Operational and Organizational Issues Facing Corporate Real Estate Executives and Managers

This article examines three major categories of issues facing corporate real estate executives in the future, as determined by a Delphi process survey conducted by the authors. We present areas of agreement and disagreement among the corporate executives surveyed, and distill the results of the Delphi survey and other major studies on the future of corporate real estate into a research agenda for further inquiry.

Language Deprivation in the Deaf Inpatient Population

non

Master of Science

thesisWater column, sediment and pore water samples were collected from multiple locations of the Great Salt Lake (GSL), Utah to examine the spatial and temporal distribution of total mercury (THg) and methyl mercury (MeHg) concentrations and MeHg production potentials (MPPs). Sampling locations included multiple transects in the south arm of the GSL, adjacent freshwater influenced bays, and multiple impounded and sheet flow freshwater wetland sites during the period of summer 2009-summer 2012. Select water column and sediment subsamples were spiked with inorganic mercury (I204Hg) and methyl mercury (Me204Hg) to examine net production of methyl mercury (Me204Hg ) and net loss of Me201Hg. First order methylation (kmeth) and demethylation (kdemeth) rate constants were determined from changes in isotope dilution corrected concentrations and/or changes in isotope ratios as a function of time. Tin reducible inorganic Hg (Hg(II)R) was used as a proxy for bioavailable Hg(II) in GSL samples. MeHg production potentials (MPPs) were calculated as the time integrated product of kmeth and Hg(II)R to compare methylation in deep brine layer (DBL) versus underlying sediment slurry (SSL) samples of the GSL. A large range of methylation rate constants (1.4E-6 to 1.1E-3 hr-1) was observed across the region, whereas demethylation was only significant in the DBL. Positive correlation of kmeth to organic matter content was observed in sediment of the freshwater influenced bays and impounded wetlands, while this trend was not observed in DBL and SSL of the GSL. These results further indicate that sediment organic matter, as well as other factors (e.g., organic matter lability and sulfide concentrations) contribute to production of MeHg. Spatially, higher MeHg concentrations in the GSL and sheet flow freshwater wetlands present the possibility of ‘hot spots' for MeHg introduction into the food web. Greater and temporally constant MPPs in SSL relative to DBL may explain the persistence of high MeHg concentrations in the DBL

How Healthcare Big Data Analytics Information Asymmetry Influences Organizational Design Absorptive Choices

Although the relationship between big data analytics (BDA) organizational, firm, and financial performance is well supported, little attention has been paid in extant research to exploring the organizational design issues resulting from information asymmetry caused by BDA; in particular, organizational absorptive choices that include acquisitions, mergers, reorganization, executive changes, or board of director adjustments. The purpose of this qualitative single case study within a U.S. hospital was to explore the conditions and circumstances that influence absorptive organizational design choices of hospital administration. The theoretical base of this study is Pfeffer and Salancik’s resource dependence theory (RDT). Logic model data analysis approach was conducted on primary data attained from semistructured interviews of 12 volunteers of hospital administration and secondary data from grey literature. The findings of this study suggest resource dependence theory activities of executive changes and intra-organizational structural changes moderate information asymmetry. Communication was the major theme, while properly formed BDA questions, prospective reimbursement models, evolving BDA demands, intellectual capacity gap, and operational complexity were minor themes that influenced organizational design decisions. The practical implications emphasize communication among multidisciplinary groups and boundary-spanning organizational design strategies to moderate information asymmetry. Lastly, the positive social change implication may be the increased BDA adoption in hospital administration from the improved communication among individual actors of multidisciplinary BDA groups

How Healthcare Big Data Analytics Information Asymmetry Influences Organizational Design Absorptive Choices

Although the relationship between big data analytics (BDA) organizational, firm, and financial performance is well supported, little attention has been paid in extant research to exploring the organizational design issues resulting from information asymmetry caused by BDA; in particular, organizational absorptive choices that include acquisitions, mergers, reorganization, executive changes, or board of director adjustments. The purpose of this qualitative single case study within a U.S. hospital was to explore the conditions and circumstances that influence absorptive organizational design choices of hospital administration. The theoretical base of this study is Pfeffer and Salancik’s resource dependence theory (RDT). Logic model data analysis approach was conducted on primary data attained from semistructured interviews of 12 volunteers of hospital administration and secondary data from grey literature. The findings of this study suggest resource dependence theory activities of executive changes and intra-organizational structural changes moderate information asymmetry. Communication was the major theme, while properly formed BDA questions, prospective reimbursement models, evolving BDA demands, intellectual capacity gap, and operational complexity were minor themes that influenced organizational design decisions. The practical implications emphasize communication among multidisciplinary groups and boundary-spanning organizational design strategies to moderate information asymmetry. Lastly, the positive social change implication may be the increased BDA adoption in hospital administration from the improved communication among individual actors of multidisciplinary BDA groups

Characterizing infection in anti-neutrophil cytoplasmic antibody-associated vasculitis:results from a longitudinal, matched-cohort data linkage study

We wish to thank Information Division Services Scotland for assisting with data linkage and data access in the National Safe Haven. Information presented in this article was previously presented as a poster at the American College of Rheumatology Annual Conference 2018, Chicago, IL, USA. The study was conceived by S.H.S., A.M., C.B. and N.B. All authors contributed to the study design and data collection. Data analysis and interpretation and drafting of the manuscript were conducted by all authors. C.B. and N.B. were joint senior authors. All authors critically reviewed the manuscript and approved the final version. Funding: S.H.S. and the study were funded by the Aberdeen Development Trust and the Farr Institute of Health Informatics Research. The Farr Institute is supported by a 10-funder consortium: Arthritis Research UK, the British Heart Foundation, Cancer Research UK, the Economic and Social Research Council, the Engineering and Physical Sciences Research Council, the Medical Research Council, the National Institute of Health Research, the National Institute for Social Care and Health Research (Welsh Assembly Government), the Chief Scientist Office (Scottish Government Health Directorates) and the Wellcome Trust (Scotland MR/K007017/1). Disclosure statement: L.E. is a GlaxoSmithKline employee. The other authors have declared no conflicts of interest.Peer reviewedPublisher PD

Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics

Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications