Active Dataset Generation for Meta-learning System Quality Improvement

2019 ◽  
pp. 394-401 ◽  
Author(s):  
Alexey Zabashta ◽  
Andrey Filchenkov
Keyword(s):  
Quality Improvement ◽  
Learning System ◽  
System Quality ◽  
Meta Learning

The Institute for Healthcare Improvement–NeuroPoint Alliance collaboration to decrease length of stay and readmission after lumbar spine fusion: using national registries to design quality improvement protocols

2020 ◽  
Vol 33 (6) ◽  
pp. 812-821
Author(s):  
Scott L. Zuckerman ◽  
Clinton J. Devin ◽  
Vincent Rossi ◽  
Silky Chotai ◽  
E. Hunter Dyer ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Length Of Stay ◽  
Discharge Planning ◽  
Lumbar Fusion ◽  
Nonparametric Tests ◽  
Learning System ◽  
Design Quality ◽  
Implementation Phase ◽  
Healthcare Improvement ◽  
The Impact

OBJECTIVENational databases collect large amounts of clinical information, yet application of these data can be challenging. The authors present the NeuroPoint Alliance and Institute for Healthcare Improvement (NPA-IHI) program as a novel attempt to create a quality improvement (QI) tool informed through registry data to improve the quality of care delivered. Reducing the length of stay (LOS) and readmission after elective lumbar fusion was chosen as the pilot module.METHODSThe NPA-IHI program prospectively enrolled patients undergoing elective 1- to 3-level lumbar fusions across 8 institutions. A three-pronged approach was taken that included the following phases: 1) Research Phase, 2) Development Phase, and 3) Implementation Phase. Primary outcomes were LOS and readmission. From January to June 2017, a learning system was created utilizing monthly conference calls, weekly data submission, and continuous refinement of the proposed QI tool. Nonparametric tests were used to assess the impact of the QI intervention.RESULTSThe novel QI tool included the following three areas of intervention: 1) preoperative discharge assessment (location, date, and instructions), 2) inpatient changes (LOS rounding checklist, daily huddle, and pain assessments), and 3) postdischarge calls (pain, primary care follow-up, and satisfaction). A total of 209 patients were enrolled, and the most common procedure was a posterior laminectomy/fusion (60.2%). Seven patients (3.3%) were readmitted during the study period. Preoperative discharge planning was completed for 129 patients (61.7%). A shorter median LOS was seen in those with a known preoperative discharge date (67 vs 80 hours, p = 0.018) and clear discharge instructions (71 vs 81 hours, p = 0.030). Patients with a known preoperative discharge plan also reported significantly increased satisfaction (8.0 vs 7.0, p = 0.028), and patients with increased discharge readiness (scale 0–10) also reported higher satisfaction (r = 0.474, p < 0.001). Those receiving postdischarge calls (76%) had a significantly shorter LOS than those without postdischarge calls (75 vs 99 hours, p = 0.020), although no significant relationship was seen between postdischarge calls and readmission (p = 0.342).CONCLUSIONSThe NPA-IHI program showed that preoperative discharge planning and postdischarge calls have the potential to reduce LOS and improve satisfaction after elective lumbar fusion. It is our hope that neurosurgical providers can recognize how registries can be used to both develop and implement a QI tool and appreciate the importance of QI implementation as a separate process from data collection/analysis.

scholarly journals Quality indicators of dental health care in Serbia

2019 ◽  
Vol 66 (1) ◽  
pp. 36-42
Author(s):  
Svetlana Jovanović ◽  
Maja Milošević ◽  
Irena Aleksić-Hajduković ◽  
Jelena Mandić
Keyword(s):  
Health Care ◽  
Quality Improvement ◽  
Health System ◽  
Quality Indicators ◽  
Health Care Quality ◽  
Care Quality ◽  
System Quality ◽  
Quality Health Care ◽  
Quality Health

Summary Health care has witnessed considerable progresses toward quality improvement over the past two decades. More precisely, there have been global efforts aimed to improve this aspect of health care along with experts and decision-makers reaching the consensus that quality is one of the most significant dimensions and features of health system. Quality health care implies highly efficient resource use in order to meet patient’s needs in terms of prevention and treatment. Quality health care is provided in a safe way while meeting patients’ expectations and avoiding unnecessary losses. The mission of continuous improvement in quality of care is to achieve safe and reliable health care through mutual efforts of all the key supporters of health system to protect patients’ interests. A systematic approach to measuring the process of care through quality indicators (QIs) poses the greatest challenge to continuous quality improvement in health care. Quality indicators are quantitative indicators used for monitoring and evaluating quality of patient care and treatment, continuous professional development (CPD), maintaining waiting lists, patients and staff satisfaction, and patient safety.

Cost-sensitive meta-learning framework

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Samar Ali Shilbayeh ◽  
Sunil Vadera
Keyword(s):  
Machine Learning ◽  
Learning System ◽  
Classification Algorithms ◽  
Data Sets ◽  
Classification Methods ◽  
Content Type ◽  
Learning Framework ◽  
Cost Sensitive Classification ◽  
Meta Learning ◽  
Training Examples

Purpose This paper aims to describe the use of a meta-learning framework for recommending cost-sensitive classification methods with the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” Design/methodology/approach This paper describes the use of a meta-learning framework for recommending cost-sensitive classification methods for the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” The framework is based on the idea of applying machine learning techniques to discover knowledge about the performance of different machine learning algorithms. It includes components that repeatedly apply different classification methods on data sets and measures their performance. The characteristics of the data sets, combined with the algorithms and the performance provide the training examples. A decision tree algorithm is applied to the training examples to induce the knowledge, which can then be used to recommend algorithms for new data sets. The paper makes a contribution to both meta-learning and cost-sensitive machine learning approaches. Those both fields are not new, however, building a recommender that recommends the optimal case-sensitive approach for a given data problem is the contribution. The proposed solution is implemented in WEKA and evaluated by applying it on different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. The developed solution takes the misclassification cost into consideration during the learning process, which is not available in the compared project. Findings The proposed solution is implemented in WEKA and evaluated by applying it to different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. Originality/value The paper presents a major piece of new information in writing for the first time. Meta-learning work has been done before but this paper presents a new meta-learning framework that is costs sensitive.

Development and Validation of an Instrument to Measure Student Perceived E-Learning Service Quality

2019 ◽  
Vol 15 (2) ◽  
pp. 15-42 ◽  
Author(s):  
Long Pham ◽  
Kioh Kim ◽  
Bruce Walker ◽  
Thomas DeNardin ◽  
Hanh Le
Keyword(s):  
Service Quality ◽  
Relevant Literature ◽  
Support Service ◽  
Learning System ◽  
System Quality ◽  
University Administrators ◽  
Course Materials ◽  
E Learning ◽  
Development And Validation ◽  
Quality Instrument

While universities have been trying to focus their resources and attention on improving e-learning, many universities seem to be lagging behind students' increasing demands and expectations. In order to sustainably grow in an increasingly competitive e-learning environment, it is clear that universities must provide e-learning students with high quality services. To do this, universities are required to understand the attributes that e-learning students use to evaluate service quality. Unfortunately, little research on e-learning service quality has been conducted. This study developed and validated an instrument to measure student perceived e-learning service quality. Based on the relevant literature review and using responses from 1,232 e-learning students, the authors validated a three-factor e-learning service quality instrument involving e-learning system quality, e-learning instructor and course materials quality, and e-learning administrative and support service quality. Among these three factors, e-learning system quality makes the highest contribution to overall e-learning service quality, followed by e-learning instructor and course materials quality, and e-learning administrative and support service quality. This scale provides a useful measurement for researchers who wish to measure e-learning service quality and for university administrators and managers who want to enhance universities' e-learning servie quality.

Extending the expectation-confirmation model with quality and flow to explore nurses’ continued blended e-learning intention

2014 ◽  
Vol 27 (3) ◽  
pp. 230-258 ◽  
Author(s):  
Yung-Ming Cheng
Keyword(s):  
Information Quality ◽  
Support Service ◽  
Learning System ◽  
System Quality ◽  
Continuance Intention ◽  
Is Success ◽  
Content Type ◽  
Delone And Mclean ◽  
Success Model ◽  
E Learning

Purpose – The purpose of this paper is to propose a hybrid model based on the expectation-confirmation model (ECM), flow theory, and updated DeLone and McLean information system (IS) success model to examine whether quality factors as the antecedents to nurse beliefs affected nurses’ intention to continue using the blended electronic learning (e-learning) system. Design/methodology/approach – Sample data for this study were collected from nurses at five hospitals in Taiwan. A total of 500 questionnaires were distributed, 396 (79.2 percent) questionnaires were returned. Consequently, 378 usable questionnaires were analyzed in this study, with a usable response rate of 75.6 percent. Collected data were analyzed using structural equation modeling. Findings – Information quality, system quality, support service quality, and instructor quality contribute significantly to perceived usefulness (PU), confirmation, and flow, which together explain nurses’ satisfaction with the usage of the blended e-learning system, and this in turn leads to their continued system usage intention. Originality/value – First, the application of the ECM with the view of updated DeLone and McLean IS success model reveals deep insights into quality evaluation (including information quality, system quality, and support service quality) in the field of nurses’ e-learning continuance intention. Especially, this study additionally contributes to the identification of instructor quality that may lead to nurses’ continued blended e-learning usage intention. Next, the empirical evidence on capturing both extrinsic motivator (i.e. PU) and intrinsic motivator (i.e. flow) for completely explaining quality antecedents of nurses’ blended e-learning continuance intention is well documented.

scholarly journals Engaging Learners in Health System Quality Improvement Efforts

2017 ◽  
Vol 92 (5) ◽  
pp. 593-597 ◽  
Author(s):  
Karnjit Johl ◽  
R. Kevin Grigsby
Keyword(s):  
Quality Improvement ◽  
Health System ◽  
System Quality ◽  
Health System Quality
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