Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine

Human health big data evaluation based on FPGA processor and big data decision algorithm

As the uptake of health information technologies increased, most healthcare organizations have become producers of big data. A growing number of hospitals are investing in the development of big data analytics (BDA) capabilities. If the promises associated with these capabilities are high, how hospitals create value from it remains unclear. The present study undertakes a scoping review of existing research on BDA use in hospitals to describe the path from BDA capabilities (BDAC) to value and its associated challenges.This scoping review was conducted following Arksey and O'Malley's 5 stages framework. A systematic search strategy was adopted to identify relevant articles in Scopus and Web of Science. Data charting and extraction were performed following an analytical framework that builds on the resource-based view of the firm to describe the path from BDA capabilities to value in hospitals.Of 1,478 articles identified, 94 were included. Most of them are experimental research (n=69) published in medical (n=66) or computer science journals (n=28). The main value targets associated with the use of BDA are improving the quality of decision-making (n=56) and driving innovation (n=52) which apply mainly to care (n=67) and administrative (n=48) activities. To reach these targets, hospitals need to adequately combine BDA capabilities and value creation mechanisms (VCM) to enable knowledge generation and drive its assimilation. Benefits are endpoints of the value creation process. They are expected in all articles but realized in a few instances only (n=19).This review confirms the value creation potential of BDA solutions in hospitals. It also shows the organizational challenges that prevent hospitals from generating actual benefits from BDAC-building efforts. The configuring of strategies, technologies and organizational capabilities underlying the development of value-creating BDA solutions should become a priority area for research, with focus on the mechanisms that can drive the alignment of BDA and organizational strategies, and the development of organizational capabilities to support knowledge generation and assimilation. 

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The path from big data analytics capabilities to value in hospitals: a scoping review

This paper proposed a new approach to design medical imaging-sharing service network based on professional medical imaging center (PMIC). PMIC is famous for advanced imaging modalities and expert resources. The network connects clinics, hospitals and PMICs to provide collaborative diagnosis, consultation, mobile expert consulting and medical imaging artificial intelligence (AI) analysis services through Internet. It allows patients to be registered in hospital and examined in PMIC. It provides to schedule and view patients exam from mobile devices. It also provides AI analysis for some specific kinds of medical images such as carotid plaque and mammary cancer, to help doctors get accurate conclusions. The network is flexible to use three layers architecture with secure messaging and data communication: data source, service cloud and service provider. It has been deployed in Guangzhou Huyun Medical Imaging Diagnosis Center since July 2018 to provide services for the First People’s Hospital of Guangzhou.

A new medical imaging sharing service network based on professional medical imaging center

This paper proposed a new generation PACS (Picture Archiving and Communication System) based on artificial intelligent visualization. It is developed from our GRIDPACS (patent number: US8805890), which combined with IHE XDS-I profile, to implement images communication, storage and display. It also uses 3D anatomical visualization model to extract multi-source data from PACS/RIS/HIS/EMR, to express patient disease location, size and severity, which was introduced as Visual Patent (VP) at previous SPIE Medical Imaging (SPIE MI 2018). It can integrate the training model of AI Imaging Diagnosis, to mark the focus and display the disease trends. The system not only has the original PACS functions, but also realizes the man-machine interaction (images and electronic medical record information between radiologist and patient) in a personalized, fast, comprehensive, quantitative and easy-to-understand way. It can be used in various medical institutions, image diagnostic centers, and imaging cloud, to support the healthy development of imaging technology in China.

Design and implementation of a new generation of PACS based on artificial intelligent visualization

Objective: an innovative concept and method is introduced to use a 3-D anatomical graphic pattern called visual patient (VP) visually to index, represent, and render the medical diagnostic records (MDRs) of a patient, so that a doctor can quickly learn the current and historical medical status of the patient by manipulating VP. The MDRs can be imaging diagnostic reports and DICOM images, laboratory reports and clinical summaries which can have clinical information relating to medical status of human organs or body parts. Methods: the concept and method included three steps. First, a VP data model called visual index object (VIO) and a VP graphic model called visual anatomic object (VAO) were introduced. Second, a series of processing methods of parsing and extracting key information from MDRs were used to fill the attributes of the VIO model of a patient. Third, a VP system (VPS) was designed to map VIO to VAO, to create a VP instance for each patient. Results: a prototype VPS has been implemented in a simulated hospital PACS/RIS integrated environment. Two evaluation results showed that more than 70% participating radiologists would like to use the VPS in their radiological imaging tasks, and the efficiency of using VPS to review the tested patients’ MDRs was 2.24 times higher than that of using PACS/RIS, while the average accuracy Pac > by using PACS/RIS was better than that by using VPS; however, this difference was only about 4%. Conclusion: the developed VPS can show the medical status of patient organs/sub-organs with 3-D anatomical graphic pattern and will be welcomed by radiologists with better efficiency in reviewing the patients’ MDRs and with acceptable accuracy. Significance: the VP introduces a new way for medical professionals to access and interact with a huge amount of patient records with better efficiency in the big data era.

Three-Dimensional Visual Patient Based on Electronic Medical Diagnostic Records

In this presentation, we presented a new approach to design cloud-based image sharing network for collaborative imaging diagnosis and consultation through Internet, which can enable radiologists, specialists and physicians locating in different sites collaboratively and interactively to do imaging diagnosis or consultation for difficult or emergency cases. The designed network combined a regional RIS, grid-based image distribution management, an integrated video conferencing system and multi-platform interactive image display devices together with secured messaging and data communication. There are three kinds of components in the network: edge server, grid-based imaging documents registry and repository, and multi-platform display devices. This network has been deployed in a public cloud platform of Alibaba through Internet since March 2017 and used for small lung nodule or early staging lung cancer diagnosis services between Radiology departments of Huadong hospital in Shanghai and the First Hospital of Jiaxing in Zhejiang Province.

Cloud-based image sharing network for collaborative imaging diagnosis and consultation

The benign and malignant differential diagnosis of small pulmonary nodules (diameter < 20 mm) found in lung CT images is big challenges for most of radiologists. Here, we presented our preliminary study of benign and malignant differentiation of small pulmonary nodules in lung CT images by using deep learning Convolutional Neural Network (CNN). The 921 cases with small benign and malignant pulmonary nodules confirmed by pathology were collected from three data sources and were used to train and validate the CNN. The preliminary results of AUCs of ROC curves for differentiating benign and malignant pulmonary small nodules with various types and sizes of solid, semi-solid and ground glass nodules were presented and discussed.

Preliminary study of benign and malignant differentiation of small pulmonary nodules in lung CT images by using deep learning convolutional neural network

Yiping Gu

Papers

Three-Dimensional Visual Patient Based on Electronic Medical Diagnostic Records

Cloud-based image sharing network for collaborative imaging diagnosis and consultation

Design and implementation of a new generation of PACS based on artificial intelligent visualization

Preliminary study of benign and malignant differentiation of small pulmonary nodules in lung CT images by using deep learning convolutional neural network