Energy-efficient vertical handover parameters, classification and solutions over wireless heterogeneous networks: a comprehensive survey

In the last few decades, the popularity of wireless networks has been growing dramatically for both home and business networking. Nowadays, smart mobile devices equipped with various wireless networking interfaces are used to access the Internet, communicate, socialize and handle short or long-term businesses. As these devices rely on their limited batteries, energy-efficiency has become one of the major issues in both academia and industry. Due to terminal mobility, the variety of radio access technologies and the necessity of connecting to the Internet anytime and anywhere, energy-efficient handover process within the wireless heterogeneous networks has sparked remarkable attention in recent years. In this context, this paper first addresses the impact of specific information (local, network-assisted, QoS-related, user preferences, etc.) received remotely or locally on the energy efficiency as well as the impact of vertical handover phases, and methods. It presents energy-centric state-of-the-art vertical handover approaches and their impact on energy efficiency. The paper also discusses the recommendations on possible energy gains at different stages of the vertical handover process

A Quantitative Method for Assessment of Prescribing Patterns Using Electronic Health Records

Background: Most available quality indicators for hospitals are represented by simple ratios or proportions, and are limited to specific events. A generalized method that can be applied to diverse clinical events has not been developed. The aim of this study was to develop a simple method of evaluating physicians ’ prescription patterns for diverse events and their level of awareness of clinical practice guidelines. Methods and Findings: We developed a quantitative method called Prescription pattern Around Clinical Event (PACE), which is applicable to electronic health records (EHRs). Three discrete prescription patterns (intervention, maintenance, and discontinuation) were determined based on the prescription change index (PCI), which was calculated by means of the increase or decrease in the prescription rate after a clinical event. Hyperkalemia and Clostridium difficile-associated diarrhea (CDAD) were used as example cases. We calculated the PCIs of 10 drugs related to hyperkalemia, categorized them into prescription patterns, and then compared the resulting prescription patterns with the known standards for hyperkalemia treatment. The hyperkalemia knowledge of physicians was estimated using a questionnaire and compared to the prescription pattern. Prescriptions for CDAD were also determined and compared to clinical knowledge. Clinical data of 1698, 348, and 1288 patients were collected from EHR data. The physicians prescribing behaviors for hyperkalemia an

A Quantitative Method for Assessment of Prescribing Patterns Using Electronic Health Records

<div><p>Background</p><p>Most available quality indicators for hospitals are represented by simple ratios or proportions, and are limited to specific events. A generalized method that can be applied to diverse clinical events has not been developed. The aim of this study was to develop a simple method of evaluating physicians' prescription patterns for diverse events and their level of awareness of clinical practice guidelines.</p><p>Methods and Findings</p><p>We developed a quantitative method called Prescription pattern Around Clinical Event (PACE), which is applicable to electronic health records (EHRs). Three discrete prescription patterns (intervention, maintenance, and discontinuation) were determined based on the prescription change index (PCI), which was calculated by means of the increase or decrease in the prescription rate after a clinical event. Hyperkalemia and Clostridium difficile-associated diarrhea (CDAD) were used as example cases. We calculated the PCIs of 10 drugs related to hyperkalemia, categorized them into prescription patterns, and then compared the resulting prescription patterns with the known standards for hyperkalemia treatment. The hyperkalemia knowledge of physicians was estimated using a questionnaire and compared to the prescription pattern. Prescriptions for CDAD were also determined and compared to clinical knowledge. Clinical data of 1698, 348, and 1288 patients were collected from EHR data. The physicians prescribing behaviors for hyperkalemia and CDAD were concordant with the standard knowledge. Prescription patterns were well correlated with individual physicians' knowledge of hyperkalemia (κ = 0.714). Prescribing behaviors according to event severity or clinical condition were plotted as a simple summary graph.</p><p>Conclusion</p><p>The algorithm successfully assessed the prescribing patterns from the EHR data. The prescription patterns were well correlated with physicians' knowledge. We expect that this algorithm will enable quantification of prescribers' adherence to clinical guidelines and be used to facilitate improved prescribing practices.</p></div

Characteristics of the study population.

a<p>Hypothesis testing: application to hyperkalemia.</p>b<p>Agreement between prescriber knowledge and prescription pattern.</p>c<p>Validation study: application to <i>Clostridium difficile</i>-associated diarrhea.</p>d<p>Individual patients.</p>e<p>Age at first admission of an individual patient.</p>f<p>Underlying diseases were identified by the International Classification of Diseases 10^th Revision (ICD-10) codes: renal failure (I12.0, I13.0, I13.1, N17.x–19.x), heart failure (I11.0, I13.0, I13.2, I50.0, I50.1, I50.9) and diabetes (E10.x–E14.x).</p><p>SD, standard deviation.</p

Prescription pattern for hyperkalemia measured by the PACE algorithm.

<p>Prescription patterns were categorized into three types based on the prescription change index (PCI): intervention pattern (PCI>1.500, bottom left), maintenance pattern (0.667</p

Prescriber hyperkalemia knowledge regarding therapeutic decisions related to 10 hyperkalemia-related drugs, as determined by questionnaire.

<p>Prescriber hyperkalemia knowledge regarding therapeutic decisions related to 10 hyperkalemia-related drugs, as determined by questionnaire.</p

Changes in prescription patterns according to <i>Clostridium difficile</i>-associated diarrhea (CDAD).

<p>(a) and (b): Prescription patterns of metronidazole (a), cefotaxime, cefpiramide, and clindamycin (b) on the day on which a <i>C. difficile</i> toxin test was ordered (before the toxin test result). (c–f): prescription patterns on the day on which toxin test results were confirmed; (c) and (d): <i>C. difficile</i> toxin negative; (e) and (f): <i>C. difficile</i> toxin positive.</p