Designing Intelligent Healthcare Operations

Author(s):  
Nilmini Wickramasinghe ◽  
Christian Guttmann ◽  
Jonathan Schaffer
Author(s):  
Inzamam Mashood Nasir ◽  
Muhammad Rashid ◽  
Jamal Hussain Shah ◽  
Muhammad Sharif ◽  
Muhammad Yahiya Haider Awan ◽  
...  

Background: Breast cancer is considered as the most perilous sickness among females worldwide and the ratio of new cases is expanding yearly. Many researchers have proposed efficient algorithms to diagnose breast cancer at early stages, which have increased the efficiency and performance by utilizing the learned features of gold standard histopathological images. Objective: Most of these systems have either used traditional handcrafted features or deep features which had a lot of noise and redundancy, which ultimately decrease the performance of the system. Methods: A hybrid approach is proposed by fusing and optimizing the properties of handcrafted and deep features to classify the breast cancer images. HOG and LBP features are serially fused with pretrained models VGG19 and InceptionV3. PCR and ICR are used to evaluate the classification performance of proposed method. Results: The method concentrates on histopathological images to classify the breast cancer. The performance is compared with state-of-the-art techniques, where an overall patient-level accuracy of 97.2% and image-level accuracy of 96.7% is recorded. Conclusion: The proposed hybrid method achieves the best performance as compared to previous methods and it can be used for the intelligent healthcare systems and early breast cancer detection.


2019 ◽  
pp. 1-17
Author(s):  
Erika Lokatt ◽  
Charlotte Holgersson ◽  
Monica Lindgren ◽  
Johann Packendorff ◽  
Louise Hagander

Abstract In this article we develop a theoretical perspective of how professional identities in multi-professional organisational settings are co-constructed in daily interactions. The research reported here is located in a healthcare context where overlapping knowledge bases, unclear divisions of responsibilities, and an increased managerialist emphasis on teamwork make interprofessional boundaries in healthcare operations more complex and blurred than ever. We thereby build on a research tradition that recognises the healthcare sector as a negotiated order, specifically studying how professional identities are invoked, constructed, and re-constructed in everyday work interactions. The perspective is employed in an analysis of qualitative data from interviews and participant observation at a large Swedish hospital, in which we find three main processes in the construction of space of action: hierarchical, inclusive, and pseudo-inclusive. In most of the interactions, existing inter-professional divides and power relations are sustained, preventing developments towards integrated interprofessional teamwork.


2014 ◽  
Vol 18 (12) ◽  
pp. 2577-2586 ◽  
Author(s):  
Ilsun You ◽  
Junho Choi ◽  
Chang Choi ◽  
Pankoo Kim

Author(s):  
Kanix Wang ◽  
Walid Hussain ◽  
John R. Birge ◽  
Michael D. Schreiber ◽  
Daniel Adelman

Having an interpretable, dynamic length-of-stay model can help hospital administrators and clinicians make better decisions and improve the quality of care. The widespread implementation of electronic medical record (EMR) systems has enabled hospitals to collect massive amounts of health data. However, how to integrate this deluge of data into healthcare operations remains unclear. We propose a framework grounded in established clinical knowledge to model patients’ lengths of stay. In particular, we impose expert knowledge when grouping raw clinical data into medically meaningful variables that summarize patients’ health trajectories. We use dynamic, predictive models to output patients’ remaining lengths of stay, future discharges, and census probability distributions based on their health trajectories up to the current stay. Evaluated with large-scale EMR data, the dynamic model significantly improves predictive power over the performance of any model in previous literature and remains medically interpretable. Summary of Contribution: The widespread implementation of electronic health systems has created opportunities and challenges to best utilize mounting clinical data for healthcare operations. In this study, we propose a new approach that integrates clinical analysis in generating variables and implementations of computational methods. This approach allows our model to remain interpretable to the medical professionals while being accurate. We believe our study has broader relevance to researchers and practitioners of healthcare operations.


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