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

AbstractOur 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

Hypothesis Generation Using Network Structures on Community Health Center Cancer-Screening Performance

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

A modelling and simulation framework for health care systems.

International audienceIn this paper, we propose a new modeling methodology named MedPRO for addressing organization problems of health care systems. It is based on a metamodel with three different views: process view (care pathways of patients), resource view (activities of relevant resources), and organization view (dependence and organization of resources). The resulting metamodel can be instantiated for a specific health care system and be converted into an executable model for simulation by means of a special class of Petri nets (PNs), called Health Care Petri Nets (HCPNs). HCPN models also serve as a basis for short-term planning and scheduling of health care activities. As a result, the MedPRO methodology leads to a fast-prototyping tool for easy and rigorous modeling and simulation of health care systems. A case study is presented to show the benefits of the MedPRO methodology

An Organizational Informatics Analysis of Colorectal, Breast, and Cervical Cancer Screening Clinical Decision Support and Information Systems within Community Health Centers

Indiana University-Purdue University Indianapolis (IUPUI)A study design has been developed that employs a dual modeling approach to identify factors associated with facility-level cancer screening improvement and how this is mediated by the use of clinical decision support. This dual modeling approach combines principles of (1) Health Informatics, (2) Cancer Prevention and Control, (3) Health Services Research, and (4) Organizational Change/Theory. The study design builds upon the constructs of a conceptual framework developed by Jane Zapka, namely, (1) organizational and/or practice settings, (2) provider characteristics, and (3) patient population characteristics. These constructs have been operationalized as measures in a 2005 HRSA/NCI Health Disparities Cancer Collaborative inventory of 44 community health centers. The first, statistical models will use: sequential, multivariable regression models to test for the organizational determinants that may account for the presence and intensity-of-use of clinical decision support (CDS) and information systems (IS) within community health centers for use in colorectal, breast, and cervical cancer screening. A subsequent test will assess the impact of CDS/IS on provider reported cancer screening improvement rates. The second, computational models will use a multi-agent model of network evolution called CONSTRUCT® to identify the agents, tasks, knowledge, groups, and beliefs associated with cancer screening practices and CDS/IS use to inform both CDS/IS implementation and cancer screening intervention strategies. This virtual experiment will facilitate hypothesis-generation through computer simulation exercises. The outcome of this research will be to identify barriers and facilitators to improving community health center facility-level cancer screening performance using CDS/IS as an agent of change. Stakeholders for this work include both national and local community health center IT leadership, as well as clinical managers deploying IT strategies to improve cancer screening among vulnerable patient populations

Rapid Mission Assurance Assessment via Sociotechnical Modeling and Simulation

How do organizations rapidly assess command-level effects of cyber attacks? Leaders need a way of assuring themselves that their organization, people, and information technology can continue their missions in a contested cyber environment. To do this, leaders should: 1) require assessments be more than analogical, anecdotal or simplistic snapshots in time; 2) demand the ability to rapidly model their organizations; 3) identify their organization’s structural vulnerabilities; and 4) have the ability to forecast mission assurance scenarios. Using text mining to build agent based dynamic network models of information processing organizations, I examine impacts of contested cyber environments on three common focus areas of information assurance—confidentiality, integrity, and availability. I find that assessing impacts of cyber attacks is a nuanced affair dependent on the nature of the attack, the nature of the organization and its missions, and the nature of the measurements. For well-manned information processing organizations, many attacks are in the nuisance range and that only multipronged or severe attacks cause meaningful failure. I also find that such organizations can design for resiliency and provide guidelines in how to do so

Evaluating Network Analysis and Agent Based Modeling for Investigating the Stability of Commercial Air Carrier Schedules

For a number of years, the United States Federal Government has been formulating the Next Generation Air Transportation System plans for National Airspace System improvement. These improvements attempt to address air transportation holistically, but often address individual improvements in one arena such as ground or in-flight equipment. In fact, air transportation system designers have had only limited success using traditional Operations Research and parametric modeling approaches in their analyses of innovative operations. They need a systemic methodology for modeling of safety-critical infrastructure that is comprehensive, objective, and sufficiently concrete, yet simple enough to be deployed with reasonable investment. The methodology must also be amenable to quantitative analysis so issues of system safety and stability can be rigorously addressed

On Computer-Aided Methods for Modeling and Analysis of Organizations

Treur, J. [Promotor

Approche intégrée pour l'analyse de risques et l'évaluation des performances : application aux services de stérilisation hospitalière

Sterilization services are vulnerable to risks, due to the contagious nature of their environment and to the degradation that risks can cause to their performances and to the safety of patients and staff. The risks in these facilities can range from equipment failure to the transmission of nosocomial infections and diseases. In this kind of high risk environment, these services are also required to maintain an adequate level of performance to ensure continuity of care in operating theaters.We focus in this research on the development of an integrated approach for risk analysis and performance assessment. This work is part of a collaborative work between the G-SCOP laboratory and the sterilization service of the University Hospital of Grenoble, which was the case study chosen to implement the proposed approach.The approach we propose is conducted in several steps: first, following a comparison of the risk analysis methods, we have chosen a model driven approach called FIS (Function Interaction Structure). Based on FIS, we have developed a risk model of Grenoble University Hospital sterilization service. This model describes the functions, the resources to achieve these functions as well as the various risks that may be encountered. Secondly, we introduced a new view to the FIS model dedicated to describe the dynamic behaviour of the resulting risk model.This dynamic model can simulate the behaviour of the sterilization service in normal situations of operations and risk situations.To do this, we have introduced a new Petri Net class called PTPS (Predicate-Transition, Prioritized, Synchronous) Petri Net to represent and simulate the dynamic behaviour of the FIS model. Subsequently, we automated the switching between the risk model and the dynamic model. This automation is performed by a set of translation algorithms capable of automatically converting the FIS model to a PTPS Petri Net simulation model .This approach resulted in a modelling and simulation tool in degraded mode called SIM-RISK. We also showed the usefulness of this tool by some examples based on different risks encountered in the sterilization service.Les services de stérilisation sont des lieux de production de soins caractérisés par une multitude d’activités et situations auxquelles ils sont confrontés. En outre, les services de stérilisation doivent assurer leurs missions dans un environnement caractérisé par la présence d’une variété de risques. Les risques présents dans ces milieux peuvent aller des pannes des équipements jusqu’aux contaminations et transmission des maladies nosocomiales. Ces services sont aussi tenus de garder un niveau de performances satisfaisant pour assurer la continuité des soins dans les blocs opératoires.Pour aider ces services dans leur quête d'un système performant, capable d’évoluer dans un environnement à haut niveau de risques, nous nous intéressons dans ce travail de recherche au développement d’une approche intégrée pour l’analyse de risques et l’évaluation des performances. Ce travail s’intègre dans un cadre collaboratif entre le laboratoire G-SCOP et le service de stérilisation du CHU de Grenoble, terrain d’étude choisi pour mettre en œuvre l’approche proposée.L'approche que nous proposons se déroule en plusieurs étapes: tout d’abord, suite à une comparaison entre les méthodes de gestion des risques, nous nous sommes orientés vers l’approche pilotée par modèle, dénommée FIS (Fonction Interaction Structure). En nous basant sur FIS, nous avons développé un modèle de risque dans ce service de stérilisation, décrivant à la fois les fonctions, les ressources permettant la réalisation de ces fonctions ainsi que les différents risques qui peuvent être rencontrés. Dans un deuxième temps, nous avons représenté le comportement dynamique du modèle de risques obtenu. Ce modèle dynamique permet de simuler le comportement du service de stérilisation et le voir évoluer dans les situations normales de fonctionnement et les situations de risques. Pour ce faire, nous avons introduit une nouvelle classe de réseau de Petri appelée réseau de Petri PTPS (Predicate-Transition, Prioritized, Synchronous) permettant de représenter et simuler le comportement dynamique du modèle FIS. Par la suite, nous avons automatisé le passage entre le modèle de risque et le modèle dynamique. Cette automatisation est effectuée par un ensemble d’algorithmes de traduction, capables de convertir automatiquement le modèle FIS et le modèle de simulation en réseau de Petri PTPS.Cette approche a donné lieu à un outil de modélisation et de simulation en mode dégradé, appelé SIM-RISK. Nous avons également montré l’utilité de cet outil sur des exemples inspirés des différents risques rencontrés dans le service de stérilisation