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نتیجه جستجو - Quality improvement

تعداد مقالات یافته شده: 37
ردیف عنوان نوع
1 Performance assessment of coupled green-grey-blue systems for Sponge City construction
ارزیابی عملکرد سیستم های سبز و خاکستری-آبی همراه برای ساخت و ساز شهر اسفنجی-2020
In recent years, Sponge City has gained significant interests as a way of urban water management. The kernel of Sponge City is to develop a coupled green-grey-blue system which consists of green infrastructure at the source, grey infrastructure (i.e. drainage system) at the midway and receiving water bodies as the blue part at the terminal. However, the current approaches for assessing the performance of Sponge City construction are confined to green-grey systems and do not adequately reflect the effectiveness in runoff reduction and the impacts on receiving water bodies. This paper proposes an integrated assessment framework of coupled green-grey-blue systems on compliance of water quantity and quality control targets in Sponge City construction. Rainfall runoff and river system models are coupled to provide quantitative simulation evaluations of a number of indicators of landbased and river quality. A multi-criteria decision-making method, i.e., Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is adopted to rank design alternatives and identify the optimal alternative for Sponge City construction. The effectiveness of this framework is demonstrated in a typical plain river network area of Suzhou, China. The results demonstrate that the performance of Sponge City strategies increases with large scale deployment under smaller rainfall events. In addition, though surface runoff has a dilution effect on the river water quality, the control of surface pollutants can play a significant role in the river water quality improvement. This framework can be applied to Sponge City projects to achieve the enhancement of urban water management.
Keywords: Low impact development | Sponge City | Green-grey-blue system | Performance assessment | TOPSIS
مقاله انگلیسی
2 Implementation of a standardized voiding management protocol to reduce unnecessary re-catheterization - A quality improvement project
اجرای یک پروتکل استاندارد مدیریت تخلیه برای کاهش دوباره کاتتریزاسیون غیر ضروری - یک پروژه بهبود کیفیت-2020
Objective. To design and implement a standardized postoperative voiding management protocol that accurately identifies patients with urinary retention and reduces unnecessary re-catheterization. Methods. A postoperative voiding management protocol was designed and implemented in patients undergoing major, inpatient, non-radical abdominal surgery with a gynecologic oncologist. No patients had epidural catheters. The implemented quality improvement (QI) protocol included: 1) Foley removal at six hours postoperatively; 2) universal bladder scan after the first void; and 3) limiting re-catheterization to patientswith bladder scan volumes N150 ml. A total of 96 patients post-protocol implementation were compared to 52 patients preprotocol. Along with baseline demographic data and timing of catheter removal,we recorded the presence or absence of urinary retention and/or unnecessary re-catheterization and postoperative urinary tract infection rates. Fishers exact test and students t-tests were performed for comparisons. Results. The overall rate of postoperative urinary retention was 21.6% (32/148). The new voiding management protocol reduced the rate of unnecessary re-catheterization by 90% (13.5% vs 2.1%, p = 0.01), without overlooking true urinary retention (23.1% vs 20.8%, p = 0.83). Additionally, there was a significant increase in hospital-defined early discharge prior to 11:00 AM (4.0% vs 22.0%, p = 0.022). There was no difference in the postoperative urinary tract infection rate between the groups (p=1.00). Risk factors associatedwith urinary retention included older age (p b 0.01), use of medications with anticholinergic properties (p b 0.01), and preexisting urinary dysfunction (p b 0.01). Conclusions. Implementation of this new voiding management protocol reduced unnecessary recatheterization, captured and treated true urinary retention, and facilitated early hospital discharge
Keywords: Quality improvement | Bladder voiding | Urinary retention | Postoperative management | Gynecologic Oncology surgery | Urinary tract infection
مقاله انگلیسی
3 IMPROVING PAIN REASSESSMENT AND DOCUMENTATION RATES: A QUALITY IMPROVEMENT PROJECT IN A TEACHING HOSPITAL’S EMERGENCY DEPARTMENT
بهبود نرخ مستند سازی و ارزیابی مجدد : یک طرح ارتقاء کیفی در بخش آمادگی دانشگاه علوم پزشکی-2020
ED pain score reassessment and documentation rates were drastically low according to sampled data from the St. Margaret Hospital Emergency Department leading to difficult pain management encounters for clinicians. The purpose of this project was to improve pain score reassessment rates in ED patients who were discharged with extremity pain. Methods: This project was an 8-month, prepostinterventional (preintervention: September-November 2018, intervention: December 2018-January 2019, and postintervention: February-April 2019) quality improvement project that took place in a community hospital emergency department. Emergency nurses participated in 6 focus groups, allowing for the creation of focus group-themed interventions at the request of the nursing staff. Daily audits of pain reassessment and documentation rates for individual nurses took place during the month of January 2019. In addition, a weekly newsletter was created and reported the ED pain reassessment and documentation rates. Results: All patient encounters (581) were reviewed over the 8-month period. Baseline pain score reassessment and documentation rates were 36.2% (confidence interval, 30.3%-42.3%) in the emergency department. Pain reassessment and documentation rates increased to 62.3% (confidence interval, 56.8%-67.6%) during the 3-month postintervention period. Discussion: Implementing daily audits and weekly newsletters that created transparency of individual and group performances increased pain score reassessment and documentation rates.
Key words: Pain reassessment | Pain documentation | Practice improvement | Quality improvement | Pain management
مقاله انگلیسی
4 An integrated tool for optimal energy scheduling and power quality improvement of a microgrid under multiple demand response schemes
ابزاری یکپارچه برای برنامه ریزی بهینه انرژی و بهبود کیفیت انرژی ریز شببکه تحت برنامه های پاسخگویی به تقاضای چندگانه-2020
This paper presents an integrated tool to mitigate power quality issues in a microgrid through coordinating the operating schedule of its generating resources and loads. Such a microgrid includes renewable and conventional distributed energy resources, electric vehicles, energy storage, linear and nonlinear loads, while it serves as an example small-to-medium scale residential and commercial buildings. The proposed tool operates on a sequential, two-stage basis: at the first stage the energy management system (EMS) ensures that the microgrid’s generation resources and loads are dispatched at the minimum total system cost. In addition, it assesses the potential provision of flexibility services towards the system operator, relying on financially incentivized power signal requests. At the second stage, the power quality (PQ) framework evaluates whether the proposed optimal solution complies or not with several PQ standards applicable to the distribution level. The unique characteristic of the proposed tool is the self-triggered interaction between the EMS and the PQ framework, which identifies potential PQ violations, and restores the PQ indices to acceptable levels through an iterative process. Case studies have been performed with realistic model parameters to verify the performance of the proposed integrated tool. The obtained results demonstrate the effectiveness of the algorithm in managing voltage deviations, voltage unbalance, as well as harmonic distortions with a small additional cost for the total system.
Keywords: Buildings-to-grid integration | Energy management system | Harmonic distortion | Optimization | Power quality | Smart grid
مقاله انگلیسی
5 Decreasing Opioid Prescriptions in Women Undergoing Mastectomy and Breast Reconstruction
کاهش تجویز مواد افیونی در زنانی که تحت عمل ماستکتومی و بازسازی پستان قرار دارند-2020
Florida enacted legislation limiting opioid prescriptions and affecting the management of acute pain in the postoperative patient. Patients in a reconstructive surgery practice were receiving prescriptions for opioids as their primary method of pain management. Clinic providers identified a need to limit opioid prescriptions. Aim: The aim of this quality improvement initiative was to decrease the number of opioids prescribed while effectively managing pain in women undergoing mastectomy and breast tissue expander placement. Design: This is a quality improvement project. Methods: The Model for Improvement was used as a framework for this project. An evidence-based pain management plan was developed after a review of the breast reconstruction surgery literature. The plan incorporated preoperative patient and family education and the standard use of preemptive analgesia, intraoperative nerve blocks, and postoperative multimodal analgesia in all patients undergoing mastectomy with breast tissue expander placement. Patient and family education and perioperative pain management were provided to patients, and the number of opioid tablets prescribed was tracked. Results: Between January 2018 and August 2019, the average number of opioid tablets prescribed per patient decreased from 84.7 to 8.4. Conclusions: Opioid prescriptions can be decreased in women undergoing breast reconstruction with the use of patient education and multimodal analgesia.
مقاله انگلیسی
6 A Healthcare Improvement Initiative to Increase Multidisciplinary Pain Management Referrals for Youth with Sickle Cell Disease
یک ابتکار بهبود بهداشتی برای افزایش مراجعه به درمان های چند رشته ای برای مدیریت درد در جوانان مبتلا به بیماری سلول داسی شکل-2020
Chronic pain is a complex integration of biological, psychological, and social variables. Multidisciplinary pain management experts design interventions that treat the multidimensional experience. Children and adolescents with sickle cell disease (SCD) are at risk for chronic pain. Increased risk is associated with multiple characteristics including sickle cell genotype, age, gender, frequency of hospitalization, duration of hospitalization, and certain comorbid diagnoses. Referral to pain management professionals for this population is often delayed. Aims: To increase multidisciplinary pain management referrals for youth with SCD identified to be at risk for chronic pain. Design: Implementation research. Setting: One pediatric, academic medical facility serving as a regional sickle cell treatment center in the Midwest. Participants: Children greater than 2 years of age and less than 21 years of age with laboratory confirmed SCD. Methods: Implementation of an evidence-based screening tool using the consolidated framework for implementation research (CFIR) to guide project planning, design, and evaluation. The CFIR model was paired with the Plan-Do-Study-Act (PDSA) quality improvement methodology to operationalize workflow and sustain project aims. Results and Conclusions: Eighty-four percent of all eligible patients were screened during their routine sickle cell appointments resulting in a 110% increase in multidisciplinary pain management referrals. Future interventions and PDSA cycles are targeted at improving attendance at scheduled appointments, reducing hospitalizations, decreasing 30-day readmissions, and shortening length of stay. © 2020 American Society for Pain Management Nursing. Published by Elsevier Inc. All rights reserved.
مقاله انگلیسی
7 Improving compliance with diabetes care using a novel mnemonic: Aquality improvement project in an urban primary care clinic
رویکرد بهبود انطباق با مراقبت از دیابت با استفاده از یک حفظی: پروژه بهبود کیفیت در یک کلینیک مراقبت های اولیه شهری-2020
Aim: The aim of this quality improvement project was to improve compliance with the delivery of multi-dimensional patient-centered diabetes care using a streamlined mnemonic based on established diabetesguidelines.Methods: Using the Institute for Healthcare Improvement (IHI) model for improvement, four rapid plan-do-study-act cycles primarily implemented different tests of change over eight weeks using a streamlinedmnemonic – the LLaVES (lifestyle, laboratory tests, vaccination, examination, social/psychosocial) bundlefor screening and case management of patients with diabetes. Secondary to the LLaVES bundle, tests ofchange were also conducted for clinic team members and patients. Team member engagement utilizeda best-practice toolkit for effective communication. Patient engagement implemented validated modelsto evaluate knowledge of diabetes and stage of change. Data were analyzed using run charts to evaluatethe impact of interventions on outcomes. Overall compliance was measured as the diabetes manage-ment compliance rate (DMCR), composed of LLaVES implementation, team engagement, and patientengagement scores.Results: The diabetes management compliance rate increased by 72.2%, from a baseline of 49% to 84.4% ineight weeks. Team engagement increased from 76.6% to 92% while patient engagement increased from70.4% to 87.4%.Conclusions: Diabetes management is complex and requires team and patient engagement to implementa structured and multidimensional process. Composed of established, high-level evidence interventions,the LLaVES bundle is one approach to systematize complex care while taking into account the specificand unique challenges of a health care organization.
Keywords:Quality improvement | LLaVES | Complex diabetes care
مقاله انگلیسی
8 Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children’s surgery
استخراج داده های خاص و اختصاصی بیمار با فناوری های یادگیری ماشین برای پیش بینی لغو جراحی کودکان-2019
Background: Last-minute surgery cancellation represents a major wastage of resources and can cause significant inconvenience to patients. Our objectives in this study were: 1) To develop predictive models of last-minute surgery cancellation, utilizing machine learning technologies, from patient-specific and contextual data from two distinct pediatric surgical sites of a single institution; and 2) to identify specific key predictors that impact children’s risk of day-of-surgery cancellation. Methods and findings: We extracted five-year datasets (2012–2017) from the Electronic Health Record at Cincinnati Children’s Hospital Medical Center. By leveraging patient-specific information and contextual data, machine learning classifiers were developed to predict all patient-related cancellations and the most frequent four cancellation causes individually (patient illness, “no show,” NPO violation and refusal to undergo surgery by either patient or family). Model performance was evaluated by the area under the receiver operating characteristic curve (AUC) using ten-fold cross-validation. The best performance for predicting all-cause surgery cancellation was generated by gradient-boosted logistic regression models, with AUC 0.781 (95% CI: [0.764,0.797]) and 0.740 (95% CI: [0.726,0.771]) for the two campuses. Of the four most frequent individual causes of cancellation, “no show” and NPO violation were predicted better than patient illness or patient/family refusal. Models showed good cross-campus generalizability (AUC: 0.725/0.735, when training on one site and testing on the other). To synthesize a human-oriented conceptualization of pediatric surgery cancellation, an iterative step-forward approach was applied to identify key predictors which may inform the design of future preventive interventions. Conclusions: Our study demonstrated the capacity of machine learning models for predicting pediatric patients at risk of last-minute surgery cancellation and providing useful insight into root causes of cancellation. The approach offers the promise of targeted interventions to significantly decrease both healthcare costs and also families’ negative experiences.
Keywords: Pediatric surgery cancellation | Quality improvement | Predictive modeling | Machine learning
مقاله انگلیسی
9 The Application of Machine Learning to Quality Improvement Through the Lens of the Radiology Value Network
کاربرد یادگیری ماشین برای بهبود کیفیت از طریق لنز شبکه ارزش رادیولوژی-2019
Recent advances in machine learning and artificial intelligence offer promising applications to radiology quality improvement initiatives as they relate to the radiology value network. Coordination within the interlocking web of systems, events, and stakeholders in the radiology value network may be mitigated though standardization, automation, and a focus on workflow efficiency. In this article the authors present applications of these various strategies via use cases for quality improvement projects at different points in the radiology value network. In addition, the authors discuss opportunities for machine-learning applications in data aggregation as opposed to traditional applications in data extraction.
Key Words: Machine learning | artificial intelligence | radiology quality improvement | radiology value network | data aggregation
مقاله انگلیسی
10 Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children’s surgery
استخراج داده های خاص و متنی از بیمار با فناوری های یادگیری ماشین برای پیش بینی لغو جراحی کودکان-2019
Background: Last-minute surgery cancellation represents a major wastage of resources and can cause significant inconvenience to patients. Our objectives in this study were: 1) To develop predictive models of last-minute surgery cancellation, utilizing machine learning technologies, from patient-specific and contextual data from two distinct pediatric surgical sites of a single institution; and 2) to identify specific key predictors that impact children’s risk of day-of-surgery cancellation. Methods and findings: We extracted five-year datasets (2012–2017) from the Electronic Health Record at Cincinnati Children’s Hospital Medical Center. By leveraging patient-specific information and contextual data, machine learning classifiers were developed to predict all patient-related cancellations and the most frequent four cancellation causes individually (patient illness, “no show,” NPO violation and refusal to undergo surgery by either patient or family). Model performance was evaluated by the area under the receiver operating characteristic curve (AUC) using ten-fold cross-validation. The best performance for predicting all-cause surgery cancellation was generated by gradient-boosted logistic regression models, with AUC 0.781 (95% CI: [0.764,0.797]) and 0.740 (95% CI: [0.726,0.771]) for the two campuses. Of the four most frequent individual causes of cancellation, “no show” and NPO violation were predicted better than patient illness or patient/family refusal. Models showed good cross-campus generalizability (AUC: 0.725/0.735, when training on one site and testing on the other). To synthesize a human-oriented conceptualization of pediatric surgery cancellation, an iterative step-forward approach was applied to identify key predictors which may inform the design of future preventive interventions. Conclusions: Our study demonstrated the capacity of machine learning models for predicting pediatric patients at risk of last-minute surgery cancellation and providing useful insight into root causes of cancellation. The approach offers the promise of targeted interventions to significantly decrease both healthcare costs and also families’ negative experiences.
Keywords: Pediatric surgery cancellation | Quality improvement | Predictive modeling | Machine learning
مقاله انگلیسی
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