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Department: search.filters.department.Department of Business AdministrationSubject: search.filters.subject.Medicine

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Now showing 1 - 3 of 3
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    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; 4534
    This 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.
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    PublicationOpen Access
    Health system modelling research: towards a whole-health-system perspective for identifying good value for money investments in health system strengthening
    (BMJ Publishing Group, 2019) Verguet, Stephane; Feldhaus, Isabelle; Kwete, Xiaoxiao Jiang; Aqil, Anwer; Atun, Rifat; Bishai, David; Cecchini, Michele; Guerra Junior, Augusto Afonso; Habtemariam, Mahlet Kifle; Jbaily, Abdulrahman; Kruk, Margaret E.; Haneuse, Sebastien; Norheim, Ole Frithjof; Smith, Peter C.; Tolla, Mieraf Taddesse; Zewdu, Solomon; Bump, Jesse; Department of Business Administration; Karanfil, Özge; Department of Business Administration; College of Administrative Sciences and Economics
    Global health research has typically focused on single diseases, and most economic evaluation research to date has analysed technical health interventions to identify 'best buys'. New approaches in the conduct of economic evaluations are needed to help policymakers in choosing what may be good value (ie, greater health, distribution of health, or financial risk protection) for money (ie, per budget expenditure) investments for health system strengthening (HSS) that tend to be programmatic. We posit that these economic evaluations of HSS interventions will require developing new analytic models of health systems which recognise the dynamic connections between the different components of the health system, characterise the type and interlinks of the system's delivery platforms; and acknowledge the multiple constraints both within and outside the health sector which limit the system's capacity to efficiently attain its objectives. We describe priority health system modelling research areas to conduct economic evaluation of HSS interventions and ultimately identify good value for money investments in HSS.
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    Optimal population screening policies for Alzheimer’s disease*
    (Taylor and Francis Inc., 2019) Gürvit, İbrahim Hakan; Department of Business Administration; N/A; Sayın, Serpil; Önen, Zehra; Faculty Member; PhD Student; Department of Business Administration; College of Administrative Sciences and Economics; Graduate School of Sciences and Engineering; 6755; N/A
    Alzheimer’s disease (AD) constitutes a serious societal healthcare issue as the proportion of the aging population increases. There are ongoing discussions about the necessity of screening the population for AD. We investigate optimal population screening policies for AD using Markov Decision Processes (MDPs). The objective function combines quality-adjusted life years and costs. The disease states are identified according to Clinical Dementia Rating (CDR) scores. The screening test in the model is the Mini Mental State Examination (MMSE), a cognitive test that is widely used in clinical practice. A numerical implementation of the MDP model is presented based on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and existing literature. In the baseline case, the optimal outcome is not to employ a population-wide screening program. We conduct extensive sensitivity analyses on several model parameters. Our study reveals that the optimal policy may be sensitive to changes in transition probability estimates. When we focus on transitions that are related to treatment effectiveness, we find that implementing a population screening policy becomes socially optimal when plans that lead to cognitive ability stabilization or improvement become available.