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J Biomed Inform. 2014 Oct;51:200-9. doi: 10.1016/j.jbi.2014.05.012. Epub 2014 Jun 18.

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

Author information

1
Indiana University School of Informatics (IUPUI), United States; University of North Carolina, Gillings School of Global Public Health, United States. Electronic address: tcarney@unc.edu.
2
Carnegie Mellon University, Computational Analysis of Social and Organizational Systems (CASOS), United States. Electronic address: gmorgan@cs.cmu.edu.
3
Indiana University School of Informatics (IUPUI), United States. Electronic address: jofjones@iupui.edu.
4
Indiana University School of Informatics (IUPUI), United States; Indiana University School of Nursing, United States. Electronic address: amcdanie@iupui.edu.
5
Indiana University School of Nursing, United States. Electronic address: mtweaver@iupui.edu.
6
University of North Carolina, Gillings School of Global Public Health, United States. Electronic address: weiner@email.unc.edu.
7
VA HSR&D Center of Excellence on Implementing Evidence-based Practice, United States; Roudebush VA Medical Center Regenstrief Institute, Inc., United States; Division of General Internal Medicine, Indiana University School of Medicine, United States. Electronic address: dahaggst@iupui.edu.

Abstract

Our conceptual model demonstrates our goal to investigate the impact of clinical decision support (CDS) utilization on cancer screening improvement strategies in the community health care (CHC) setting. We employed a dual modeling technique using both statistical and computational modeling to evaluate impact. Our statistical model used the Spearman's Rho test to evaluate the strength of relationship between our proximal outcome measures (CDS utilization) against our distal outcome measure (provider self-reported cancer screening improvement). Our computational model relied on network evolution theory and made use of a tool called Construct-TM to model the use of CDS measured by the rate of organizational learning. We employed the use of previously collected survey data from community health centers Cancer Health Disparities Collaborative (HDCC). Our intent is to demonstrate the added valued gained by using a computational modeling tool in conjunction with a statistical analysis when evaluating the impact a health information technology, in the form of CDS, on health care quality process outcomes such as facility-level screening improvement. Significant simulated disparities in organizational learning over time were observed between community health centers beginning the simulation with high and low clinical decision support capability.

KEYWORDS:

Cancer screening; Community health center; Computational; Modeling; Simulation; Systems-thinking

PMID:
24953241
PMCID:
PMC4194243
DOI:
10.1016/j.jbi.2014.05.012
[Indexed for MEDLINE]
Free PMC Article
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