Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

انشاء خوارزمية لتوزيع الاحمال للحوسبة السحابية تحت عبء العمل المتقطع

Cloud computing is a recent emerging technology in IT industry. It is an evolution of previous computing models such as grid computing. It enables a wide range of users to access a large sharing pool of resources over the Internet. In such complex system, there is a tremendous need for efficient load balancing scheme in order to satisfy peak user demands and provide high quality of services. Load balancing is a methodology to distribute workload across multiple nodes over the network links to achieve optimal utilizing of resources, minimizing data processing time and response time, and avoid overload. One of the challenging problems that affect the load balancing process is bursty workload. Burstiness occurs in workloads in which bursts of requests aggregate together during short periods of time and create periods of peak system utilization. This can lead to dramatically degradation in system performance. Several load balancing algorithms had been proposed which focus on key elements such as processing time, response time and processing costs. However these algorithms neglect cases of bursty workload. In the same time, research works which deals with the problem of bursty workload are quite a few. Motivated by this problem, this research comes to handle the load balancing problem in cloud computing under bursty workload by predicting the variation in the request rate and apply the suitable load balancing algorithm according to the predicted load status. In turn, the selected load balancing algorithm assign the received request to a virtual machine based on information supplied by a fuzzifier. The proposed algorithm has been tested and the experiments results showed that our algorithm improves the cloud system performance by decreasing the response and the data center processing time compared with other algorithms. The decrement is about 2ms when the instruction length is 250 Byte, while this improvement becomes more obvious by decreasing the response time about 10ms and the processing time about 5ms when the instruction length is increased to 1000 Byte

Neural network-based minutiae extraction for fingerprint verification system

Fingerprint is one of the most important biometrics that has been employed for verification systems. Fingerprint is characterized by two fundamental properties; Easy to acquire, and it is unique for each person. This paper presents minutia extraction method based on Neural Network-based. These features can be used in verification systems. The verification process includes four main phases: image acquisition, preprocessing, feature extraction, and pattern matching. The method is applied on a set of fingerprint images and the results show that the average matching accuracy of fingerprints is 91.6%

The effect of a digital targeted client communication intervention on pregnant women’s worries and satisfaction with antenatal care in Palestine–A cluster randomized controlled trial

Background: The eRegCom cluster randomized controlled trial assesses the effectiveness of targeted client communication (TCC) via short message service (SMS) to pregnant women, from a digital maternal and child health registry (eRegistry) in Palestine, on improving attendance and quality of care. In this paper, we assess whether this TCC intervention could also have unintended consequences on pregnant women’s worries, and their satisfaction with antenatal care (ANC). Methods: We interviewed a sub-sample of Arabic-speaking women attending ANC at public primary healthcare clinics, randomized to either the TCC intervention or no TCC (control) in the eRegCom trial, who were in 38 weeks of gestation and had a phone number registered in the eRegistry. Trained female data collectors interviewed women by phone from 67 intervention and 64 control clusters, after securing informed oral consent. The Arabic interview guide, pilot-tested prior to the data collection, included close-ended questions to capture the woman’s socio-demographic status, agreement questions about their satisfaction with ANC services, and the 13-item Cambridge Worry Scale (CWS). We employed a non-inferiority study design and an intention-to-treat analysis approach. Results: A total of 454 women, 239 from the TCC intervention and 215 from the control arm participated in this sub-study. The mean and standard deviation of the CWS were 1.8 (1.9) for the intervention and 2.0 (1.9) for the control arm. The difference in mean between the intervention and control arms was -0.16 (95% CI: -0.31 to -0.01) after adjusting for clustering, which was below the predefined non-inferiority margin of 0.3. Women in both groups were equally satisfied with the ANC services they received. Conclusion: The TCC intervention via SMS did not increase pregnancy-related worries among recipients. There was no difference in women’s satisfaction with the ANC services between intervention and control arms

eRegCom—Quality Improvement Dashboard for healthcare providers and Targeted Client Communication to pregnant women using data from an electronic health registry to improve attendance and quality of antenatal care: study protocol for a multi-arm cluster randomized trial

Background This trial evaluates interventions that utilize data entered at point-of-care in the Palestinian maternal and child eRegistry to generate Quality Improvement Dashboards (QID) for healthcare providers and Targeted Client Communication (TCC) via short message service (SMS) to clients. The aim is to assess the effectiveness of the automated communication strategies from the eRegistry on improving attendance and quality of care for pregnant women. Methods This four-arm cluster randomized controlled trial will be conducted in the West Bank and the Gaza Strip, Palestine, and includes 138 clusters (primary healthcare clinics) enrolling from 45 to 3000 pregnancies per year. The intervention tools are the QID and the TCC via SMS, automated from the eRegistry built on the District Health Information Software 2 (DHIS2) Tracker. The primary outcomes are appropriate screening and management of anemia, hypertension, and diabetes during pregnancy and timely attendance to antenatal care. Primary analysis, at the individual level taking the design effect of the clustering into account, will be done as intention-to-treat. Discussion This trial, embedded in the implementation of the eRegistry in Palestine, will inform the use of digital health interventions as a health systems strengthening approach