Forecasting daily attendances at an emergency department to aid resource planning

<p>Abstract</p> <p>Background</p> <p>Accurate forecasting of emergency department (ED) attendances can be a valuable tool for micro and macro level planning.</p> <p>Methods</p> <p>Data for analysis was the counts of daily patient attendances at the ED of an acute care regional general hospital from July 2005 to Mar 2008. Patients were stratified into three acuity categories; i.e. P1, P2 and P3, with P1 being the most acute and P3 being the least acute. The autoregressive integrated moving average (ARIMA) method was separately applied to each of the three acuity categories and total patient attendances. Independent variables included in the model were public holiday (yes or no), ambient air quality measured by pollution standard index (PSI), daily ambient average temperature and daily relative humidity. The seasonal components of weekly and yearly periodicities in the time series of daily attendances were also studied. Univariate analysis by t-tests and multivariate time series analysis were carried out in SPSS version 15.</p> <p>Results</p> <p>By time series analyses, P1 attendances did not show any weekly or yearly periodicity and was only predicted by ambient air quality of PSI > 50. P2 and total attendances showed weekly periodicities, and were also significantly predicted by public holiday. P3 attendances were significantly correlated with day of the week, month of the year, public holiday, and ambient air quality of PSI > 50.</p> <p>After applying the developed models to validate the forecast, the MAPE of prediction by the models were 16.8%, 6.7%, 8.6% and 4.8% for P1, P2, P3 and total attendances, respectively. The models were able to account for most of the significant autocorrelations present in the data.</p> <p>Conclusion</p> <p>Time series analysis has been shown to provide a useful, readily available tool for predicting emergency department workload that can be used to plan staff roster and resource planning.</p

A computational framework for complex disease stratification from multiple large-scale datasets.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine

Urinary and sexual dysfunction after rectal cancer treatment

In light of the improving prognosis for patients with rectal cancer, the quality of functional outcome has become increasingly important. Despite the good functional results achieved by expert surgeons, large multicenter studies show that urogenital dysfunction remains a common problem after rectal cancer treatment. More than half of patients experience a deterioration in sexual function, consisting of ejaculatory problems and impotence in men and vaginal dryness and dyspareunia in women. Urinary dysfunction occurs in one-third of patients treated for rectal cancer. Surgical nerve damage is the main cause of urinary dysfunction. Radiotherapy seems to have a role in the development of sexual dysfunction, without affecting urinary function. Pelvic autonomic nerves are especially at risk in cases of low rectal cancer and during abdominoperineal resection. Data concerning nerve damage during laparoscopic surgery for resection of rectal cancer are awaited. Structured education of surgeons with regard to pelvic neuroanatomy, and systematic registration of identified nerves, could well be the key to improving functional outcome for these patients. Meanwhile, patients should be informed of all associated risks before their operation, and their functional status should be evaluated before and after surgery.Surgical oncolog