PDF(Pubmed)
Abstract：
UNASSIGNED: Many digital health interventions (DHIs), including mobile health (mHealth) apps, aim to improve both client outcomes and efficiency like electronic medical record systems (EMRS). Although interoperability is the gold standard, it is also complex and costly, requiring technical expertise, stakeholder permissions, and sustained funding. Manual data linkage processes are commonly used to \"integrate\" across systems and allow for assessment of DHI impact, a best practice, before further investment. For mHealth, the manual data linkage workload, including related monitoring and evaluation (M&E) activities, remains poorly understood.
UNASSIGNED: As a baseline study for an open-source app to mirror EMRS and reduce healthcare worker (HCW) workload while improving care in the Nurse-led Community-based Antiretroviral therapy Program (NCAP) in Lilongwe, Malawi, we conducted a time-motion study observing HCWs completing data management activities, including routine M&E and manual data linkage of individual-level app data to EMRS. Data management tasks should reduce or end with successful app implementation and EMRS integration. Data was analysed in Excel.
UNASSIGNED: We observed 69:53:00 of HCWs performing routine NCAP service delivery tasks: 39:52:00 (57%) was spent completing M&E data related tasks of which 15:57:00 (23%) was spent on manual data linkage workload, alone.
UNASSIGNED: Understanding the workload to ensure quality M&E data, including to complete manual data linkage of mHealth apps to EMRS, provides stakeholders with inputs to drive DHI innovations and integration decision making. Quantifying potential mHealth benefits on more efficient, high-quality M&E data may trigger new innovations to reduce workloads and strengthen evidence to spur continuous improvement.
UNASSIGNED: As a baseline study for an open-source app to mirror EMRS and reduce healthcare worker (HCW) workload while improving care in the Nurse-led Community-based Antiretroviral therapy Program (NCAP) in Lilongwe, Malawi, we conducted a time-motion study observing HCWs completing data management activities, including routine M&E and manual data linkage of individual-level app data to EMRS. Data management tasks should reduce or end with successful app implementation and EMRS integration. Data was analysed in Excel.
UNASSIGNED: We observed 69:53:00 of HCWs performing routine NCAP service delivery tasks: 39:52:00 (57%) was spent completing M&E data related tasks of which 15:57:00 (23%) was spent on manual data linkage workload, alone.
UNASSIGNED: Understanding the workload to ensure quality M&E data, including to complete manual data linkage of mHealth apps to EMRS, provides stakeholders with inputs to drive DHI innovations and integration decision making. Quantifying potential mHealth benefits on more efficient, high-quality M&E data may trigger new innovations to reduce workloads and strengthen evidence to spur continuous improvement.
摘要：
■许多数字健康干预措施(DHIs)，包括移动健康(mHealth)应用程序，旨在提高客户的结果和效率，如电子病历系统(EMRS)。尽管互操作性是黄金标准，它也是复杂和昂贵的，需要技术专长，利益相关者权限,和持续的资金。手动数据链接过程通常用于跨系统“集成”，并允许评估DHI影响，最佳实践,在进一步投资之前。对于mHealth，手动数据联动工作量，包括相关的监测和评估(M&E)活动，仍然知之甚少。
作为一项开源应用程序的基线研究，该应用程序可以反映EMRS并减少医护人员（HCW）的工作量，同时改善由护士领导的基于社区的抗逆转录病毒治疗计划（NCAP）的护理。马拉维,我们进行了时间运动研究，观察HCWs完成数据管理活动，包括常规M&E和个人级别应用程序数据到EMRS的手动数据链接。数据管理任务应该通过成功的应用程序实施和EMRS集成来减少或结束。在Excel中分析数据。
■我们观察到69:53:00的HCWs执行常规NCAP服务交付任务：39:52:00(57%)用于完成M&E数据相关任务，其中15:57:00(23%)用于手动数据链接工作负载，独自一人。
■了解工作负载以确保高质量的M＆E数据，包括完成mHealth应用程序到EMRS的手动数据链接，为利益相关者提供投入，以推动DHI创新和集成决策。量化潜在的mHealth益处，提高效率，高质量的M＆E数据可能会引发新的创新，以减少工作量并加强证据以刺激持续改进。
作为一项开源应用程序的基线研究，该应用程序可以反映EMRS并减少医护人员（HCW）的工作量，同时改善由护士领导的基于社区的抗逆转录病毒治疗计划（NCAP）的护理。马拉维,我们进行了时间运动研究，观察HCWs完成数据管理活动，包括常规M&E和个人级别应用程序数据到EMRS的手动数据链接。数据管理任务应该通过成功的应用程序实施和EMRS集成来减少或结束。在Excel中分析数据。
■我们观察到69:53:00的HCWs执行常规NCAP服务交付任务：39:52:00(57%)用于完成M&E数据相关任务，其中15:57:00(23%)用于手动数据链接工作负载，独自一人。
■了解工作负载以确保高质量的M＆E数据，包括完成mHealth应用程序到EMRS的手动数据链接，为利益相关者提供投入，以推动DHI创新和集成决策。量化潜在的mHealth益处，提高效率，高质量的M＆E数据可能会引发新的创新，以减少工作量并加强证据以刺激持续改进。
