Hypothesis generation using network structures on community health center cancer-screening performance

Abstract

Research Objectives Nationally sponsored cancer care quality improvement efforts have been deployed in community health centers to increase breast, cervical, and colorectal cancer screening rates among vulnerable populations. Despite some immediate and short-term gains screening rates remain below national benchmark objectives. Overall improvement has been both difficult to sustain over time in some organizational settings and/or diffuse to others as repeatable best practices. One reason is that facility-level changes typically occur in dynamic organizational environments that are complex, adaptive, and unpredictable. This study seeks to better understand the factors that help shape community health center facility-level cancer screening performance over time. This study applies a computational modeling approach that combines principles of health services research, health informatics, network theory, and systems science. Methods In order to investigate the role of knowledge acquisition, retention, and sharing within the setting of the community health center and the effect of this role on the relationship between clinical decision support capabilities and improvement in cancer screening rate improvement, we employed Construct TM to create simulated community health centers using previous collected point-in-time survey data. Construct TM is a multi-agent model of network evolution. Social, knowledge, and belief networks co-evolve. Groups and organizations are treated as complex systems, thus capturing the variability in human and organizational factors. In Construct TM, individuals and groups interact communicate, learn, and make decisions in a continuous cycle. Data from the survey was used to create high-performing simulated community health centers and low-performing ones based on extent of both computer decision support use and cancer-screening rates. Results Our virtual experiment revealed that patterns of overall network symmetry, agent cohesion, and connectedness varied by community health center performance level. Visual assessment of both the agent-to-agent knowledge sharing network and agent-to-resource knowledge use network diagrams demonstrated that community health centers labeled as high performers typically showed higher levels of collaboration and cohesiveness among agent classes, faster knowledge absorption rates, and fewer unconnected agents to key knowledge resources. Conclusions and Research Implications Using the point-in-time survey data outlining community health center cancer screening practices our computational model successfully distinguished between high and low performers. Our study showed that high performance environments displayed distinctive network characteristics in patterns of interaction among agents, as well as in the access and utilization of key knowledge resources. Our study demonstrated how non-network specific data obtained from a point-in-time survey can be employed to forecast community health center performance over time and thereby enhance sustainability of long-term...

Using computational modeling to assess the impact of clinical decision support on cancer screening improvement strategies within the community health centers

“…Carney et al describe the staging of the data for input into Construct-TM, as well as details on the modeling methods, task definitions, knowledge definitions, and performance level descriptive statistics [39]. The focus of this manuscript will be on the comparative results of the two types of modeling used in this dual modeling experimental design.…”

“…The purpose of this study was to examine CST agent as it was projected to be influenced by CDS exclusively for comparison with our Spearman’s Rho test. Our companion manuscript highlighted the use of all five our agent classifications including provider perspective, administrator perspective, collaborator perspective, health information technology perspective, and CST perspective [39]. …”

“…Information input into the model in mathematical form consisted of (1) a glossary of the simulation’s variables, (2) agent, knowledge, and task definitions, (3) definitions of model nodes, to which simulation entities are assigned, and (4) types of networks to be used. For more details on this facet of the study please see our companion manuscript [39]…”

A Computational Perspective of Knowledge Empowerment for Healthcare Decision Making

“…Our relational algorithm represents a necessary first step in designing complex computational experiments that examines the environmental contextual factors which shape cognitive demand and stakeholder engagement in health-related knowledge-commerce to address varying levels of stakeholder uncertainty and manage risk. We seek to build models that will allow us to better understand discrete changes of the knowledge life cycle within a defined health-system over the course of time (e.g., 1, 3, 5, 10, 25 years) (Carney et al, 2014(Carney et al, , 2015. This is not unlike our previous research where we examined the rate of knowledge in a network of community health centers over a ten-year simulated period to compare high and low performance in breast, cervical, and colorectal cancer screening among vulnerable populations.…”

“…Computational models are commonly used to provide information on scenarios in which traditional methods of data collection are not possible, suitable, or ethical (Carley, 1999;Carney et al, 2015). Computational models can be used to conceptualize the core components that directly and indirectly impact on complex healthcare issues like cancer health disparities in a variety of organizational and social settings (Burton & Obel, 2004;Kling, 1993).…”