Specifying Agents in Construct

Hirshman, Brian R.; Carley, Kathleen M.; Kowalchuck, Michael

doi:10.21236/ada500804

Cited by 20 publications

(4 citation statements)

References 4 publications

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“…According to Carley, Construct-TM can employ any or all of three models—(1) the standard interaction model, (2) the standard influence model, and (3) the standard belief model [37, 38]. This study relied heavily on the standard interaction model because summary measures such as senior leadership, clinical leadership, and team activities are assumed responsible for the CHC values, beliefs, and attitudes that modify agent behavior and contribute to the within-CHC exchange of knowledge on health center cancer screening performance, strategies, and priority areas.…”

Section: Methodsmentioning

confidence: 99%

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

Carney¹,

Morgan²,

Jones³

et al. 2014

Journal of Biomedical Informatics

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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.

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Section: Methodsmentioning

confidence: 99%

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

Carney¹,

Morgan²,

Jones³

et al. 2014

Journal of Biomedical Informatics

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“…According to this concept, knowledge represented in its various forms can be deconstructed into quantifiable units [29, 30]. The number of knowledge units or bits that may comprise a discreet package of knowledge is determined by the level of complexity of the decisions or tasks this knowledge is designed to inform [29, 30]. As such, a direct relationship exists between the number of knowledge bits and the level of the complexity in related decisions and tasks.…”

Section: Smart Systems Vulnerability Indexmentioning

confidence: 99%

“…As such, a direct relationship exists between the number of knowledge bits and the level of the complexity in related decisions and tasks. The greater the level of task or decision complexity/criticality or decision, the larger the knowledge complement (or number of knowledge bits) associated with the management, storage, display/representation, diffusion, use, and comprehension of knowledge [29, 30]. This perspective assumes more knowledge bits are needed to saturate or carry out a complex task or make a critical choice than to implement a more simplified/less complex task or choice.…”

Section: Smart Systems Vulnerability Indexmentioning

confidence: 99%

Informatics Metrics and Measures for a Smart Public Health Systems Approach: Information Science Perspective

Carney

Shea

2017

Computational and Mathematical Methods in Medicine

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Public health informatics is an evolving domain in which practices constantly change to meet the demands of a highly complex public health and healthcare delivery system. Given the emergence of various concepts, such as learning health systems, smart health systems, and adaptive complex health systems, health informatics professionals would benefit from a common set of measures and capabilities to inform our modeling, measuring, and managing of health system “smartness.” Here, we introduce the concepts of organizational complexity, problem/issue complexity, and situational awareness as three codependent drivers of smart public health systems characteristics. We also propose seven smart public health systems measures and capabilities that are important in a public health informatics professional's toolkit.

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“…[28][29][30][31] An agent is more likely to interact with other agents who are in close proximity during a given time tick. Also, an agent tends to interact with agents who are in his/her social network.…”

Section: Agent Interactionsmentioning

confidence: 99%

Virtual epidemic in a virtual city: simulating the spread of influenza in a US metropolitan area

Lee

Bedford

Roberts

et al. 2008

Translational Research

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A wide variety of biological, physiological, social, economic and geographic factors may affect the transmission, spread, and impact of influenza. Recent concerns about an impending influenza epidemic have generated a need for predictive computer simulation models to forecast the spread of influenza and the effectiveness of prevention and control strategies. We designed an agent-based computer simulation of a theoretical influenza epidemic in Norfolk (Virginia) that included extensive city-level details and computer representations of every Norfolk citizen, including their expected behavior and social interactions. The simulation introduced 200 people infected cases on November 27, 2002 (Day 87) and tracked the progress of the epidemic. On average, the prevalence peaked on Day 178 (12.2% of the population). Our model showed a cyclical variation in influenza cases by day of the week with fewer people being exposed on weekends, differences in emergency room and clinic visits by day of the week, an earlier peak in influenza cases and persistent high prevalence among people age 65 or older, and the daily prevalence of infection among health care workers. The level of detail included in our simulation model made these findings possible. Compared to other existing models, our model has a very extensive and detailed social network, which may be important since individuals with more social interactions and extensive social networks may be more likely to spread influenza. Our simulation may serve as a virtual laboratory to better understand the way different factors and interventions affect the spread of influenza.

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Specifying Agents in Construct

Cited by 20 publications

References 4 publications

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

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

Informatics Metrics and Measures for a Smart Public Health Systems Approach: Information Science Perspective

Virtual epidemic in a virtual city: simulating the spread of influenza in a US metropolitan area

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