A new architecture for intelligent clinical decision support for intensive medicine

Real-time and intelligent decision support systems are of most importance to supply intensive care professionals with important information in useful time. The work presented hereby shows an architectural overview of the communication system with bedside devices such as vital sign monitors. Intelligent Decision Support System for Intensive Medicine (ICDS4IM) goal is to ensure information quality and availability to Intensive Medicine professionals to take supported decisions in a mutable environment where complex and unpredictable events are a common state. Therefore, this work focus on Health Information Systems, Interoperability and Information Diffusion and Archive. Moreover, communication standards and the usage of a new technology such as containerization are discussed. (C) 2020 The Authors. Published by Elsevier B.V.The work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the Projects Scope: UID/CEC/00319/2020 and DSAIPA/DS/0084/2018

Health Information Systems – Opportunities and Challenges in a Global Health Ecosystem

Health Information Systems (HIS) and Health Information Technology (HIT) have experienced significant growth in use and improved functionality in recent years. The global HIS/HIT market is estimated to grow exponentially in value by 2020. This growth in market size is largely attributable to three key factors: 1) the need for disruptive solutions to challenge the spiraling cost of healthcare, 2) the increased penetration into new markets of healthcare related systems, and 3) the increasing demand for personalized medicine driven by the availability of novel, real-time data streams not previously experienced in the healthcare domain. This short article explores these three aspects of HIS/HIT. In order to achieve meaningful advances in people\u27s health through the provision of new technologies, a more integrated and holistic approach is needed in the design and implementation of HIS. The increasing costs of healthcare coupled with the heightened expectations of stakeholders continues to place increasing pressure on those tasked with delivering new health technologies that are ‘fit for purpose’ in respective healthcare settings. More attention needs to be given to understanding the cost of healthcare and how HIS/HIT may create value in healthcare services

Architecture for intensive care data processing and visualization in real-time

Clinical data is growing every day. Ergo, to treat, store and publish such data is an emergent task. Furthermore, analysing data in real-time using streaming and processing technologies and methods, in order to obtain quality data, prepared to support decision making is of extreme value. Big Data emerged with the introduction of real-time processing, thus revolutionizing traditional technologies and techniques through the ability to deal with the volume, speed and variety of data. Countless studies have been proposed in the healthcare domain in search of solutions that allow the flow of data in real-time. However, the work presented hereby is distinguished by allowing the collection, processing, storage and analysis of Intensive Care Units (ICU) data, both collected in real-time from bedside monitors but also stored in a historical repository. The architecture proposed makes use of current technologies, like Nextgen Connector as message supplier and integrator, Elasticsearch as a search index, Kibana for viewing stored data and Grafana for real-time streaming. This article is part of the ICDS4IM project - Intelligent Clinical Decision Support in Intensive Care Medicine to support the experimentation of data processing techniques and technologies, based in HL7 format and collected in real-time so that it can be made available through Health Information Systems across the healthcare institutions.The work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: DSAIPA/DS/0084/2018

The development and internal validation of a multivariable model predicting 6-month mortality for people with opioid use disorder presenting to community drug services in England: a protocol

BackgroundPeople with opioid use disorder have substantially higher standardised mortality rates compared to the general population; however, lack of clear individual prognostic information presents challenges to prioritise or target interventions within drug treatment services. Previous prognostic models have been developed to estimate the risk of developing opioid use disorder and opioid-related overdose in people routinely prescribed opioids but, to our knowledge, none have been developed to estimate mortality risk in people accessing drug services with opioid use disorder. Initial presentation to drug services is a pragmatic time to evaluate mortality risk given the contemporaneous routine collection of prognostic indicators and as a decision point for appropriate service prioritisation and targeted intervention delivery. This study aims to develop and internally validate a model to estimate 6-month mortality risk for people with opioid use disorder from prognostic indicators recorded at initial assessment in drug services in England.MethodsAn English national dataset containing records from individuals presenting to drug services between 1 April 2013 and 1 April 2023 (n > 800,000) (the National Drug Treatment Monitoring System (NDTMS)) linked to their lifetime hospitalisation and death records (Hospital Episode Statistics-Office of National Statistics (HES-ONS)). Twelve candidate prognostic indicator variables were identified based on literature review of demographic and clinical features associated with increased mortality for people in treatment for opioid use disorder. Variables will be extracted at initial presentation to drug services with mortality measured at 6 months. Two multivariable Cox regression models will be developed one for 6-month all-cause mortality and one for 6-month drug-related mortality using backward elimination with a fractional polynomial approach for continuous variables. Internal validation will be undertaken using bootstrapping methods. Discrimination of both models will be reported using Harrel’s c and d-statistics. Calibration curves and slopes will be presented comparing expected and observed event rates.DiscussionThe models developed and internally validated in this study aim to improve clinical assessment of mortality risk for people with opioid use disorder presenting to drug services in England. External validation in different populations will be required to develop the model into a tool to assist future clinical decision-making

Artificial intelligence ethics in precision oncology: balancing advancements in technology with patient privacy and autonomy

Precision oncology is a rapidly evolving field that uses advanced technologies to deliver personalized cancer care based on a patient’s unique genetic and clinical profile. The use of artificial intelligence (AI) in precision oncology has shown great potential to improve diagnosis, treatment planning, and treatment outcomes. However, the integration of AI in precision oncology also raises important ethical considerations related to patient privacy, autonomy, and protection from bias. In this opinion paper, an overview is provided of previous studies that have explored the use of AI in precision oncology and the ethical considerations associated with this technology. The conclusions of these studies are compared, and the importance of approaching the use of AI in precision oncology with caution is emphasized. It is stressed that patient privacy, autonomy, and protection from bias should be made central to the development and use of AI in precision oncology. Clear guidelines and regulations must be established to ensure that AI is used ethically and for the benefit of patients. The use of AI in precision oncology has the potential to revolutionize cancer care, but it should be ensured that it striked a balance between advancements in technology and ethical considerations. In conclusion, the use of AI in precision oncology is a promising development that has the potential to improve cancer outcomes. However, ethical considerations related to patient privacy, autonomy, and protection from bias must be central to the development and use of AI in precision oncology

Sistem Pendukung Keputusan Pemilihan Bahan Makanan Bergizi Untuk Manula Menggunakan Metode Simple Additive Weighting ( Studi Kasus di Panti Jompo Laweyan )

Elderly is natural process that accompained a decrease in the physical condition, psychological and social has the potential to cause health problems, both on general cases and specifically on the mental health of the elderly. The lack of nutrition will reflects a low physical quality and impact on the level of health condition. The purpose of this research is to ease users in selecting nutritious foods for elderly. This application use SAW methods (simple additive weighting). Aapplication use some benefit & cost criteria. The cost criteria is sex (male, female), and the benefit are (age 60-75, 75-90, >90), diseases (heart, uric, stroke, high blood), and alergics (fish, shrimp, duck, shells). The result of this research is an application that serve users to determine good foodstuff for elderly

Investigating the IT Silo problem: From Strict to Adaptive mirroring between IT Architecture and Organisational Health Services

A crucial problem reducing efficient information flow within healthcare is the presence of siloed IT architectures. Siloed IT Architectures causes disruptive and disconnected information flow within and between health institutions, and complicates the establishment of qualitative health services to practitioners and citizens. In this paper, we analyze this challenge using a mirroring lens. Our research question is, how can we establish a supportive IT architecture that reduces the IT silo problem? Our empirical evidence comes from a case in Norway, where we analyzed a transformation initiative on the national, regional, and local levels. Our investigation into the IT silo problem contributes to the literature on information flow and IT architecture within healthcare in two ways. First, we find that strict mirroring that leads to sub-optimization and silofication, is a major cause for the presence of IT silos. Second, we demonstrate how adaptive mirroring – a modular strategy for combining global and local requirements in IT architecture – improves the changeability and manageability of IT architectures

On Intelligence Augmentation and Visual Analytics to Enhance Clinical Decision Support Systems

Human-in-the-loop intelligence augmentation (IA) methods combined with visual analytics (VA) have the potential to provide additional functional capability and cognitively driven interpretability to Decision Support Systems (DSS) for health risk assessment and patient-clinician shared decision making. This paper presents some key ideas underlying the synthesis of IA with VA (IA/VA) and the challenges in the design, implementation, and use of IA/VA-enabled clinical decision support systems (CDSS) in the practice of medicine through data driven analytical models. An illustrative IA/VA solution provides a visualization of the distribution of health risk, and the impact of various parameters on the assessment, at the population and individual levels. It also allows the clinician to ask “what-if” questions using interactive visualizations that change actionable risk factors of the patient and visually assess their impact. This approach holds promise in enhancing decision support systems design, deployment and use outside the medical sphere as well