Biomedical subjects

Kathleen M Carley

Publications and source records attributed to Kathleen M Carley.

4 recordsLinked to original sources

PubMed2006

Description of a method to support public health information management: organizational network analysis.

In this case study, we describe a method that has potential to provide systematic support for public health information management. Public health agencies depend on specialized information that travels throughout an organization via communication networks among employees. Interactions that occur within these networks are poorly understood and are generally unmanaged. We applied organizational network analysis, a method for studying communication networks, to assess the method's utility to support decision making for public health managers, and to determine what links existed between information use and agency processes. Data on communication links among a health department's staff was obtained via survey with a 93% response rate, and analyzed using Organizational Risk Analyzer (ORA) software. The findings described the structure of information flow in the department's communication networks. The analysis succeeded in providing insights into organizational processes which informed public health managers' strategies to address problems and to take advantage of network strengths.

Database Management Systems↗

PubMed2004

The impact of countermeasure propagation on the prevalence of computer viruses.

Countermeasures such as software patches or warnings can be effective in helping organizations avert virus infection problems. However, current strategies for disseminating such countermeasures have limited their effectiveness. We propose a new approach, called the Countermeasure Competing (CMC) strategy, and use computer simulation to formally compare its relative effectiveness with three antivirus strategies currently under consideration. CMC is based on the idea that computer viruses and countermeasures spread through two separate but interlinked complex networks-the virus-spreading network and the countermeasure-propagation network, in which a countermeasure acts as a competing species against the computer virus. Our results show that CMC is more effective than other strategies based on the empirical virus data. The proposed CMC reduces the size of virus infection significantly when the countermeasure-propagation network has properties that favor countermeasures over viruses, or when the countermeasure-propagation rate is higher than the virus-spreading rate. In addition, our work reveals that CMC can be flexibly adapted to different uncertainties in the real world, enabling it to be tuned to a greater variety of situations than other strategies.

Computer Communication Networks↗

PubMed2002

Computational organization science: a new frontier.

Synthetic adaptation is the process whereby any entity composed of intelligent, adaptive, and computational agents is also an intelligent, adaptive, and computational agent. Because of synthetic adaptation, organizations, like the agents of which they are composed, are inherently computational. We can gain insight into the behavior of groups, organizations, and societies by using multiagent computational models composed of collections of intelligent adaptive artificial agents. CONSTRUCT-O and ORGAHEAD are examples of such models whose value for social, organizational, and policy analysis lies in the fact that they combine a network (social and knowledge approach) with a multiagent approach to effect more realistic behavior. The results from a series of virtual experiments using these models are examined to illustrate the power of this approach for social, organizational, and policy analysis.

Journal Article↗

PubMedDate not supplied

Using computational modeling to transform nursing data into actionable information.

Transforming organizational research data into actionable information nurses can use to improve patient outcomes remains a challenge. Available data are numerous, at multiple levels of analysis, and snapshots in time, which makes application difficult in a dynamically changing healthcare system. One potential solution is computational modeling. We describe our use of OrgAhead, a theoretically based computational modeling program developed at Carnegie Mellon University, to transform data into actionable nursing information. We calibrated the model by using data from 16 actual patient care units to adjust model parameters until performance of simulated units ordered in the same way as observed performance of the actual units 80% of the time. In future research, we will use OrgAhead to generate hypotheses about changes nurses might make to improve patient outcomes, help nurses use these hypotheses to identify and implement changes on their units, and then measure the impact of those changes on patient outcomes.

Computational Biology↗