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

Timothy Jay Carney, Geoffrey P. Morgan, Josette Jones, Anna M. McDaniel, Michael T. Weaver, Bryan Weiner, David Haggstrom

Research output: Contribution to journalArticle

3 Citations (Scopus)

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 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.

Original languageEnglish (US)
Pages (from-to)288-307
Number of pages20
JournalJournal of Biomedical Informatics
Volume57
DOIs
StatePublished - Oct 1 2015

Fingerprint

Community Health Centers
Early Detection of Cancer
Screening
Health
Clinical Decision Support Systems
Benchmarking
Informatics
Health Services Research
Health Facilities
Vulnerable Populations
Quality Improvement
Systems science
Practice Guidelines
Research
Knowledge acquisition
Uterine Cervical Neoplasms
Circuit theory
Bayesian networks
Colorectal Neoplasms
Decision Making

Keywords

  • Cancer screening
  • Community health centers
  • Computational modeling
  • Health Disparities
  • Learning health system
  • Network theory
  • Simulation
  • Systems-thinking

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

Cite this

Hypothesis generation using network structures on community health center cancer-screening performance. / Carney, Timothy Jay; Morgan, Geoffrey P.; Jones, Josette; McDaniel, Anna M.; Weaver, Michael T.; Weiner, Bryan; Haggstrom, David.

In: Journal of Biomedical Informatics, Vol. 57, 01.10.2015, p. 288-307.

Research output: Contribution to journalArticle

Carney, Timothy Jay ; Morgan, Geoffrey P. ; Jones, Josette ; McDaniel, Anna M. ; Weaver, Michael T. ; Weiner, Bryan ; Haggstrom, David. / Hypothesis generation using network structures on community health center cancer-screening performance. In: Journal of Biomedical Informatics. 2015 ; Vol. 57. pp. 288-307.
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