Physicians' attitudes towards ePrescribing – evaluation of a Swedish full-scale implementation

<p>Abstract</p> <p>Background</p> <p>The penetration rate of Electronic Health Record (EHR) systems in health care is increasing. However, many different EHR-systems are used with varying ePrescription designs and functionalities. The aim of the present study was to evaluate experienced ePrescribers' attitudes towards ePrescribing for suggesting improvements.</p> <p>Methods</p> <p>Physicians (n = 431) from seven out of the 21 Swedish health care regions, using one of the six most widely implemented EHR-systems with integrated electronic prescribing modules, were recruited from primary care centers and hospital clinics of internal medicine, orthopaedics and surgery. The physicians received a web survey that comprised eight questions on background data and 19 items covering attitudes towards ePrescribing. Forty-two percent (n = 199) of the physicians answered the questionnaire; 90% (n = 180) of the respondents met the inclusion criteria and were included in the final analysis.</p> <p>Results</p> <p>A majority of the respondents regarded their EHR-system easy to use in general (81%), and for the prescribing of drugs (88%). Most respondents believed they were able to provide the patients better service by ePrescribing (92%), and regarded ePrescriptions to be time saving (91%) and to be safer (83%), compared to handwritten prescriptions. Some of the most frequently reported weaknesses were: not clearly displayed price of drugs (43%), complicated drug choice (21%), and the perception that it was possible to handle more than one patient at a time when ePrescribing (13%). Moreover, 62% reported a lack of receipt from the pharmacy after successful transmission of an ePrescription. Although a majority (73%) of the physicians reported that they were always or often checking the ePrescription a last time before transmitting, 25% declared that they were seldom or never doing a last check. The respondents suggested a number of improvements, among others, to simplify the drug choice and the cancellation of ePrescriptions.</p> <p>Conclusion</p> <p>The Swedish physicians in the group studied were generally satisfied with their specific EHR-system and with ePrescribing as such. However, identified weaknesses warrant improvements of the EHR-systems as well as of their implementation in the individual health care organisation.</p

Pharmacokinetics - Effect Relations of Glibenclamide and its Metabolites in Humans.

Glibenclamide (Gb) is the most commonly employed sulphonylurea worldwide for the treatment of non-insulin-dependent diabetes mellitus (NIDDM). There has been uncertainty concerning its pharmacokinetics (PK) and pharmacodynamics (PD). Numerous long-lasting hypoglycemic reactions, which have sometimes been fatal, have been reported and have been difficult to reconsile with an allegedly short elimination half-life of Gb and presumedly low hypoglycemic activity of the metabolites, 4-trans-hydroxy-glibenclamide (M1) and 3-cis-hydroxy-glibenclamide (M2). Therefore, the objective of this thesis was to investigate the pharmacokinetics and concentration - effect relations of Gb and these two major metabolites. A simple liquid chromatographic method was developed for the analysis of low concentrations of Gb (1 ng/mL) and the two metabolites (5 ng/mL). The method was shown to be suitable for pharmacokinetic studies on Gb and its metabolites. The terminal elimination half-life of Gb was examined in 10 NIDDM subjects after cessation of long-term treatment, and it was concluded that the elimination of Gb during therapeutic conditions and chronic dosing is slower than previously thought. The long terminal half-life may help to explain why Gb may sometimes provoke long-lasting hypoglycemic reactions, and the result adds support to the clinical experience that Gb has a relatively long effect duration and supports the use of once-daily dosage of Gb. The PK of Gb and of its metabolites per se after intravenous administration was studied and compared in 8 healthy volunteers. It was concluded that the two Gb metabolites have similar PK except for volume of distribution (V) and renal clearance (CL). The PK of Gb and its metabolites was investigated and compared in 11 diabetic patients with impaired renal function (IRF) and 11 diabetic patients with normal renal function (NRF). It was concluded that very small amounts of M1 and M2 were excreted in the urine in the IRF group, and that the fraction excreted correlated significantly with renal function. CL/F for Gb was higher in the IRF group than in the NRF control group. Biliary excretion of Gb and metabolites seems likely. The hypoglycemic and insulin-releasing effect of M1 and M2 was assessed after intravenous administration of each metabolite in a placebo-controlled, randomized, single-blind crossover study. It was found that both metabolites have a marked hypoglycemic effect. The relationship between serum concentrations of Gb, M1 and M2 and their respective effects on blood glucose levels was studied. While there was no simple, direct relationship between drug concentrations and the hypoglycemic effect, consideration of PK and PD time dependencies by means of population PK/PD modelling demonstrated a relationship, involving both Gb and its active metabolites. It was concluded that the major metabolites contribute to the hypoglycemic effect subsequent in vivo formation in the body, that at low concentrations they may have higher activity and a longer effect duration than the parent drug per se. This should be clinically relevant

Screening for Dyslexia Using Eye Tracking during Reading

<div><p>Dyslexia is a neurodevelopmental reading disability estimated to affect 5–10% of the population. While there is yet no full understanding of the cause of dyslexia, or agreement on its precise definition, it is certain that many individuals suffer persistent problems in learning to read for no apparent reason. Although it is generally agreed that early intervention is the best form of support for children with dyslexia, there is still a lack of efficient and objective means to help identify those at risk during the early years of school. Here we show that it is possible to identify 9–10 year old individuals at risk of persistent reading difficulties by using eye tracking during reading to probe the processes that underlie reading ability. In contrast to current screening methods, which rely on oral or written tests, eye tracking does not depend on the subject to produce some overt verbal response and thus provides a natural means to objectively assess the reading process as it unfolds in real-time. Our study is based on a sample of 97 high-risk subjects with early identified word decoding difficulties and a control group of 88 low-risk subjects. These subjects were selected from a larger population of 2165 school children attending second grade. Using predictive modeling and statistical resampling techniques, we develop classification models from eye tracking records less than one minute in duration and show that the models are able to differentiate high-risk subjects from low-risk subjects with high accuracy. Although dyslexia is fundamentally a language-based learning disability, our results suggest that eye movements in reading can be highly predictive of individual reading ability and that eye tracking can be an efficient means to identify children at risk of long-term reading difficulties.</p></div

Experimental test protocol based on repeated cross-validation with internal feature selection.

<p>The entire dataset is randomly divided into 10 subsets, setting aside one subset (10% of all subjects) as a test sample and the remaining nine subsets (90% of all subjects) as a training sample. A feature selection algorithm is applied on the training sample to select a subset of <i>n</i> features. Using this feature subset, a classification algorithm is applied on the training sample, producing a parametrized classifier as output. This classifier is then used to classify the subjects in the test sample and the predicted results are compared to the actual identity (HR or LR) of the test subjects. This step is iterated 10 times, with a different training and test set for each iteration. After one completed run of 10-fold cross validation, each subject in the entire dataset has been tested exactly once, while we still have maintained a strict separation between training and test subjects. To reduce the variance of the cross-validated performance estimate, the whole process is repeated 100 times with different initial random splits of the original dataset. The final estimate of the expected predictive performance is calculated by averaging the cross-validation performance over all 100 repetitions. This estimate represents the expected prediction accuracy of the final model. The final model–the one we would deploy in practice–is the classifier we would build from the entire dataset using feature selection method <i>m</i> to select <i>n</i> features.</p

Box plots of features selected in 1000 (100%) training folds by the best performing classification model.

<p>The box plots show the distribution of values, normalized to range between 0 and 1, by feature and group HR (<i>n</i> = 97) and LR (<i>n</i> = 88).</p

Example of eye movement analysis where the horizontal (CH) and vertical (CV) eye movement signal is plotted over time.

<p>Light green stripes represent saccades, light gray areas represent fixations. Light blue stripes represent sweeping movements (most commonly return sweeps) and red stripes represent transients. Plot <b>A</b> represents a subject from the HR group and plot <b>B</b> a subject from the LR group. The analysis was performed using a dynamic dispersion threshold algorithm based on the physiological properties of the foveal and parafoveal fields of vision. The algorithm analyzes the tracking signal sample by sample and switches between four mutually exclusive states: distortions, transients, fixations, and saccades. A distortion state is detected if the horizontal or vertical signal is missing for both eyes. A transient state is detected if the horizontal and vertical position is within a threshold distance of 0.5 degrees + signal noise (2.5 × RMS error of the last 25 samples) from the average of the samples in the current state. A fixation state is detected when the eyes have remained stable for at least 50 ms, and a saccade state when the eyes have moved beyond the threshold distance. Once a change of state is detected, the samples of the previous state are identified as a new event.</p

Prediction accuracy as a function of the numbers of features selected during training.

<p>Accuracy is shown for classifiers based on recursive feature elimination (solid blue line), random feature selection (dashed red line), and chance (dotted green line). Chance-level accuracy is based on Y-randomization of training data. Accuracy is the percentage of correctly identified HR and LR subjects averaged over 100 × 10-fold cross-validation. Maximum accuracy, 95.6%, (± 4.5%), is obtained using recursive feature elimination to select 48 features from the original feature set of 168 features. Shaded regions indicate mean ± 1 standard deviation over the 100 repetitions. Performance at chance level, averaged over the feature subset sizes, is 49.3%.</p

Frequency of features selected during training of the best performing classification model grouped by progressive/regressive fixation- and saccade features.

<p>The Y-axis shows the number of times a feature in the original feature set was selected by the recursive feature elimination algorithm (SVM-RFE) during the 10 x 100 cross-validation with internal feature selection. The X-axis shows the features (represented by their index in the dataset) grouped by progressive/regressive fixation- and saccade features.</p