Using life cycle costing for product management

Introducing a case study on product management, this paper applies the Life Cycle Cost (LCC) method to solve a particular problem of design selection in the area of mechanical engineering. It is clearly explained and illustrated that various cost types need to be taken into account, ranging over the whole life of the product from concept to end-of-life, and related to an appropriate unit of utility. In order to achieve maximum effect, such a comprehensive economic analysis should ideally be undertaken at a very early stage of the product’s life cycle, such as its design or even conception. Advanced techniques, including sensitivity analyses and simulations, will typically be required to gain an adequate insight into various processes and uncertainties contained in any realistic life cycle model

THE COST OF SHARING: THE EFFECT OF SHARING INCLINATION ON INFORMATION OVERLOAD

Current research on social media emphasizes that sharing information comes with great benefits to the individual who shares. In this paper, we adopt a different perspective by arguing that individuals with a high inclination to share information through social media also incur substantial cognitive costs. In particular, we hypothesize that during two phases of the sharing process, information appraisal and asynchronous interactivity, the sharer is confronted with information processing requirements that con-siderably draw on his or her limited cognitive capacity and thus increase the likelihood of experiencing information overload. We furthermore argue that this effect is more pronounced for individuals with a high compared to low need for cognition because they feel particularly motivated to process infor-mation. Our hypotheses are supported by a large survey-generated dataset (n=30,392) from six coun-tries. We additionally find a positive direct effect of need for cognition on information overload. We discuss contributions to conversations on information sharing, information overload, and need for cog-nition research in the context of social media and we highlight managerial implications of our findings

Risk Factors for Seizures Among Young Children Monitored With Continuous Electroencephalography in Intensive Care Unit: A Retrospective Study

Objective: cEEG is an emerging technology for which there are no clear guidelines for patient selection or length of monitoring. The purpose of this study was to identify subgroups of pediatric patients with high incidence of seizures.Study Design: We conducted a retrospective study on 517 children monitored by cEEG in the intensive care unit (ICU) of a children's hospital. The children were stratified using an age threshold selection method. Using regression modeling, we analyzed significant risk factors for increased seizure risk in younger and older children. Using two alternative correction procedures, we also considered a relevant comparison group to mitigate selection bias and to provide a perspective for our findings.Results: We discovered an approximate risk threshold of 14 months: below this threshold, the seizure risk increases dramatically. The older children had an overall seizure rate of 18%, and previous seizures were the only significant risk factor. In contrast, the younger children had an overall seizure rate of 45%, and the seizures were significantly associated with hypoxic-ischemic encephalopathy (HIE; p = 0.007), intracranial hemorrhage (ICH; p = 0.005), and central nervous system (CNS) infection (p = 0.02). Children with HIE, ICH, or CNS infection accounted for 61% of all seizure patients diagnosed through cEEG under 14 months.Conclusions: An extremely high incidence of seizures prevails among critically ill children under 14 months, particularly those with HIE, ICH, or CNS infection

Creative destruction in science

Drawing on the concept of a gale of creative destruction in a capitalistic economy, we argue that initiatives to assess the robustness of findings in the organizational literature should aim to simultaneously test competing ideas operating in the same theoretical space. In other words, replication efforts should seek not just to support or question the original findings, but also to replace them with revised, stronger theories with greater explanatory power. Achieving this will typically require adding new measures, conditions, and subject populations to research designs, in order to carry out conceptual tests of multiple theories in addition to directly replicating the original findings. To illustrate the value of the creative destruction approach for theory pruning in organizational scholarship, we describe recent replication initiatives re-examining culture and work morality, working parents\u2019 reasoning about day care options, and gender discrimination in hiring decisions. Significance statement It is becoming increasingly clear that many, if not most, published research findings across scientific fields are not readily replicable when the same method is repeated. Although extremely valuable, failed replications risk leaving a theoretical void\u2014 reducing confidence the original theoretical prediction is true, but not replacing it with positive evidence in favor of an alternative theory. We introduce the creative destruction approach to replication, which combines theory pruning methods from the field of management with emerging best practices from the open science movement, with the aim of making replications as generative as possible. In effect, we advocate for a Replication 2.0 movement in which the goal shifts from checking on the reliability of past findings to actively engaging in competitive theory testing and theory building. Scientific transparency statement The materials, code, and data for this article are posted publicly on the Open Science Framework, with links provided in the article

Crowdsourcing hypothesis tests: Making transparent how design choices shape research results

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer fiveoriginal research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams renderedstatistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.</div

Examining the generalizability of research findings from archival data

This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability—for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples

Essays in Healthcare Operations and Management

The overarching objective of the research described in this dissertation is to analyze some pressing healthcare problems in various areas, ranging from health IT to payment models and healthcare operations, using mathematical and economic models. Analytical models have been at the forefront of the recent transformational efforts in healthcare (see e.g., Reid et al. (2005), Obermeyer and Emanuel (2016), Romeijn and Zenios (2008), Bates et al. (2014)). The dissertation comprises three chapters corresponding to three research projects: 1) econometric evaluation of Health Information Exchanges, 2) game-theoretical modeling of the Bundled Payments financing model, and 3) queueing algorithm design for Bed Management with overflows in hospitals.Ph.D

Association of an Electroencephalography-Based Risk Score With Seizure Probability in Hospitalized Patients.

IMPORTANCE: Continuous electroencephalography (EEG) use in critically ill patients is expanding. There is no validated method to combine risk factors and guide clinicians in assessing seizure risk. OBJECTIVE: To use seizure risk factors from EEG and clinical history to create a simple scoring system associated with the probability of seizures in patients with acute illness. DESIGN, SETTING, AND PARTICIPANTS: We used a prospective multicenter (Emory University Hospital, Brigham and Womens Hospital, and Yale University Hospital) database containing clinical and electrographic variables on 5427 continuous EEG sessions from eligible patients if they had continuous EEG for clinical indications, excluding epilepsy monitoring unit admissions. We created a scoring system model to estimate seizure risk in acutely ill patients undergoing continuous EEG. The model was built using a new machine learning method (RiskSLIM) that is designed to produce accurate, risk-calibrated scoring systems with a limited number of variables and small integer weights. We validated the accuracy and risk calibration of our model using cross-validation and compared its performance with models built with state-of-the-art logistic regression methods. The database was developed by the Critical Care EEG Research Consortium and used data collected over 3 years. The EEG variables were interpreted using standardized terminology by certified reviewers. EXPOSURES: All patients had more than 6 hours of uninterrupted EEG recordings. MAIN OUTCOMES AND MEASURES: The main outcome was the average risk calibration error. RESULTS: There were 5427 continuous EEGs performed on 4772 participants (2868 men, 49.9%; median age, 61 years) performed at 3 institutions, without further demographic stratification. Our final model, 2HELPS2B, had an area under the curve of 0.819 and average calibration error of 2.7% (95% CI, 2.0%-3.6%). It included 6 variables with the following point assignments: (1) brief (ictal) rhythmic discharges (B[I]RDs) (2 points); (2) presence of lateralized periodic discharges, lateralized rhythmic delta activity, or bilateral independent periodic discharges (1 point); (3) prior seizure (1 point); (4) sporadic epileptiform discharges (1 point); (5) frequency greater than 2.0 Hz for any periodic or rhythmic pattern (1 point); and (6) presence of plus features (superimposed, rhythmic, sharp, or fast activity) (1 point). The probable seizure risk of each score was 5% for a score of 0, 12% for a score of 1, 27% for a score of 2, 50% for a score of 3, 73% for a score of 4, 88% for a score of 5, and greater than 95% for a score of 6 or 7. CONCLUSIONS AND RELEVANCE: The 2HELPS2B model is a quick accurate tool to aid clinical judgment of the risk of seizures in critically ill patients