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نتیجه جستجو - بهبود کیفیت

تعداد مقالات یافته شده: 54
ردیف عنوان نوع
1 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
2 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
3 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
4 Combining the roles of evaluator and facilitator: Assessing societal impacts of transdisciplinary research while building capacities to improve its quality
ترکیب نقشهای ارزیاب و تسهیلگر: ارزیابی تأثیرات اجتماعی تحقیقات بین رشته ای ضمن ساختن ظرفیت ها برای بهبود کیفیت آن-2020
Participation of relevant stakeholders, knowledge integration, responsive and emergent design and effective boundary management are four key features of transdisciplinary research (TDR). These features pose significant challenges to both undertaking TDR and evaluating its societal impact. We argue that TDR’s context specificity and complexity warrant an evaluation approach that supports the coordinating team in developing these key features. In light of this, this article aims to reconcile two distinct foci of TDR evaluation, namely supporting transdisciplinary capacity building and impact evaluation. We share the results from a combined approach in which the authors acted both as facilitators and evaluators of a TDR project, to conduct an embedded, formative evaluation. Our findings show that the approach allowed for better access to the participants and sensitivity to their perspectives on impact, and for enhanced understanding of complex internal and external project dynamics and how these shaped the project. This resulted in a meaningful assessment of TDR’s societal impacts and enabled attributing these to specific process elements. Moreover, the approach supported the coordinating TDR team’s capacities for developing key TDR features. Four TDR capacities were identified: building TDR ownership, openness and transparency for integrating divergent TDR needs, purposeful responsiveness to emergent TDR needs and navigating institutional realities and TDR ambitions. The approach presented may serve as stepping stone for the TDR community to further the conversation on (the impact of) inclusive, reflexive and responsive research
Keywords: Transdisciplinary research | Evaluation | Impact assessment | Capacity building | Researcher roles | Research quality
مقاله انگلیسی
5 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
6 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
7 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
8 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
9 کیفیت رابطه به عنوان پیش بینی کننده وفاداری مشتری B2B در بخش داروسازی: شاهدی از کشور اردن
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 17 - تعداد صفحات فایل doc فارسی: 24
این مقاله با هدف بررسی تاثیر پذیری ابعاد کیفیت رابطه ( به عنوان مثال اعتماد، رضایت و تعهد) بر جنبه های وفاداری یعنی وفاداری نگرشی و وفاداری رفتاری است. روش نظرسنجی کمی برای دستیابی به اهداف مطالعه مورد استفاده قرار گرفت. علاوه بر این، یک تکنیک نمونه گیری آسان برای انتخاب نمونه پزشکان در حال کار در بخش مراقبت های بهداشتی دولتی در کشور اردن به کار گرفته شد. در مجموع 500 پرسشنامه توزیع شد که408 پرسشنامه در تجزیه و تحلیل آماری مورد استفاده قرار گرفت. داده ها بوسیله مدلسازی معادلات ساختاری اعمال شده برای تست مدل مطالعه مورد تجزیه و تحلیل قرار گرفت و فرضیه ها نیز از نظر کمی مورد تست و از نظر کیفی مورد بحث و بررسی قرار گرفتند. نتایج نشان داد که هر دو جنبه وفاداری مشتری ( یعنی وفاداری رفتاری و وفاداری نگرشی) به صورت مثبت بر ابعاد کلی کیفیت رابطه ( یعنی اعتماد ، رضایت و تعهد) تاثیر می گذارند و یک رهنمود را برای شرکت های داروسازی در کشور اردن در راستای تمرکز بر بهبود کیفیت روابط بین نمایندگان دارویی و پزشکی شان و پزشکان فراهم می کند و این ناشی از اهمیت چنین عواملی در بهبود وفاداری مشتری است که در مدیریت مثبت و موثر مشتریان شان و افزایش فرصت های کسب و کاری در آینده منعکس می شود.
کلمات کلیدی: وفاداری نگرشی | B2B | وفاداری رفتاری | کیفیت رابطه | SEM
مقاله ترجمه شده
10 چارچوب بازیابی کیفیت تصویر برای افزایش کنتراست تصاویر ماهواره ای سنجش از دور
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 21 - تعداد صفحات فایل doc فارسی: 39
پژوهش ها در حوزه ی تصاویر ماهواره ای سنجش از دور عمدتاً بر افزایش کنتراست و حذف نویز از تمرکز دارند، این بر قابلیت درک داده ها و وضوح آنها تأثیر می گذارد. در نتیجه، همیشه با چالشی در پردازش تصاویر ماهواره ای سنجش از دور به منظور دستیابی به تصاویری با کیفیت بالاتر و افزایش قابلیت دیداری و تصاویری با کمترین میزان آرتیفکت برای ارتقای ارزش کاربردی آنها مواجهیم. در این مقاله، یک چارچوب مؤثر بهبود کیفیت پیشنهاد می شود، که عمدتاً بر افزایش کنتراست تصاویر ماهواره ای سنجش از دور تمرکز دارد. چندین تصویر ماهواره ای سنجش از دور در تأیید اثربخشی روش پیشنهادی در مقایسه با دیگر روش های موجود ارتقای سنجش از دور ارزیابی شدند، و نتایج کمّی آنها با NIQMC (معیار کیفیت تصویر بدون مرجع برای انحراف کنتراست)، BIQME (معیار کور کیفیت تصویر برای تصاویر بهبودیافته)، MICHELSON (کنتراست مایکلسون)، DE انتروپی گسسته)، EME (معیار بهبود) و PIXDIST (فاصله ی پیکسل) و همراه با مقایسه ی نتایج کیفی تأیید شد. نتایج نشان می دهد که بهبود بصری به دست آمده با استفاده از روش پیشنهادی بر سایر روش های بهبود بصری برتری دارد. نهایتاً نتایج شبیه سازی مشخص نمود که روش پیشنهادی برای تصاویر ماهواره ای سنجش از دور، مؤثر و کارامد است.
کلیدواژه: سنجش از دور | کنتراست | بازیابی | کیفیت
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