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

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

doi:10.1016/j.jbi.2014.05.012

Cited by 13 publications

(13 citation statements)

References 39 publications

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“…A secondary analysis was performed to determine the extent of impact of the clinical decision support (CDS) as an innovative best practice (with the use of clinical reminders, use of provider prompts at point-of-care, and generation of automated patient results for providers) on facility-level cancer-screening improvement scores. 27 Although our previous results indicate a positive correlation between HDCC participation and CDS use, traditional statistical analysis could not find any correlation between the use of CDS and its impact on cancer-screening improvement in CHCs. We were also unable to observe continued rates of improvement in cancer screening or sustained uptake of innovative technologies.…”

Section: Methods: Modeling Community Health Center Behavior With Ccontrasting

confidence: 59%

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

Carney

Morgan

Jones

et al. 2015

Journal of Biomedical Informatics

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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 some 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 diffuse to others as repeatable best practices. One reason is that facility-level changes typically occur in dynamic organizational environments that are complex, adaptive, and unpredictable. This study seeks to better understand the factors that help shape community health center facility-level cancer screening performance over time. This study applies a computational modeling approach that combines principles of health services research, health informatics, network theory, and systems science. Methods In order to investigate the role of knowledge acquisition, retention, and sharing within the setting of the community health center and the effect of this role 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 previous collected point-in-time survey data. Construct TM is a multi-agent model of network evolution. Social, knowledge, and belief networks co-evolve. Groups and organizations are treated as complex systems, thus capturing the variability in human and organizational factors. In Construct TM, individuals and groups interact communicate, learn, and make decisions in a continuous cycle. Data from the survey was used to create high-performing simulated community health centers and low-performing ones based on extent of both computer decision support use and cancer-screening rates. Results Our 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 unconnected agents 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. Our study showed 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 and thereby enhance sustainability of long-term...

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Section: Methods: Modeling Community Health Center Behavior With Ccontrasting

confidence: 59%

“…We were also unable to observe continued rates of improvement in cancer screening or sustained uptake of innovative technologies. 27 …”

Section: Methods: Modeling Community Health Center Behavior With Cmentioning

confidence: 99%

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

Carney

Morgan

Jones

et al. 2015

Journal of Biomedical Informatics

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“…In previous studies, we examined the knowledge absorption rate of community health clinical staff with regard to breast, cervical, and colorectal cancer screening policies, guidelines, and protocols as derived from the use of electronic clinical decision-support (CDS) [31]. We examined the extent to which CDS use and corresponding knowledge absorption rates would be correlated to organizational performance for cancer screening [31]. We demonstrated that KAR was, in fact, a predictor of organizational performance in meeting process-of-care outcomes in cancer care [31].…”

Section: Smart Systems Vulnerability Indexmentioning

confidence: 99%

“…We examined the extent to which CDS use and corresponding knowledge absorption rates would be correlated to organizational performance for cancer screening [31]. We demonstrated that KAR was, in fact, a predictor of organizational performance in meeting process-of-care outcomes in cancer care [31]. Hence, we suggest that KAR can serve as an effective measure of HIT impact on performance by focusing on end-users' ability to access key knowledge by interacting with HIT tools and applying this knowledge to healthcare delivery and public health practice.…”

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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“…Colorectal cancer screening performance rates are based on evidence-based guidelines. CDSS has been particularly effective in acquisition greater levels of health care evidence base practice (Carney et al, 2014). A big obstacle for CDSS is the difficulty in obtaining the patient and relatives information required for decision making (Wagholikar et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Information Engineering and Workflow Design in a Clinical Decision Support System for Colorectal Cancer Screening in Iran

Maserat

Farajollah²,

Safdari³

et al. 2015

Asian Pacific Journal of Cancer Prevention

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Background: Colorectal cancer is a major cause of morbidity and mortality throughout the world. Colorectal cancer screening is an optimal way for reducing of morbidity and mortality and a clinical decision support system (CDSS) plays an important role in predicting success of screening processes. DSS is a computer-based information system that improves the delivery of preventive care services. The aim of this article was to detail engineering of information requirements and work flow design of CDSS for a colorectal cancer screening program. Materials and Methods: In the first stage a screening minimum data set was determined. Developed and developing countries were analyzed for identifying this data set. Then information deficiencies and gaps were determined by check list. The second stage was a qualitative survey with a semi-structured interview as the study tool. A total of 15 users and stakeholders' perspectives about workflow of CDSS were studied. Finally workflow of DSS of control program was designed by standard clinical practice guidelines and perspectives. Results: Screening minimum data set of national colorectal cancer screening program was defined in five sections, including colonoscopy data set, surgery, pathology, genetics and pedigree data set. Deficiencies and information gaps were analyzed. Then we designed a work process standard of screening. Finally workflow of DSS and entry stage were determined. Conclusions: A CDSS facilitates complex decision making for screening and has key roles in designing optimal interactions between colonoscopy, pathology and laboratory departments. Also workflow analysis is useful to identify data reconciliation strategies to address documentation gaps. Following recommendations of CDSS should improve quality of colorectal cancer screening.

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

Cited by 13 publications

References 39 publications

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

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

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

Information Engineering and Workflow Design in a Clinical Decision Support System for Colorectal Cancer Screening in Iran

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