Indicators of Quality of Care in Individuals With Traumatic Spinal Cord Injury: A Scoping Review

Study Design: Scoping review. Objectives: To identify a practical and reproducible approach to organize Quality of Care Indicators (QoCI) in individuals with traumatic spinal cord injury (TSCI). Methods: A comprehensive literature review was conducted in the Cochrane Central Register of Controlled Trials (CENTRAL) (Date: May 2018), MEDLINE (1946 to May 2018), and EMBASE (1974 to May 2018). Two independent reviewers screened 6092 records and included 262 full texts, among which 60 studies were included for qualitative analysis. We included studies, with no language restriction, containing at least 1 quality of care indicator for individuals with traumatic spinal cord injury. Each potential indicator was evaluated in an online, focused group discussion to define its categorization (healthcare system structure, medical process, and individuals with Traumatic Spinal Cord Injury related outcomes), definition, survey options, and scale. Results: A total of 87 indicators were identified from 60 studies screened using our eligibility criteria. We defined each indicator. Out of 87 indicators, 37 appraised the healthcare system structure, 30 evaluated medical processes, and 20 included individuals with TSCI related outcomes. The healthcare system structure included the impact of the cost of hospitalization and rehabilitation, as well as staff and patient perception of treatment. The medical processes included targeting physical activities for improvement of health-related outcomes and complications. Changes in motor score, functional independence, and readmission rates were reported as individuals with TSCI-related outcomes indicators. Conclusion: Indicators of quality of care in the management of individuals with TSCI are important for health policy strategists to standardize healthcare assessment, for clinicians to improve care, and for data collection efforts including registries. © The Author(s) 2021

A PSO-based model to increase the accuracy of software development effort estimation

Development effort is one of the most important metrics that must be estimated in order to design the plan of a project. The uncertainty and complexity of software projects make the process of effort estimation dif?cult and ambiguous. Analogy-based estimation (ABE) is the most common method in this area because it is quite straightforward and practical, relying on comparison between new projects and completed projects to estimate the development effort. Despite many advantages, ABE is unable to produce accurate estimates when the importance level of project features is not the same or the relationship among features is dif?cult to determine. In such situations, ef?cient feature weighting can be a solution to improve the performance of ABE. This paper proposes a hybrid estimation model based on a combination of a particle swarm optimization (PSO) algorithm and ABE to increase the accuracy of software development effort estimation. This combination leads to accurate identi?cation of projects that are similar, based on optimizing the performance of the similarity function in ABE. A framework is presented in which the appropriate weights are allocated to project features so that the most accurate estimates are achieved. The suggested model is ?exible enough to be used in different datasets including categorical and non-categorical project features. Three real data sets are employed to evaluate the proposed model, and the results are compared with other estimation models. The promising results show that a combination of PSO and ABE could signi?cantly improve the performance of existing estimation models