Implementation requirements for patient discharge planning in health system: a qualitative study in Iran

Background: Effective discharge planning plays a vital role in care continuity and integrated care. Identifying and providing infrastructures for discharge planning can reduce avoidable hospital readmissions and finally lead to improvement of quality of care. The current study aimed to identify the requirements of discharge planning from the perspective of professionals in the health system of Iran.Methods: For the purposes of this qualitative study, semistructured interviews and sessions of focus group discussions with experts in the field were conducted. The data were analyzed using a thematic and framework analyses method. The study population was 51 participants including health policy makers, hospital andhealth managers, faculty members, nurses, practitioners, community medicine specialists and other professionals of the Ministry of Health andMedical Education (MOHME).Results: According to the control knobs (health reforms levels), recruitments of effective hospital discharge planning were divided into four areas, behavior (of policy makers, service providers, recipients services), organization, payment and financing and regulation (themes), in which there were 3, 7, 2 and 3 sub-themes respectively. Based on the findings of the interviews, they were categorized into the following main themes: behavior (policy makers, providers and patients), organizational change, financing and payment system and rules and regulations.Conclusions: According to the results of the present study, it appears to be essential for health managers and policy makers to pay attention to essential requirements of effective discharge planning.Keywords: Hospital discharge planning, health system,effective discharge plannin

Medical Tourism Attraction of Tehran Hospitals

Introduction: Today the market of medical tourism is growing as one of the competitive and profitable industries in the world. The aim of this study was to determine medical tourist attraction in Tehran hospitals. Methods: This is a descriptive study which was carried out in 8 hospitals of Tehran in 2012. 195 people from the managing boards of these hospitals participated in the study. A questionnaire was designed to gather data. The validity of the questionnaire was confirmed by professors and administrators and reliability was calculated 80% by Cronbach's alpha. The data was analyzed using SPSS. Findings: The total amount of medical tourist attractions in Tehran hospitals is moderate (51%) and also average number of foreign patients admitted to hospitals and average income for hospitals is also moderate. Conclusion: According to the results it seems media advertising is the most effective in attracting medical tourists. Furthermore, the advertisement of the capabilities of hospitals alongside marketing could help attract more medical tourists

CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework

Point-of-Interest (POI) recommendation systems have gained popularity for their unique ability to suggest geographical destinations, with the incorporation of contextual information such as time, location, and user-item interaction. Existing recommendation frameworks lack the contextual fusion required for POI systems. This paper presents CAPRI, a novel POI recommendation framework that effectively integrates context-aware models, such as GeoSoCa, LORE, and USG, and introduces a novel strategy for the efficient merging of contextual information. CAPRI integrates an evaluation module that expands the evaluation scope beyond accuracy to include novelty, personalization, diversity, and fairness. With an aim to establish a new industry standard for reproducible results in the realm of POI recommendation systems, we have made CAPRI openly accessible on GitHub, facilitating easy access and contribution to the continued development and refinement of this innovative framework. 19. Industry, innovation and infrastructur

Vision-based Situational Graphs Generating Optimizable 3D Scene Representations

3D scene graphs offer a more efficient representation of the environment by hierarchically organizing diverse semantic entities and the topological relationships among them. Fiducial markers, on the other hand, offer a valuable mechanism for encoding comprehensive information pertaining to environments and the objects within them. In the context of Visual SLAM (VSLAM), especially when the reconstructed maps are enriched with practical semantic information, these markers have the potential to enhance the map by augmenting valuable semantic information and fostering meaningful connections among the semantic objects. In this regard, this paper exploits the potential of fiducial markers to incorporate a VSLAM framework with hierarchical representations that generates optimizable multi-layered vision-based situational graphs. The framework comprises a conventional VSLAM system with low-level feature tracking and mapping capabilities bolstered by the incorporation of a fiducial marker map. The fiducial markers aid in identifying walls and doors in the environment, subsequently establishing meaningful associations with high-level entities, including corridors and rooms. Experimental results are conducted on a real-world dataset collected using various legged robots and benchmarked against a Light Detection And Ranging (LiDAR)-based framework (S-Graphs) as the ground truth. Consequently, our framework not only excels in crafting a richer, multi-layered hierarchical map of the environment but also shows enhancement in robot pose accuracy when contrasted with state-of-the-art methodologies.Comment: 7 pages, 6 figures, 2 table

Towards Localizing Structural Elements: Merging Geometrical Detection with Semantic Verification in RGB-D Data

peer reviewedRGB-D cameras supply rich and dense visual and spatial information for various robotics tasks such as scene understanding, map reconstruction, and localization. Integrating depth and visual information can aid robots in localization and element mapping, advancing applications like 3D scene graph generation and Visual Simultaneous Localization and Mapping (VSLAM). While point cloud data containing such information is primarily used for enhanced scene understanding, exploiting their potential to capture and represent rich semantic information has yet to be adequately targeted. This paper presents a real-time pipeline for localizing building components, including wall and ground surfaces, by integrating geometric calculations for pure 3D plane detection followed by validating their semantic category using point cloud data from RGB-D cameras. It has a parallel multi-thread architecture to precisely estimate poses and equations of all the planes detected in the environment, filters the ones forming the map structure using a panoptic segmentation validation, and keeps only the validated building components. Incorporating the proposed method into a VSLAM framework confirmed that constraining the map with the detected environment-driven semantic elements can improve scene understanding and map reconstruction accuracy. It can also ensure (re-)association of these detected components into a unified 3D scene graph, bridging the gap between geometric accuracy and semantic understanding. Additionally, the pipeline allows for the detection of potential higher-level structural entities, such as rooms, by identifying the relationships between building components based on their layout.9. Industry, innovation and infrastructur

UAV-Assisted Visual SLAM Generating Reconstructed 3D Scene Graphs in GPS-Denied Environments

peer reviewedAerial robots play a vital role in various applications where situational awareness concerning the environment is a fundamental demand. As one such use case, drones in Global Positioning System (GPS)-denied environments require equipping with different sensors that provide reliable sensing results while performing pose estimation and localization. This paper aims to reconstruct maps of indoor environments and generate 3D scene graphs for a high-level representation using a camera mounted on a drone. Accordingly, an aerial robot equipped with a companion computer and an RGB-D camera was employed to be integrated with a Visual Simultaneous Localization and Mapping (VSLAM) framework proposed by the authors. To enhance situational awareness while reconstructing maps, various structural elements, i.e., doors and walls, were labeled with printed fiducial markers, and a dictionary of their topological relations was fed to the system. The system detects markers and reconstructs the map of the indoor areas enriched with higher-level semantic entities, including corridors and rooms. In this regard, integrating VSLAM into the employed drone provides an end-to-end robot application for GPS-denied environments that generates multi-layered vision-based situational graphs containing hierarchical representations. To demonstrate the system's practicality, various real-world condition experiments have been conducted in indoor scenarios with dissimilar structural layouts. Evaluations show the proposed drone application can perform adequately w.r.t. the ground-truth data and its baseline.9. Industry, innovation and infrastructur

Late Breaking Results on Visual S-Graphs for Robust Semantic Scene Understanding and Hierarchical Representation

peer reviewedThe current work introduces an improved version of our previous marker-based approach presented in IROS'23 with a broader coverage of sensors and semantic concepts support built upon ORB-SLAM 3.0. It employs pose information of the markers placed on walls and doorways to estimate the 3D equation of their planes, while the markers only keep their correspondence information of the semantic entities. Compared to its baseline and other similar works, the proposed approach produces higher-accuracy reconstructed maps with semantic entities, including walls, rooms, corridors, and doorways.9. Industry, innovation and infrastructur

From SLAM to Situational Awareness: Challenges and Survey

The knowledge that an intelligent and autonomous mobile robot has and is able to acquire of itself and the environment, namely the situation, limits its reasoning, decision-making, and execution skills to efficiently and safely perform complex missions. Situational awareness is a basic capability of humans that has been deeply studied in fields like Psychology, Military, Aerospace, Education, etc., but it has barely been considered in robotics, which has focused on ideas such as sensing, perception, sensor fusion, state estimation, localization and mapping, spatial AI, etc. In our research, we connected the broad multidisciplinary existing knowledge on situational awareness with its counterpart in mobile robotics. In this paper, we survey the state-of-the-art robotics algorithms, we analyze the situational awareness aspects that have been covered by them, and we discuss their missing points. We found out that the existing robotics algorithms are still missing manifold important aspects of situational awareness. As a consequence, we conclude that these missing features are limiting the performance of robotic situational awareness, and further research is needed to overcome this challenge. We see this as an opportunity, and provide our vision for future research on robotic situational awareness.Comment: 15 pages, 8 figure

Hierarchical Visual SLAM based on Fiducial Markers

Fiducial markers can encode rich information about the environment and aid Visual SLAM (VSLAM) approaches in reconstructing maps with practical semantic information. Current marker-based VSLAM approaches mainly utilize markers for improving feature detection in low-feature environments and/or incorporating loop closure constraints, generating only low-level geometric maps of the environment prone to inaccuracies in complex environments. To bridge this gap, this paper presents a VSLAM approach utilizing a monocular camera and fiducial markers to generate hierarchical representations of the environment while improving the camera pose estimate. The proposed approach detects semantic entities from the surroundings, including walls, corridors, and rooms encoded within markers, and appropriately adds topological constraints among them. Experimental results on a real-world dataset demonstrate that the proposed approach outperforms a traditional marker-based VSLAM baseline in terms of accuracy, despite adding new constraints while creating enhanced map representations. Furthermore, it shows satisfactory results when comparing the reconstructed map quality to the one reconstructed using a LiDAR SLAM approach