Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

After being collected for patient care, Observational Health Data (OHD) can further benefit patient well-being by sustaining the development of health informatics and medical research. Vast potential is unexploited because of the fiercely private nature of patient-related data and regulations to protect it. Generative Adversarial Networks (GANs) have recently emerged as a groundbreaking way to learn generative models that produce realistic synthetic data. They have revolutionized practices in multiple domains such as self-driving cars, fraud detection, digital twin simulations in industrial sectors, and medical imaging. The digital twin concept could readily apply to modelling and quantifying disease progression. In addition, GANs posses many capabilities relevant to common problems in healthcare: lack of data, class imbalance, rare diseases, and preserving privacy. Unlocking open access to privacy-preserving OHD could be transformative for scientific research. In the midst of COVID-19, the healthcare system is facing unprecedented challenges, many of which of are data related for the reasons stated above. Considering these facts, publications concerning GAN applied to OHD seemed to be severely lacking. To uncover the reasons for this slow adoption, we broadly reviewed the published literature on the subject. Our findings show that the properties of OHD were initially challenging for the existing GAN algorithms (unlike medical imaging, for which state-of-the-art model were directly transferable) and the evaluation synthetic data lacked clear metrics. We find more publications on the subject than expected, starting slowly in 2017, and since then at an increasing rate. The difficulties of OHD remain, and we discuss issues relating to evaluation, consistency, benchmarking, data modelling, and reproducibility.Comment: 31 pages (10 in previous version), not including references and glossary, 51 in total. Inclusion of a large number of recent publications and expansion of the discussion accordingl

Platforms for big data business models in the healthcare context

Abstract. The profitability of the business opportunity is defined by the level of owned data and its insights to the business organization. However, the existing literature has not identified how to link between different business models in the data-oriented systems. The previous research efforts focused on the technical aspects of data including data monetization, clustering, and data lifecycle. The purpose of this research is to understand how to link big data and business model thinking in the healthcare context. The main argument of this study provides a novel way to the modularity in the big data business models, which enables the system customers to control the system Studies show if there is a kind of data-oriented platform that remind patients to do certain tasks (ex. nutrition and medicine reminders) before going to doctors and nurses; the patients would like to use it. In addition, around 90% of the platform users will recommend it to other patients and so on. This pushes the operators in the healthcare industry to transform their traditional human-based data systems into a computer-to-computer system. In the data-intensive systems like the healthcare industry, the value creation is done by monetizing data between system actors to analyze the data and develop extensive knowledge about the end customer. For example, the hospitals have the right to own and anonymize the patient data to ensure the privacy and security of patient information. Then hospitals monetize the patient data with their business partner who has the technical and analytical capability to analyze data. Later, they provide the system with useful insights gained from data analytics. This is an exploratory phase of research where the qualitative case study approach is applied to examine the possibility of having a common platform for the integrated solutions in the data-oriented systems. To approach these platforms, an empirical study has been conducted over three case companies working in the healthcare context. The data were collected using semi-structured interview discussion. Similar qualitative approaches have been used in some prior studies to examine the value creation in the data-oriented systems and identify the future business models for the digital environments and IoT. This research contributes to the existing literature by identifying four main platforms for big data business models. The modular platform is done due to the lack of knowledge about the end-customer, it grants system partners the right to control over their platforms. The partnership platform guarantees the continuity of the business process, the Ecosystemic platform gives the end customer the possibility to select what they need from the overall ecosystem. The ownership platform is related to the centralized control over the data source, enabling consistency of the business process

Models of information systems devoted to medical-imaging labs: an experience in the CNR clinical physiology institute

At the end of the 1990s, the SPERIGEST project, supported by the Italian Health Ministry, and fully developed at the Institute of Clinical Physiology, established an operative integrated clinical and healthcare information system. Continuously evolving and dynamically optimising procedures and protocols solve problems of: harmonisation of instrumentation of different brands; management of multimedia data provided by different medical imaging labs; satisfaction of both clinical and research needs; legal and economical requirements; user-friendship of the system. A ten years experience shows positive approach by medical and healthcare operators, coordinated activity, higher efficiency, simplified procedures, major concentration on medical decision-making

LORIS: a web-based data management system for multi-center studies

Longitudinal Online Research and Imaging System (LORIS) is a modular and extensible web-based data management system that integrates all aspects of a multi-center study: from heterogeneous data acquisition (imaging, clinical, behavior, and genetics) to storage, processing, and ultimately dissemination. It provides a secure, user-friendly, and streamlined platform to automate the flow of clinical trials and complex multi-center studies. A subject-centric internal organization allows researchers to capture and subsequently extract all information, longitudinal or cross-sectional, from any subset of the study cohort. Extensive error-checking and quality control procedures, security, data management, data querying, and administrative functions provide LORIS with a triple capability (1) continuous project coordination and monitoring of data acquisition (2) data storage/cleaning/querying, (3) interface with arbitrary external data processing “pipelines.” LORIS is a complete solution that has been thoroughly tested through a full 10 year life cycle of a multi-center longitudinal project1 and is now supporting numerous international neurodevelopment and neurodegeneration research projects

O3 – prizadevanja na področju medicinske informatike za e-zdravstveno regijo

The Open Three (O3) Consortium is promoting the adoption of Open Source in e-health on regional, European and World-wide levels. This project aims to contribute to the development of e-health through the study of Healthcare Information Systems and the contemporary proposal of new concepts, designs and solutions for the management of health data in an integrated environment of hospitals, Regional Health Information Organizations and citizens (home-care, mobile-care and ambient assisted living). Some concrete technical solutions in the field of medical informatics are presented in this paper. The applications presented are the heart of the Radiology information system, which is open to other health institutions, thus forming a basis for the realization of e-health integration. The formation of a genuine e-health region is just one step forward.Konzorcij Odprti trije (O3) si prizadeva za sprejetje odprtega vira v e-zdravstvu na regionalnem, evropskem in svetovnem nivoju. Projekt si prizadeva prispevati k razvoju e-zdravstva s pomočjo preučevanja informacijskega sistema zdravstvenega varstva ter sodobnih predlogov novih zasnov, načrtov in rešitev za upravljanje z zdravstvenimi podatki v integriranem okolju bolnišnic, regionalnih organizacijah zdravstvene informatike in pri državljanih (v domači negi, mobilni negi in v primeru bivanja z asistenco v okolju). V prispevku so prikazane nekatere konkretne tehnične rešitve na področju medicinske informatike. Prikazane aplikacije so bistvo radiološkega informacijskega sistema, ki je odprt drugim zdravstvenim institucijam in tako predstavlja temelj za realizacijo e-zdravstvene integracije. Tako je oblikovanje prave e-zdravstvene regije oddaljeno le še za korak

Comparison of Open-Source Electronic Health Record Systems Based on Functional and User Performance Criteria

Objectives: Open-source Electronic Health Record (EHR) systems have gained importance. The main aim of our research is to guide organizational choice by comparing the features, functionality, and user-facing system performance of the five most popular open-source EHR systems. Methods: We performed qualitative content analysis with a directed approach on recently published literature (2012-2017) to develop an integrated set of criteria to compare the EHR systems. The functional criteria are an integration of the literature, meaningful use criteria, and the Institute of Medicine's functional requirements of EHR, whereas the user-facing system performance is based on the time required to perform basic tasks within the EHR system. Results: Based on the Alexa web ranking and Google Trends, the five most popular EHR systems at the time of our study were OSHERA VistA, GNU Health, the Open Medical Record System (OpenMRS), Open Electronic Medical Record (OpenEMR), and OpenEHR. We also found the trends in popularity of the EHR systems and the locations where they were more popular than others. OpenEMR met all the 32 functional criteria, OSHERA VistA met 28, OpenMRS met 12 fully and 11 partially, OpenEHR-based EHR met 10 fully and 3 partially, and GNU Health met the least with only 10 criteria fully and 2 partially. Conclusions: Based on our functional criteria, OpenEMR is the most promising EHR system, closely followed by VistA. With regards to user-facing system performance, OpenMRS has superior performance in comparison to OpenEMR