Health data in cloud environments

The process of provisioning healthcare involves massive healthcare data which exists in different forms on disparate data sources and in different formats. Consequently, health information systems encounter interoperability problems at many levels. Integrating these disparate systems requires the support at all levels of a very expensive infrastructures. Cloud computing dramatically reduces the expense and complexity of managing IT systems. Business customers do not need to invest in their own costly IT infrastructure, but can delegate and deploy their services effectively to Cloud vendors and service providers. It is inevitable that electronic health records (EHRs) and healthcare-related services will be deployed on cloud platforms to reduce the cost and complexity of handling and integrating medical records while improving efficiency and accuracy. The paper presents a review of EHR including definitions, EHR file formats, structures leading to the discussion of interoperability and security issues. The paper also presents challenges that have to be addressed for realizing Cloudbased healthcare systems: data protection and big health data management. Finally, the paper presents an active data model for housing and protecting EHRs in a Cloud environment

Brining Point-of-Care Ultrasound to a Rural Primary Care Clinic

Background and Objectives: In the rural primary care setting, the process for outpatient ultrasound testing has multiple steps, all of which provide opportunities for delay in testing and diagnosis. Point-of-care ultrasound (POCUS) is a solution that allows the physician to perform same-day ultrasound in the primary care clinic. The objective of this program development project was to implement POCUS in a rural primary care clinic to reduce time to testing and diagnosis for those patients requiring ultrasound testing. Methods: A 3-month chart audit was conducted to examine the average length of time required to complete ultrasound testing in the outpatient radiology department. Audit results were compared to POCUS testing. A 6-question Likert scale was developed to assess patient satisfaction with the POCUS process. The Donabedian Model and Promoting Action on Research Implementation in Health Services framework were used to examine and implement POCUS. Results: Chart audits revealed 34 ultrasounds that were ordered. The mean number of days from the time the ultrasound was ordered to the time it was uploaded into the electronic medical record was 27. One POCUS was performed during the implementation period. It was done same- day and its results eliminated unnecessary specialty referral. Conclusion: POCUS is a valid and reliable tool that can be used by the primary care provider to assist in diagnosis and may significantly reduce time to testing and time to diagnosis. It may also have a unique role in rural settings where resources may be limited

Browser-based Data Annotation, Active Learning, and Real-Time Distribution of Artificial Intelligence Models: From Tumor Tissue Microarrays to COVID-19 Radiology.

BACKGROUND: Artificial intelligence (AI) is fast becoming the tool of choice for scalable and reliable analysis of medical images. However, constraints in sharing medical data outside the institutional or geographical space, as well as difficulties in getting AI models and modeling platforms to work across different environments, have led to a "reproducibility crisis" in digital medicine. METHODS: This study details the implementation of a web platform that can be used to mitigate these challenges by orchestrating a digital pathology AI pipeline, from raw data to model inference, entirely on the local machine. We discuss how this federated platform provides governed access to data by consuming the Application Program Interfaces exposed by cloud storage services, allows the addition of user-defined annotations, facilitates active learning for training models iteratively, and provides model inference computed directly in the web browser at practically zero cost. The latter is of particular relevance to clinical workflows because the code, including the AI model, travels to the user's data, which stays private to the governance domain where it was acquired. RESULTS: We demonstrate that the web browser can be a means of democratizing AI and advancing data socialization in medical imaging backed by consumer-facing cloud infrastructure such as Box.com. As a case study, we test the accompanying platform end-to-end on a large dataset of digital breast cancer tissue microarray core images. We also showcase how it can be applied in contexts separate from digital pathology by applying it to a radiology dataset containing COVID-19 computed tomography images. CONCLUSIONS: The platform described in this report resolves the challenges to the findable, accessible, interoperable, reusable stewardship of data and AI models by integrating with cloud storage to maintain user-centric governance over the data. It also enables distributed, federated computation for AI inference over those data and proves the viability of client-side AI in medical imaging. AVAILABILITY: The open-source application is publicly available at , with a short video demonstration at

Development of a Radiology Information Systems (RIS)

Un sistema d'informació de radiologia o RIS és un sistema de programari unificat que té com a objectiu gestionar totes les dades i donar suport a tots els processos de negoci i fluxos de treball necessaris en un centre de radiologia. Aquest document mostra el procés de dissenyar, desenvolupar, i implantar una solució de nova creació. En aquest document s'explica tot el procés des de l'anàlisi prèvia fins al resultat final. És important tenir en compte que, a causa de les restriccions d'extensió i de temps, s'ha seleccionat acuradament un abast representatiu més reduït de manera que encara il·lustra el millor posible el projecte.A Radiology Information System or RIS is a unified software system which that aims to manage all the data and support all the required business processes and workflows in a radiology center. This document showcases the process of designing, developing, and implanting a newly created solution. The whole process starting from the prior analysis up to the final result is explained in this document. It is important to note that due to extension and time constraints, a smaller representative scope has been carefully selected in a way that still illustrates the larger project as best as possible

Center Scan: March 1990

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