Building and experimenting with an agent-based model to study the population-level impact of CommunityRx, a clinic-based community resource referral intervention.
Building and experimenting with an agent-based model to study the population-level impact of CommunityRx, a clinic-based community resource referral intervention.
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DOI：
10.1371/journal.pcbi.1009471
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发表时间：
2021-10
影响因子：
4.3
通讯作者：
Tung EL
中科院分区：
文献类型：
--
作者：
Lindau ST;Makelarski JA;Kaligotla C;Abramsohn EM;Beiser DG;Chou C;Collier N;Huang ES;Macal CM;Ozik J;Tung EL
社区处方(Community Rx，CRx)是一种信息技术干预，为患者提供了个性化的健康社区资源列表(HealtheRx)。在反复的临床研究中，近一半接受HealtheRx临床“剂量”的人与其他人分享了他们的信息(“社会剂量”)。临床试验设计不能完全捕捉信息扩散的影响，信息扩散可以作为干预的力量倍增器。此外，还需要进行实验，以了解干预提供如何在不同情况下优化社会传播。为了研究CRX在不同条件下的信息扩散，我们建立了一个基于主体的模型。这项研究描述了模型的建立过程，并说明了ABM如何通过电子实验提供关于信息扩散的洞察。为了构建ABM，我们使用公开可用的数据源构建了一个合成种群(“代理”)。利用临床试验数据，我们开发了模拟代理人活动、资源知识演变和信息共享的经验知情过程。利用RepastHPC和chiSIM软件，复制矽肺干预，模拟信息扩散过程，生成应急信息扩散网络。使用经验数据校准CRX ABM，以在矽肺中复制CRX干预。我们使用ABM来量化通过社会剂量与临床剂量传播的信息，然后进行信息扩散实验，比较由医生、护士或临床办事员提供的干预的社会剂量效果。合成种群(N=802,191)表现出不同的行为特征，包括活动和知识进化模式。在电子传递中，干预被高保真地复制。在交换资源信息的代理之间出现了大规模的信息扩散网络。不同的信息交换倾向导致网络具有不同的拓扑特征。通过社会剂量传播的社区资源信息几乎是仅通过临床剂量传播的4倍，并且不随交付模式而变化。这项研究以CRX为例，展示了建立和实验ABM的过程，以研究临床信息干预的信息传播和人口水平的影响。虽然CRX ABM的重点是重现硅胶中的CRX干预，但所给出的建模和计算实验的一般过程可推广到其他大规模的信息扩散ABM。社区处方(CRX)是一种以临床为基础的干预措施，为患者提供有关社区资源的信息，用于维护和促进健康。先前的研究发现，接触CRX的人中有近一半与他人分享他们的资源信息。这项研究描述了基于代理的模型(ABM)的构建和实验，以检查CRX和其他健康信息干预措施通过直接暴露于干预措施的人的社会传播或“剂量”对更广泛社区的潜在影响。我们展示了我们如何整合临床试验、人口统计学和流行病学数据以及专家告密者的见解，以开发行为并将其分配给综合研究人群(代理人)。利用CRX临床试验数据，我们将干预传递给这些药物，并模拟信息传播。我们在电子实验中描述，以说明对ABM产生的信息传播的洞察，这些信息传播补充了临床试验结果。这项研究展示了如何使用来自个人水平的临床和人口研究的数据来创建一个计算实验室，以评估卫生信息干预的更广泛影响。除了鼓励将个体水平和系统科学方法整合到卫生信息干预研究中之外，这项研究还使同行审查能够为模型迭代和实验提供信息。
CommunityRx (CRx), an information technology intervention, provides patients with a personalized list of healthful community resources (HealtheRx). In repeated clinical studies, nearly half of those who received clinical “doses” of the HealtheRx shared their information with others (“social doses”). Clinical trial design cannot fully capture the impact of information diffusion, which can act as a force multiplier for the intervention. Furthermore, experimentation is needed to understand how intervention delivery can optimize social spread under varying circumstances. To study information diffusion from CRx under varying conditions, we built an agent-based model (ABM). This study describes the model building process and illustrates how an ABM provides insight about information diffusion through in silico experimentation. To build the ABM, we constructed a synthetic population (“agents”) using publicly-available data sources. Using clinical trial data, we developed empirically-informed processes simulating agent activities, resource knowledge evolution and information sharing. Using RepastHPC and chiSIM software, we replicated the intervention in silico, simulated information diffusion processes, and generated emergent information diffusion networks. The CRx ABM was calibrated using empirical data to replicate the CRx intervention in silico. We used the ABM to quantify information spread via social versus clinical dosing then conducted information diffusion experiments, comparing the social dosing effect of the intervention when delivered by physicians, nurses or clinical clerks. The synthetic population (N = 802,191) exhibited diverse behavioral characteristics, including activity and knowledge evolution patterns. In silico delivery of the intervention was replicated with high fidelity. Large-scale information diffusion networks emerged among agents exchanging resource information. Varying the propensity for information exchange resulted in networks with different topological characteristics. Community resource information spread via social dosing was nearly 4 fold that from clinical dosing alone and did not vary by delivery mode. This study, using CRx as an example, demonstrates the process of building and experimenting with an ABM to study information diffusion from, and the population-level impact of, a clinical information-based intervention. While the focus of the CRx ABM is to recreate the CRx intervention in silico, the general process of model building, and computational experimentation presented is generalizable to other large-scale ABMs of information diffusion. CommunityRx (CRx) is a clinic-based intervention that provides patients with information about community resources for health-maintenance and promotion. Prior work found that nearly half of people exposed to CRx share their resource information with others. This study describes construction of and experimentation with an agent-based model (ABM) to examine the potential impact of CRx and other health information interventions on the broader community via social spread or “dosing” from people directly exposed to the intervention. We show how we integrated clinical trial, demographic and epidemiologic data and expert informant insights to develop and assign behaviors to a synthetic study population (agents). Using CRx clinical trial data, we then delivered the intervention to these agents and simulated information spread. We describe in silico experimentation to illustrate insights about information spread generated by the ABM that complement clinical trial findings. This study shows how data from individual-level clinical and population studies can be used to create a computational laboratory to assess the broader impact of a health information intervention. In addition to inspiring integration of individual-level and systems science approaches to the study of health information interventions, this study enables peer review to inform model iteration and experimentation.
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DOI：
10.1080/10618600.2018.1442342
发表时间：
2018-01-01
影响因子：
2.4
作者：
Gallagher, Shannon;Richardson, Lee F.;Eddy, William F.
通讯作者：
Eddy, William F.
影响因子：
7.7
作者：
Galea, Sandro;Riddle, Matthew;Kaplan, George A.
通讯作者：
Kaplan, George A.
影响因子：
2.9
作者：
Carey G;Malbon E;Carey N;Joyce A;Crammond B;Carey A
通讯作者：
Carey A
影响因子：
56.9
作者：
Barabási, AL;Albert, R
通讯作者：
Albert, R
影响因子：
5
作者：
ANDERSEN, RM
通讯作者：
ANDERSEN, RM