Development of an Innovative Digital Data Collection System for Routine Mental Health Care Delivery in Rural Haiti.
Development of an Innovative Digital Data Collection System for Routine Mental Health Care Delivery in Rural Haiti.
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DOI：
10.9745/ghsp-d-20-00486
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发表时间：
2021-12-31
期刊：
影响因子：
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通讯作者：
Raviola G
中科院分区：
文献类型：
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作者：
Rose AL;Fenelon DL;Fils-Aimé JR;Dubuisson W;Singer SFC;Smith SL;Jerome G;Eustache E;Raviola G
全世界资源匮乏地区的精神卫生信息系统都很稀缺。数据收集准确，但在海地农村卫生中心使用任务共享临床提供者进行数据收集时，可持续的人员配置是一个挑战。将心理健康数据收集整合到现有数据收集系统中将有助于缩小这一关键差距。使用分担任务的医护人员收集数据时，平衡临床职责时间与数据收集时间是一个关键挑战。向用户反馈数据、持续监督以及奖励高绩效者的机会可能有助于增加提供商对新数据收集系统的支持。可能需要将纸质表格与数字数据收集系统一起保留，或直接在数字数据收集系统中包含决策支持工具，以充分支持卫生保健工作者在任务共享的心理健康系统中的学习。在设计数字数据收集项目时，项目经理应从一开始就仔细考虑可持续的人员配置。项目经理应考虑数字数据收集系统如何整合决策支持工具，以可持续地支持任务共享提供者的持续学习。政策制定者应将心理健康数据收集整合到其他数据系统中，以利用心理健康的基础设施和资源。有效的数字健康管理信息系统（HMIS）支持健康数据的有效性，使医疗保健团队能够做出规划性决策和国家级决策，以支持国际发展目标。 2015年，精神卫生被纳入可持续发展目标，但在资源有限的环境中，任何类型的HMIS在精神卫生保健实践中的应用都很少。 Zanmi Lasante (ZL) 是海地最大的精神卫生保健提供商之一，为 11 个农村公共卫生机构的精神健康开发了数字数据收集系统。我们描述了 ZL 心理健康数据收集数字系统的开发、实施和评估。为了评估系统可靠性，我们评估了丢失的月度报告的数量。为了评估数据的有效性，我们计算了 2 个设施的数字系统和纸质图表之间的一致性。为了评估系统为决策提供信息的能力，我们指定并计算了 4 个优先指标。所有设施的 143 份月度报告中，数字系统缺失了 5 份，与纸质图表的一致性为 74.3% (55/74) 和 98% (49/50)。可以计算所有 4 个指标，这导致了 2 个案例的程序变更。为了应对实施挑战，有必要采取策略来增加提供商的支持，并最终引入专门的数据文员，以跟上数据收集的步伐并保护临床工作的时间。在展示在资源匮乏的农村环境中以数字方式收集心理健康数据的潜力的同时，我们发现有必要考虑纸质记录与数字数据收集的持续作用。我们还发现了在有限的员工之间平衡临床和数据收集职责的挑战。需要持续的工作来开发真正可持续和可扩展的模型，用于在资源有限的环境中收集心理健康数据。
Mental health information systems in low-resource settings are scarce worldwide. Data collection was accurate, yet sustainable staffing was a challenge when using task-shared clinical providers for data collection in health centers in rural Haiti. Integrating mental health data collection within existing data collection systems would help close this key gap. Balancing time for clinical duties with time for data collection was a key challenge when using task-shared health care workers to collect data. Feedback of data to users, ongoing supervision, and opportunities for rewards for high-performers may help increase provider buy-in for new data collection systems. Retaining paper forms alongside digital data collection systems or the inclusion of decision support tools directly within digital data collection systems may be needed to fully support health care worker learning in a task-shared mental health system. Program managers should carefully consider sustainable staffing from the beginning when designing digital data collection projects. Program managers should consider how digital data collection systems can incorporate decision support tools to sustainably support ongoing learning of task-shared providers. Policy makers should integrate mental health data collection within other data systems to leverage infrastructure and resources for mental health. Effective digital health management information systems (HMIS) support health data validity, which enables health care teams to make programmatic decisions and country-level decision making in support of international development targets. In 2015, mental health was included within the Sustainable Development Goals, yet there are few applications of HMIS of any type in the practice of mental health care in resource-limited settings. Zanmi Lasante (ZL), one of the largest providers of mental health care in Haiti, developed a digital data collection system for mental health across 11 public rural health facilities. We describe the development, implementation, and evaluation of the digital system for mental health data collection at ZL. To evaluate system reliability, we assessed the number of missing monthly reports. To evaluate data validity, we calculated concordance between the digital system and paper charts at 2 facilities. To evaluate the system's ability to inform decision making, we specified and then calculated 4 priority indicators. The digital system was missing 5 of 143 monthly reports across all facilities and had 74.3% (55/74) and 98% (49/50) concordance with paper charts. It was possible to calculate all 4 indicators, which led to programmatic changes in 2 cases. In response to implementation challenges, it was necessary to use strategies to increase provider buy-in and ultimately to introduce dedicated data clerks to keep pace with data collection and protect time for clinical work. While demonstrating the potential of collecting mental health data digitally in a low-resource rural setting, we found that it was necessary to consider the ongoing roles of paper records alongside digital data collection. We also identified the challenge of balancing clinical and data collection responsibilities among a limited staff. Ongoing work is needed to develop truly sustainable and scalable models for mental health data collection in resource-limited settings.