Hypothesis generation using network structures on community health center cancer-screening performance.
OBJECTIVE
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 several 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 challenging to diffuse to other settings as repeatable best practices. Reasons for this include facility-level changes, which typically occur in dynamic organizational environments that are complex, adaptive, and unpredictable. This study seeks to understand the factors that shape community health center facility-level cancer-screening performance over time. This study applies a computational-modeling approach, combining principles of health-services research, health informatics, network theory, and systems science.


METHODS
To investigate the roles of knowledge acquisition, retention, and sharing within the setting of the community health center and to examine their effects 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 previously collected point-in-time survey data. Construct-TM is a multi-agent model of network evolution. Because social, knowledge, and belief networks co-evolve, groups and organizations are treated as complex systems to capture the variability of human and organizational factors. In Construct-TM, individuals and groups interact by communicating, learning, and making decisions in a continuous cycle. Data from the survey was used to differentiate high-performing simulated community health centers from low-performing ones based on computer-based decision support usage and self-reported cancer-screening improvement.


RESULTS
This 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 agents that were unconnected 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. Results indicated 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, thereby enhancing the sustainability of long-term strategic-improvement efforts. Our results revealed a strategic profile for community health center cancer-screening improvement via simulation over a projected 10-year period. The use of computational modeling allows additional inferential knowledge to be drawn from existing data when examining organizational performance in increasingly complex environments.
Indiana University School of Informatics (IUPUI), United States; University of North Carolina, Gillings School of Global Public Health, United States; Global Health Equity Intelligence Collaborative (GHEIC), LLC, United States. Electronic address: tcarney@unc.edu.
Timothy Jay
TJ
Carney
Carnegie Mellon University, Computational Analysis of Social and Organizational Systems (CASOS), United States. Electronic address: gmorgan@cs.cmu.edu.
Geoffrey P
GP
Morgan
Indiana University School of Informatics (IUPUI), United States. Electronic address: jofjones@iupui.edu.
Josette
J
Jones
Indiana University School of Informatics (IUPUI), United States; Indiana University School of Nursing, United States. Electronic address: amcdanie@iupui.edu.
Anna M
AM
McDaniel
Indiana University School of Nursing, United States. Electronic address: mtweaver@iupui.edu.
Michael T
MT
Weaver
University of North Carolina, Gillings School of Global Public Health, United States. Electronic address: weiner@email.unc.edu.
Bryan
B
Weiner
VA HSR&D Center of Excellence on Implementing Evidence-based Practice, Roudebush VA Medical Center, United States; Division of General Internal Medicine, Indiana University School of Medicine, United States; IU Center for Health Services and Outcomes Research, Regenstrief Institute, Inc., United States. Electronic address: dahaggst@iupui.edu.
David A
DA
Haggstrom
D003151:Community Health Centers; D003198:Computer Simulation; D003299:Cooperative Behavior; D020000:Decision Support Systems, Clinical; D055088:Early Detection of Cancer / Q000592:standards*; D006801:Humans; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care; D058996:Quality Improvement
Cancer screening; Community health centers; Computational modeling; Health Disparities; Learning health system; Network theory; Simulation; Systems-thinking