Business models & business cases for point-of-care testing

Point-Of-Care Testing (POCT) enables clinical tests at or near the patient, with test results that are available instantly or in a very short time frame, to assist caregivers with immediate diagnosis and/or clinical intervention. The goal of POCT is to provide accurate, reliable, fast, and cost-effective information about patient condition. POCT can be part of the solution to the rising healthcare and welfare costs without any loss of healthcare quality. In this research, business models are used to create business cases in order to assess the viability of POCT. Two methods to create business models were designed by tailoring and extending them from an existing method. It was found that the method used has impact on the resulting business case. POCT was assessed to be viable in all business cases created for the specific case study used

Coinductive interpreters for process calculi

This paper suggests functional programming languages with coinductive types as suitable devices for prototyping process calculi. The proposed approach is independent of any particular process calculus and makes explicit the different ingredients present in the design of any such calculi. In particular structural aspects of the underlying behaviour model (e.g. the dichotomies such as active vs reactive, deterministic vs nondeterministic) become clearly separated from the interaction structure which defines the synchronisation discipline. The approach is illustrated by the detailed development in Charity of an interpreter for a family of process languages.(undefined

Optimizing projectional radiographic imaging of the abdomen of obese patients: an e-Delphi study

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Purpose: Obesity is increasing in prevalence globally, with increased demands placed on radiology departments to image obese patients to assist with diagnosis and management. The aim of this study was to determine perceived best practice techniques currently used in clinical practice for projectional radiography of the abdomen for obese patients with the aim to help elucidate areas for future research and education needs in this field. Experimental Design: A two round e-Delphi study was undertaken to establish a consensus within a reference group of expert Australian clinical educator diagnostic radiographers (CEDRs). Initially, a conceptual map of issues regarding imaging obese patients was undertaken by analysing interview transcripts of 12 CEDRs. This informed an online questionnaire design used in Delphi rounds 1 and 2. A consensus threshold was set <75% “agreement/disagreement”, with 15 and 14 CEDRs participating in rounds 1 and 2, respectively. Results: Seven of the 11 statements reach consensus after round 2. Consensus on using a combination of higher peak kilovoltage (kVp) and milliampere-seconds (mAs) to increase radiation exposure increased source-to-image distance and tighter collimation was achieved. There was no consensus regarding patient positioning practices or patient communication strategies. The expert group reported the importance of personal confidence and treating patients as individuals when applying techniques. Conclusion: Diversity of experts' opinions and current practice may be due to the variations in obese patients’ size and presentation. Therefore, there is a need for extensive empirical evidence to underpin practice and education resources for radiographers when imaging obese patients

The effects of computed tomography image characteristics and knot spacing on the spatial accuracy of B-spline deformable image registration in the head and neck geometry

Objectives: To explore the effects of computed tomography (CT) image characteristics and B-spline knot spacing (BKS) on the spatial accuracy of a B-spline deformable image registration (DIR) in the head-and-neck geometry. Methods: The effect of image feature content, image contrast, noise, and BKS on the spatial accuracy of a B-spline DIR was studied. Phantom images were created with varying feature content and varying contrast-to-noise ratio (CNR), and deformed using a known smooth B-spline deformation. Subsequently, the deformed images were repeatedly registered with the original images using different BKSs. The quality of the DIR was expressed as the mean residual displacement (MRD) between the known imposed deformation and the result of the B-spline DIR. Finally, for three patients, head-and-neck planning CT scans were deformed with a realistic deformation field derived from a rescan CT of the same patient, resulting in a simulated deformed image and an a-priori known deformation field. Hence, a B-spline DIR was performed between the simulated image and the planning CT at different BKSs. Similar to the phantom cases, the DIR accuracy was evaluated by means of MRD. Results: In total, 162 phantom registrations were performed with varying CNR and BKSs. MRD-values = +/- 250 HU and noise <+/- 200 HU. Decreasing the image feature content resulted in increased MRD-values at all BKSs. Using BKS = 15 mm for the three clinical cases resulted in an average MRD <1.0 mm. Conclusions: For synthetically generated phantoms and three real CT cases the highest DIR accuracy was obtained for a BKS between 10-20 mm. The accuracy decreased with decreasing image feature content, decreasing image contrast, and higher noise levels. Our results indicate that DIR accuracy in clinical CT images (typical noise levels <+/- 100 HU) will not be effected by the amount of image noise

Time-to-birth prediction models and the influence of expert opinions

Preterm birth is the leading cause of death among children under five years old. The pathophysiology and etiology of preterm labor are not yet fully understood. This causes a large number of unnecessary hospitalizations due to high--sensitivity clinical policies, which has a significant psychological and economic impact. In this study, we present a predictive model, based on a new dataset containing information of 1,243 admissions, that predicts whether a patient will give birth within a given time after admission. Such a model could provide support in the clinical decision-making process. Predictions for birth within 48 h or 7 days after admission yield an Area Under the Curve of the Receiver Operating Characteristic (AUC) of 0.72 for both tasks. Furthermore, we show that by incorporating predictions made by experts at admission, which introduces a potential bias, the prediction effectiveness increases to an AUC score of 0.83 and 0.81 for these respective tasks

A critical look at studies applying over-sampling on the TPEHGDB dataset

Preterm birth is the leading cause of death among young children and has a large prevalence globally. Machine learning models, based on features extracted from clinical sources such as electronic patient files, yield promising results. In this study, we review similar studies that constructed predictive models based on a publicly available dataset, called the Term-Preterm EHG Database (TPEHGDB), which contains electrohysterogram signals on top of clinical data. These studies often report near-perfect prediction results, by applying over-sampling as a means of data augmentation. We reconstruct these results to show that they can only be achieved when data augmentation is applied on the entire dataset prior to partitioning into training and testing set. This results in (i) samples that are highly correlated to data points from the test set are introduced and added to the training set, and (ii) artificial samples that are highly correlated to points from the training set being added to the test set. Many previously reported results therefore carry little meaning in terms of the actual effectiveness of the model in making predictions on unseen data in a real-world setting. After focusing on the danger of applying over-sampling strategies before data partitioning, we present a realistic baseline for the TPEHGDB dataset and show how the predictive performance and clinical use can be improved by incorporating features from electrohysterogram sensors and by applying over-sampling on the training set

Quality of interhospital transport of critically ill patients: a prospective audit

INTRODUCTION: The aim of transferring a critically ill patient to the intensive care unit (ICU) of a tertiary referral centre is to improve prognosis. The transport itself must be as safe as possible and should not pose additional risks. We performed a prospective audit of the quality of interhospital transports to our university hospital-based medical ICU. METHODS: Transfers were undertaken using standard ambulances. On departure and immediately after arrival, the following data were collected: blood pressure, heart rate, body temperature, oxygen saturation, arterial blood gas analysis, serum lactic acid, plasma haemoglobin concentration, blood glucose, mechanical ventilation settings, use of vasopressor/inotropic drugs, and presence of venous and arterial catheters. Ambulance personnel completed forms describing haemodynamic and ventilatory data during transport. Data were collected by our research nurse and analyzed. RESULTS: A total of 100 consecutive transfers of ICU patients over a 14-month period were evaluated. Sixty-five per cent of patients were mechanically ventilated; 38% were on vasoactive drugs. Thirty-seven per cent exhibited an increased number of vital variables beyond predefined thresholds after transport compared with before transport; 34% had an equal number; and 29% had a lower number of vital variables beyond thresholds after transport. The distance of transport did not correlate with the condition on arrival. Six patients died within 24 hours after arrival; vital variables in these patients were not significantly different from those in patients who survived the first 24 hours. ICU mortality was 27%. Adverse events occurred in 34% of transfers; in 50% of these transports, pretransport recommendations given by the intensivist of our ICU were ignored. Approximately 30% of events may be attributed to technical problems. CONCLUSION: On aggregate, the quality of transport in our catchment area carried out using standard ambulances appeared to be satisfactory. However, examination of the data in greater detail revealed a number of preventable events. Further improvement must be achieved by better communication between referring and receiving hospitals, and by strict adherence to checklists and to published protocols. Patients transported between ICUs are still critically ill and should be treated as such

Perception of Nuclear Energy and Coal in France and the Netherlands

This study focuses on the perception of large scale application of nuclear energy and coal in the Netherlands and France. The application of these energy-sources and the risks and benefits are judged differently by various group in society. In Europe, France has the highest density of nuclear power plants and the Netherlands has one of the lowest. In both countries scientists and social scientists completed a questionnaire assessing the perception of the large scale application of both energy sources. Furthermore, a number of variables relating to the socio cultural and political circumstances were measured. The results indicate that the French had a higher risk perception and a more negative attitude toward nuclear power than the Dutch. But they also assess the benefits of the use of nuclear power to be higher. Explanations for these differences are discussed