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Browsing by Author "Weaver, Michael"
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Item Comparing Engagement in Advance Care Planning Between Stages of Heart Failure(2019-08) Catalano, Lori A.; Hickman, Susan; Von Ah, Diane; Torke, Alexia; Weaver, MichaelHeart failure is a terminal disease with an unpredictable trajectory. Family members of patients with heart failure are often called upon to make decisions about treatment and end of life care, sometimes with little guidance as to the patients’ wishes. Advance care planning (ACP) is an ongoing process by which patients make decisions about their future healthcare. Only about one-third of patients with heart failure have participated in ACP, which is a similar percentage to the overall population. Despite increased focus on ACP and interventions to improve it, the rates of ACP in the population remain relatively unchanged. There is a need to develop interventions that are targeted based on patient engagement in the process rather than the existing broad-based interventions. The purpose of this dissertation study is to examine the relationship between the American Heart Association stage of heart failure and readiness to engage in advance care planning. The study consisted of mailed surveys that consisted of demographic questionnaires and the Advance Care Planning Engagement Survey. Engagement was analyzed in relation to heart failure stage, heart failure class, comorbidities, perception of health status, recent hospitalizations, making healthcare decisions for others, and demographic variables. The results demonstrated that although there was no significant association between heart failure stage or class and engagement in advance care planning, there were significant associations between medical comorbidities and advance care planning engagement. Other significantly associated participant characteristics included age, gender, education, ethnicity, and income. Findings suggest that people with multiple comorbid conditions will be more likely to be ready to engage in ACP than those with fewer health conditions. The results from this study will contribute to the development of strategies to improve advance care planning that are targeted based on engagement level.Item Hypothesis Generation Using Network Structures on Community Health Center Cancer-Screening Performance(Elsevier, 2015-10) Carney, Timothy Jay; Morgan, Geoffrey P.; Jones, Josette; McDaniel, Anna M.; Weaver, Michael; Weiner, Bryan; Haggstrom, David A.; BioHealth Informatics, School of Informatics and ComputingRESEARCH 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.Item An Organizational Informatics Analysis of Colorectal, Breast, and Cervical Cancer Screening Clinical Decision Support and Information Systems within Community Health Centers(2013-03-06) Carney, Timothy Jay; Jones, Josette F.; Haggstrom, David A.; McDaniel, Anna M.; Weaver, Michael; Palakal, Mathew J.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.Item Psychometric Testing of the Life Changes in Epilepsy Scale(Springer, 2017) Miller, Wendy Renee; Weaver, Michael; Bakoyannis, Giorgos; Bakas, Tamilyn; Buelow, Janice; Sabau, Dragos; School of NursingPurpose: Three aims were addressed: (a) Evaluate properties of the items comprising the Life Changes in Epilepsy Scale-Pilot (LCES-P), (b) use item analysis to optimize the scale, (c) evaluate construct and criterion-related validity of the optimized LCES. Methods: The LCES-P was administered to 174 adults with epilepsy. Item analysis and exploratory factor analysis were performed. Internal consistency reliability, construct validity, and criterion-related validity were evaluated. Results: 17 items were retained in the optimized LCES. Internal consistency reliability was supported. Path analysis was used to evaluate construct validity. Criterion-related validity was supported by correlations with the Medical Outcomes SF-36 Survey (SF-36) General Health subscale and a criterion variable. Conclusions: The optimized version of the LCES can serve as a valuable outcome measure in clinical and research environments.Item Systematic review of sleep disorders in cancer patients: can the prevalence of sleep disorders be ascertained?(Wiley, 2015-02) Otte, Julie L.; Carpenter, Janet S.; Manchanda, Shalini; Rand, Kevin L.; Skaar, Todd C.; Weaver, Michael; Chernyak, Yelena; Zhong, Xin; Igega, Christele; Landis, CarolAlthough sleep is vital to all human functioning and poor sleep is a known problem in cancer, it is unclear whether the overall prevalence of the various types of sleep disorders in cancer is known. The purpose of this systematic literature review was to evaluate if the prevalence of sleep disorders could be ascertained from the current body of literature regarding sleep in cancer. This was a critical and systematic review of peer-reviewed, English-language, original articles published from 1980 through 15 October 2013, identified using electronic search engines, a set of key words, and prespecified inclusion and exclusion criteria. Information from 254 full-text, English-language articles was abstracted onto a paper checklist by one reviewer, with a second reviewer randomly verifying 50% (k = 99%). All abstracted data were entered into an electronic database, verified for accuracy, and analyzed using descriptive statistics and frequencies in SPSS (v.20) (North Castle, NY). Studies of sleep and cancer focus on specific types of symptoms of poor sleep, and there are no published prevalence studies that focus on underlying sleep disorders. Challenging the current paradigm of the way sleep is studied in cancer could produce better clinical screening tools for use in oncology clinics leading to better triaging of patients with sleep complaints to sleep specialists, and overall improvement in sleep quality.Item Using computational modeling to assess the impact of clinical decision support on cancer screening improvement strategies within the community health centers(Elsevier, 2014-10) Carney, Timothy Jay; Morgan, Geoffrey P.; Jones, Josette; McDaniel, Anna M.; Weaver, Michael; Weiner, Bryan; Haggstrom, David A.; IU School of NursingOur 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.