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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Modeling change in a health system: implications on patient flows and resource allocations(Canadian Soc Clinical Investigation, 2005) Yaman, Hande; Department of Business Administration; Güneş, Evrim Didem; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 51391This work is motivated by the recent changes in the health system in Turkey, which is a consolidation of health insurance funds, and its implications on the resource allocations and the flow of patients in the system. Our aim is to provide a model to find the best reallocation of resources between the hospitals and the best patient-hospital match to minimize the costs.Publication Metadata only A construal level account of the impact of religion and god on prosociality(Sage, 2020) N/A; N/A; Department of Business Administration; Canlı, Zeynep Gürhan; Karataş, Mustafa; PhD Student; Faculty Member; Department of Business Administration; Graduate School of Business; College of Administrative Sciences and Economics; N/A; 16135This research shows that the two most prevalent religious constructs-God and religion-differentially impact cognition. Activating thoughts about God (vs. religion) induces a relatively more abstract (vs. concrete) mindset (Studies 1a-1c). Consequently, time donation intentions (Study 2) and actual monetary donations (Study 3) after a God (vs. religion) prime increase when people are presented an abstractly (vs. concretely) framed donation appeal. Similarly, people donate more money to distant (vs. close) donation targets, which are construed relatively abstractly (vs. concretely), when a religious speech activates predominantly God-specific (vs. religion-specific) thoughts (Study 4). These effects are mediated by "feeling right" under construal level fit (Study 3). Overall, this research significantly advances extant knowledge on religious cognition and past research on the link between religion and prosociality.Publication Metadata only Targeting resources efficiently and justifiably by combining causal machine learning and theory(Frontiers Media Sa, 2022) Department of Business Administration; Ali, Özden Gür; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 57780Introduction: Efficient allocation of limited resources relies on accurate estimates of potential incremental benefits for each candidate. these heterogeneous treatment effects (HTE) can be estimated with properly specified theory-driven models and observational data that contain all confounders. Using causal machine learning to estimate HTE from big data offers higher benefits with limited resources by identifying additional heterogeneity dimensions and fitting arbitrary functional forms and interactions, but decisions based on black-box models are not justifiable. MethodsOur solution is designed to increase resource allocation efficiency, enhance the understanding of the treatment effects, and increase the acceptance of the resulting decisions with a rationale that is in line with existing theory. the case study identifies the right individuals to incentivize for increasing their physical activity to maximize the population's health benefits due to reduced diabetes and heart disease prevalence. We leverage large-scale data from multi-wave nationally representative health surveys and theory from the published global meta-analysis results. We train causal machine learning ensembles, extract the heterogeneity dimensions of the treatment effect, sign, and monotonicity of its moderators with explainable aI, and incorporate them into the theory-driven model with our generalized linear model with the qualitative constraint (GLM_QC) method. Resultsthe results show that the proposed methodology improves the expected health benefits for diabetes by 11% and for heart disease by 9% compared to the traditional approach of using the model specification from the literature and estimating the model with large-scale data. Qualitative constraints not only prevent counter-intuitive effects but also improve achieved benefits by regularizing the model.Publication Metadata only How removing prescription drugs from reimbursement lists increases the pharmaceutical expenditures for alternatives(Springer, 2011) Department of Business Administration; N/A; Ali, Özden Gür; Topaler, Başak; Faculty Member; Master Student; Department of Business Administration; College of Administrative Sciences and Economics; Graduate School of Business; 57780; N/AChanging the status of drugs from prescription-only to over-the-counter and removing them from reimbursement list has been used as a cost reduction measure by several third-party payers. in June 2006, the Turkish government, in an effort to curtail costs, removed many prescription drugs from the reimbursement list. This paper examines the effect of this policy on the expenditures for drugs that were removed from the reimbursement list and for their reimbursable alternatives that can be prescribed by physicians on patient request. To accomplish these goals, Actual expenditures in four anatomical therapeutic chemical (ATC) groups were compared with expected expenditures in the absence of policy change for both removed and alternative drugs. the findings indicated that the expenditures on alternative drugs beyond expectations. in two of the four ATC groups involved in the study, the increase was large enough to wipe out the reduction in expenditures on the drugs removed from the reimbursement list.Publication Metadata only Pharma rebates, pharmacy benefit managers and employer outcomes(Springer, 2010) Mantrala, Murali; Department of Business Administration; Ali, Özden Gür; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 57780Corporate employers contract with pharmacy benefit managers (PBMs) with the goals of lowering their employee prescription drug coverage costs while maintaining health care quality. However, little is known about how employer-PBM contract elements and brand drugmakers' rebates combine to influence a profit-maximizing PBM's actions, and the impact of those actions on the employer's outcomes. To shed more light on these issues, the authors build and analyze a mathematical simulation model of a competitive pharmaceutical market comprised of one generic and two branded drugs, and involving a PBM contracted by a corporate employer to help it lower prescription drug costs while achieving a minimum desired quality of health care for its employees. The brand drugmakers' rebate offers, the PBM's assignment of drugs to formulary tiers, and the resulting employer outcomes under varying contracts and pharma brand marketing mix environmental scenarios are analyzed to provide insights. The findings include that the pharma brands offer rebates for the PBM's ability to move prescription share away from the unpreferred brand, but reduce these offers when the PBM's contract requires it to proactively influence physicians to prescribe the generic drug alternative. Further, Pareto optimal contracts that provide the highest health benefit for a given employer cost budget for the employer are analyzed to provide managerial implications. They are found to involve strong PBM influence on physician prescribing to discourage unpreferred brands, as well as high patient copayment requirements for unpreferred brands to align the patient prescription fill probability with the formulary, while other copayment requirements provide an instrument to determine the level of desired health benefit-cost tradeoff.Publication Metadata only Breast cancer screening services: trade-offs in quality, capacity, outreach, and centralization(Springer Nature, 2004) Chick, Stephen E.; Güneş, Evrim D.; Department of Business Administration; Karaesmen, Zeynep Akşin; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 4534This work combines and extends previous work on breast cancer screening models by explicitly incorporating, for the first time, aspects of the dynamics of health care states, program outreach, and the screening volume-quality relationship in a service system model to examine the effect of public health policy and service capacity decisions on public health outcomes. We consider the impact of increasing standards for minimum reading volume to improve quality, expanding outreach with or without decentralization of service facilities, and the potential of queueing due to stochastic effects and limited capacity. The results indicate a strong relation between screening quality and the cost of screening and treatment, and emphasize the importance of accounting for service dynamics when assessing the performance of health care interventions. For breast cancer screening, increasing outreach without improving quality and maintaining capacity results in less benefit than predicted by standard models.Publication Metadata only Screening for preclinical alzheimer's disease: deriving optimal policies using a partially observable Markov model(Springer) Gürvit, İbrahim Hakan; Department of Business Administration; N/A; Sayın, Serpil; Dumlu, Zehra Önen; Faculty Member; PhD Student; Department of Business Administration; College of Administrative Sciences and Economics; Graduate School of Sciences and Engineering; 6755; N/AAlzheimer's Disease (AD) is believed to be the most common type of dementia. Even though screening for AD has been discussed widely, there is no screening program implemented as part of a policy in any country. Current medical research motivates focusing on the preclinical stages of the disease in a modeling initiative. We develop a partially observable Markov decision process model to determine optimal screening programs. The model contains disease free and preclinical AD partially observable states and the screening decision is taken while an individual is in one of those states. An observable diagnosed preclinical AD state is integrated along with observable mild cognitive impairment, AD and death states. Transition probabilities among states are estimated using data from Knight Alzheimer's Disease Research Center (KADRC) and relevant literature. With an objective of maximizing expected total quality-adjusted life years (QALYs), the output of the model is an optimal screening program that specifies at what points in time an individual over 50 years of age with a given risk of AD will be directed to undergo screening. The screening test used to diagnose preclinical AD has a positive disutility, is imperfect and its sensitivity and specificity are estimated using the KADRC data set. We study the impact of a potential intervention with a parameterized effectiveness and disutility on model outcomes for three different risk profiles (low, medium and high). When intervention effectiveness and disutility are at their best, the optimal screening policy is to screen every year between ages 50 and 95, with an overall QALY gain of 0.94, 1.9 and 2.9 for low, medium and high risk profiles, respectively. As intervention effectiveness diminishes and/or its disutility increases, the optimal policy changes to sporadic screening and then to never screening. Under several scenarios, some screening within the time horizon is optimal from a QALY perspective. Moreover, an in-depth analysis of costs reveals that implementing these policies are either cost-saving or cost-effective.Publication Metadata only Can advertising enhance consumers' desirable covid-19 health behavioral intentions? the role of brand-pandemic fit(Wiley, 2022) Newmeyer, Casey E.; Schmidt-Devlin, Ellen; Department of Business Administration; N/A; Tunalı, Ayşegül Özsomer; Güzel, Zeynep Müge; Faculty Member; PhD Student; Department of Business Administration; College of Administrative Sciences and Economics; Graduate School of Business; 108158; N/AThis article explores the fit between the advertised brand and the pandemic as a potential influence on consumers' intentions to engage in socially responsible health behaviors (social distancing, mask wearing, and getting tested when exposed). In an advanced and emerging market setting we find that advertisements for brands that are perceived as high on brand-pandemic fit enhance consumers' socially desirable COVID-19 health behavioral intentions and changes in brand credibility is the mechanism that drives such intentions. Fit is especially beneficial on the intentions of consumers whose health beliefs reflect only low to moderate concern about COVID-19. Consumers with low or moderate (vs. high) COVID-19 health beliefs exhibit an increased susceptibility to the fit-desirable health behavioral intentions relationship. The results are also corroborated in an emerging market context. Together, the results establish links between brand-pandemic fit of advertisements, brand credibility, health beliefs, and consumers' intentions to engage in socially desirable health behaviors. The results suggest that advertising can play a role in encouraging desirable health behaviors and can promote consumer welfare via ads of high fit products and services that provide benefits during the pandemic in both advanced and emerging markets.