Driving Innovation During Times of Growth

As the official coverage provider, the Cornell HR Review covered the keynote and panel discussions at the Human Capital Association’s (HCA) 9th Annual Symposium. The HCA is a student run organization within Cornell’s Johnson School and School of Industrial and Labor Relations, which strives to drive the future of the HR profession through educational and professional development opportunities across the Cornell community. The symposium provides a forum for students, faculty and corporate executives to explore the various dimensions of human capital issues prevalent in global business. This year’s symposium topic focused on driving innovation proactively through human resources and across organizations as we recover from the economic crisis of the past several years

Response times in healthcare systems

It is a goal universally acknowledged that a healthcare system should treat its patients – and especially those in need of critical care – in a timely manner. However, this is often not achieved in practice, particularly in state-run public healthcare systems that suffer from high patient demand and limited resources. In particular, Accident and Emergency (A&E) departments in England have been placed under increasing pressure, with attendances rising year on year, and a national government target whereby 98% of patients should spend 4 hours or less in an A&E department from arrival to admission, transfer or discharge. This thesis presents techniques and tools to characterise and forecast patient arrivals, to model patient flow and to assess the response-time impact of different resource allocations, patient treatment schemes and workload scenarios. Having obtained ethical approval to access five years of pseudonymised patient timing data from a large case study A&E department, we present a number of time series models that characterise and forecast daily A&E patient arrivals. Patient arrivals are classified as one of two arrival streams (walk-in and ambulance) by mode of arrival. Using power spectrum analysis, we find the two arrival streams exhibit different statistical properties and hence require separate time series models. We find that structural time series models best characterise and forecast walk-in arrivals, but that time series analysis may not be appropriate for ambulance arrivals; this prompts us to investigate characterisation by a non-homogeneous Poisson process. Next we present a hierarchical multiclass queueing network model of patient flow in our case study A&E department. We investigate via a discrete-event simulation the impact of class and time-based priority treatment of patients, and compare the resulting service-time densities and moments with actual data. Then, by performing bottleneck analysis and investigating various workload and resource scenarios, we pinpoint the resources that have the greatest impact on mean service times. Finally we describe an approximate generating function analysis technique which efficiently approximates the first two moments of customer response time in class-dependent priority queueing networks with population constraints. This technique is applied to the model of A&E and the results compared with those from simulation. We find good agreement for mean service times especially when minors patients are given priority

Modelling Regulatory Change V's Volume of Trade Effects in HSIF and HSI Volatility: A Note

In an earlier paper we adopted a Bi-variate BEKK-GARCH framework and employed a systematic approach to examine structural breaks in the Hang Seng Index and Index Futures market volatility. Switching dummy variables were included and tested in the variance equations to check for any structural changes in the autoregressive volatility structure due to the events that have taken place in the Hong Kong market surrounding the Asian markets crisis. In this paper we include measures of daily trading volume from both markets in the estimation. Likelihood ratio tests indicate the switching dummy variables become insignificant and the GARCH effects diminish but remain significant. There is some evidence that the Sequential arrival of Information Model provides a platform to explain these market induced effects when volume of trade is accounted for.Regulatory change, Multivariate Volatility, Volume of Trade.

Balayage of Fourier Transforms and the Theory of Frames

Every separable Hilbert space has an orthogonal basis. This allows every element in the Hilbert space to be expressed as an infinite linear combination of the basis elements. The structure of a basis can be too rigid in some situations. Frames gives us greater flexibility than bases. A frame in Hilbert space is a spanning set with the reconstruction property. A frame must satisfy both an upper frame bound and a lower frame bound. The requirement of an upper bound is rather modest. Most of the mathematical difficulty lies in showing the lower bound exists. We examine the theory of Beurling on Balayage of Fourier transforms and the role of spectral synthesis in this theory. Beurling showed that if the condition of Balayage holds, then the lower frame bound for a Fourier frame exists under suitable hypothesis. We extend this theory to obtain lower bound inequalities for other types of frames. We prove that lower bounds exist for generalized Fourier frames and two types of semi-discrete Gabor frames

Common 'Inborn Errors' of Metabolism in the General Population

Inborn errors of metabolism (IEMs) are a group of disorders characterised by the toxic accumulation or deficiency of circulating molecules (‘metabolites’) caused by rare genetic mutations. Previous studies have identified select examples where common variants at genes known to cause rare Mendelian diseases, including IEMs (e.g. LPL, DBH, PPM1K), are linked to phenotypic consequences in the general population that also occur in patients with the corresponding rare disease. Advances in genetic and metabolic profiling at an epidemiological scale now provide an opportunity to systematically identify such examples in the population and characterise their downstream effects on health. To assess the value of untargeted metabolomic profiling for the study of common complex diseases, I identified candidate mediators of the association between weight gain and future type 2 diabetes risk based on untargeted, large-scale metabolomic profiling of a large prospective cohort. Integration of metabolomics, genetic profiling and comprehensive longitudinal follow up for a range of diseases together with the application of Bayesian and genetic epidemiological methods enabled the identification of 20 candidate mediators. These reflected genetic susceptibility to adiposity and insulin resistance and explained most of the increased T2D risk associated with weight gain. To systematically characterise the phenotypic effects of variation at IEM-causing genes, I identified sentinel variants at these genes associated with plasma metabolites affected in the corresponding IEM across the genome. Of the 202 ‘IEM familiar’ variants (IFVs) detected, 187 at 89 loci were not previously reported as pathogenic for the corresponding IEM in ClinVar and 51 of these were associated with extreme metabolite levels (97.5th percentile) or had non-additive effects on metabolite levels. Phenome-wide assessment identified 1,553 IFV-phenotype associations at 108 loci. Of the detected associations, 703 at 54 loci were of particular interest as the phenotype related to a symptom of the corresponding IEM. At 24 of these 54 loci, genetic colocalisation detected shared genetic signals for IEM-related metabolites and phenotypes. For example, in line with norepinephrine deficiency causing dizziness on standing in severe cases of rare orthostatic hypotension (OMIM #223360), I identified a genetic signal at the dopamine beta hydroxylase (DBH) locus associated with decreased levels of the downstream catecholamine vanillylmandelate in the general population (IFV EAF=0.074). This signal was shared with that for lower risk of hypertension (based on 462,933 participants in UK Biobank) and other blood pressure-related phenotypes with high posterior probability of colocalisation (PPcolocalisation=0.94, with >99% of the probability explained by the IFV). This work uses untargeted metabolomic profiling to identify underlying disease mechanisms and demonstrate the proof-of-principle that common variants can have similar health consequences to those caused by rare mutations at the same IEM gene.Wellcome Trust, Cambridge Trus