS电话号码到挂断电话的中位时间从28分22秒（IQR25:14-31:05）减少到6分25秒（IQR5:32-7:08）部署聊天机器人（U=169；P<.001）。在这10周内,OHS热线工作人员在电话上花费的平均时间从每天3小时11分钟（IQR2:32-4:15）下降到47分钟（IQR42-54）（U=193；P<.001），每个OHS员工每周节省约16.8小时。
结论：使用敏捷方法，可以为员工快速设计和部署聊天机器人，以有效地接收有关RTW的指导，该指导符合复杂且不断变化的RTW政策，这可能会减少OHS资源的使用。
