识别关键决定因素对于提高项目实施和在医疗组织中实现长期维持至关重要。多方利益相关者之间组织层次的复杂性和异构性可能会使我们对项目实施的理解变得复杂。我们描述了两种数据可视化方法，用于操作实施成功并整合和选择进一步分析的实施因素。我们使用了过程映射和矩阵热图的组合，以系统地综合和可视化来自9个医疗组织的66个利益相关者访谈的定性数据，以表征所有新诊断结肠和子宫内膜癌普遍肿瘤筛查程序，并了解背景因素对实施的影响。我们构建了可视化流程图来比较流程并对流程优化组件进行评分。我们还使用色彩编码的矩阵来使用实施研究一致性框架中的因素系统地编码、汇总和合并背景数据。将组合分数可视化为最终数据矩阵热图。我们创建了19幅流程图，以直观地表示每种方案的流程。流程图标识出以下差距和低效：方案不一致的执行、没有例行反射测试、正筛选后参照无一致性、没有数据跟踪的证据以及缺乏质量保证措施。这些障碍帮助我们定义了五个流程优化组件，并使用它们将程序优化量化为从0（无程序）到5（优化）的比例，表示程序实施和最佳维护的程度。最终数据矩阵热图中的组合分数显示出跨优化程序、非优化程序和无程序组织的背景因素模式。过程映射提供了一种有效的方法，以可视化的方式比较不同地点的病人流程、提供者互动以及处理差距和低效，从而通过优化分数测量实施成功。矩阵热图在数据可视化和合并方面证明了其有用性，从而产生了一个跨站点比较和选择相关CFIR因素的综述矩阵。结合这些工具，可以在进行正式巧合分析之前以系统和透明的方式了解复杂的组织异质性，介绍逐步方法来进行数据合并和因素选择。©2023年，作者。

Identifying key determinants is crucial for improving program implementation and achieving long-term sustainment within healthcare organizations. Organizational-level complexity and heterogeneity across multiple stakeholders can complicate our understanding of program implementation. We describe two data visualization methods used to operationalize implementation success and to consolidate and select implementation factors for further analysis.We used a combination of process mapping and matrix heat mapping to systematically synthesize and visualize qualitative data from 66 stakeholder interviews across nine healthcare organizations, to characterize universal tumor screening programs of all newly diagnosed colorectal and endometrial cancers and understand the influence of contextual factors on implementation. We constructed visual representations of protocols to compare processes and score process optimization components. We also used color-coded matrices to systematically code, summarize, and consolidate contextual data using factors from the Consolidated Framework for Implementation Research (CFIR). Combined scores were visualized in a final data matrix heat map.Nineteen process maps were created to visually represent each protocol. Process maps identified the following gaps and inefficiencies: inconsistent execution of the protocol, no routine reflex testing, inconsistent referrals after a positive screen, no evidence of data tracking, and a lack of quality assurance measures. These barriers in patient care helped us define five process optimization components and used these to quantify program optimization on a scale from 0 (no program) to 5 (optimized), representing the degree to which a program is implemented and optimally maintained. Combined scores within the final data matrix heat map revealed patterns of contextual factors across optimized programs, non-optimized programs, and organizations with no program.Process mapping provided an efficient method to visually compare processes including patient flow, provider interactions, and process gaps and inefficiencies across sites, thereby measuring implementation success via optimization scores. Matrix heat mapping proved useful for data visualization and consolidation, resulting in a summary matrix for cross-site comparisons and selection of relevant CFIR factors. Combining these tools enabled a systematic and transparent approach to understanding complex organizational heterogeneity prior to formal coincidence analysis, introducing a stepwise approach to data consolidation and factor selection.© 2023. The Author(s).