Consolidated List of Requirements

This document is a consolidated catalogue of requirements for the Electronic Health Care Record (EHCR) and Electronic Health Care Record Architecture (EHCRA), gleaned largely from work done in the EU Framework III and IV programmes and CEN, but also including input from other sources including world-wide standardisation initiatives. The document brings together the relevant work done into a classified inventory of requirements to inform the on-going standardisation process as well as act as a guide to future implementation of EHCRA-based systems. It is meant as a contribution both to understanding of the standard and to the work that is being considered to improve the standard. Major features include the classification into issues affecting the Health Care Record, the EHCR, EHCR processing, EHCR interchange and the sharing of health care information and EHCR systems. The principal information sources are described briefly. It is offered as documentation that is complementary to the four documents of the ENV 13606 Parts I-IV produced by CEN Pts 26,27,28,29. The requirements identified and classified in this deliverable are referenced in other deliverables

Paying for the quantity and quality of hospital care : the foundations and evolution of payment policy in England

Prospective payment arrangements are now the main form of hospital funding in most developed countries. An essential component of such arrangements is the classification system used to differentiate patients according to their expected resource requirements. In this article we describe the evolution and structure of Healthcare Resource Groups (HRGs) in England and the way in which costs are calculated for patients allocated to each HRG. We then describe how payments are made, how policy has evolved to incentivise improvements in quality, and how prospective payment is being applied outside hospital settings

Automated Generation of ICD-11 Cluster Codes for Precision Medical Record Classification

Accurate clinical coding using the International Classification of Diseases (ICD) standard is essential for healthcare analytics. ICD-11 introduces new coding guidelines and cluster structures, posing challenges for existing coding tools. This research presents an automated approach to generate valid ICD-11 cluster codes from medical text. Natural language records are represented as vectors and compared to an ICD-11 corpus using cosine similarity. A bidirectional matching technique then refines similarity estimation. Experiments demonstrate the method yields up to 0.91 F1 score in coding accuracy, significantly outperforming a baseline tool. This work enables efficient high-quality ICD-11 coding to support healthcare informatics

Patterns in domain models : a methodology and its application in the healthcare management domain

Projecte realitzat en el marc d’un programa de mobilitat amb l'Institute of Database Systems and Information Management (DIMA) de la Technische Universität BerlinWe developed a method for extracting patterns from models of the domain. We applied the method and created a catalog for the healthcare management domain. The results of the thesis enable software designers to obtain models of high quality through the reuse of abstracted domain knowledge

Clinical Decision Support System for Unani Medicine Practitioners

Like other fields of Traditional Medicines, Unani Medicines have been found as an effective medical practice for ages. It is still widely used in the subcontinent, particularly in Pakistan and India. However, Unani Medicines Practitioners are lacking modern IT applications in their everyday clinical practices. An Online Clinical Decision Support System may address this challenge to assist apprentice Unani Medicines practitioners in their diagnostic processes. The proposed system provides a web-based interface to enter the patient's symptoms, which are then automatically analyzed by our system to generate a list of probable diseases. The system allows practitioners to choose the most likely disease and inform patients about the associated treatment options remotely. The system consists of three modules: an Online Clinical Decision Support System, an Artificial Intelligence Inference Engine, and a comprehensive Unani Medicines Database. The system employs advanced AI techniques such as Decision Trees, Deep Learning, and Natural Language Processing. For system development, the project team used a technology stack that includes React, FastAPI, and MySQL. Data and functionality of the application is exposed using APIs for integration and extension with similar domain applications. The novelty of the project is that it addresses the challenge of diagnosing diseases accurately and efficiently in the context of Unani Medicines principles. By leveraging the power of technology, the proposed Clinical Decision Support System has the potential to ease access to healthcare services and information, reduce cost, boost practitioner and patient satisfaction, improve speed and accuracy of the diagnostic process, and provide effective treatments remotely. The application will be useful for Unani Medicines Practitioners, Patients, Government Drug Regulators, Software Developers, and Medical Researchers.Comment: 59 pages, 11 figures, Computer Science Bachelor's Thesis on use of Artificial Intelligence in Clinical Decision Support System for Unani Medicine

HEALTH OUTCOME PATHWAY PREDICTION. A GRAPH-BASED FRAMEWORK

This dissertation is part of the project FrailCare.AI, which aims to detect frailty in the elderly Portuguese population in order to optimize the SNS24 (telemonitoring) service, with the goal of suggesting health pathways to reduce the patients frailty. Frailty can be defined as the condition of being weak and delicate which normally increases with age and is the consequence of several health and non-health related factors. A patient health journey is recorded in Eletronic Health Record (EHR), which are rich but sparse, noisy and multi-modal sources of truth. These can be used to train predictive models to predict future health states, where frailty is just one of them. In this work, due to lack of data access we pivoted our focus to phenotype prediction, that is, predicting diagnosis. What is more, we tackle the problem of data-insufficiency and class imbalance (e.g. rare diseases and other infrequent occurrences in the training data) by integrating standardized healthcare ontologies within graph neural networks. We study the broad task of phenotype prediction, multi-task scenarios and as well few-shot scenarios - which is when a class rarely occurs in the training set. Furthermore, during the development of this work we detect some reproducibility issues in related literature which we detail, and also open-source all of our implementations introduding a framework to aid the development of similar systems.A presente dissertação insere-se no projecto FrailCare.AI, que visa detectar a fragilidade da população idosa portuguesa com o objectivo de optimizar o serviço de telemonitoriza- ção do Sistema Nacional de Saúde Português (SNS24), e também sugerir acções a tomar para reduzir a fragilidade dos doentes. A fragilidade é uma condição de risco composta por multiplos fatores. Hoje em dia, grande parte da história clinica de cada utente é gravada digitalmente. Estes dados diversos e vastos podem ser usados treinar modelos preditivos cujo objectivo é prever futuros estados de saúde, sendo que fragilidade é só um deles. Devido à falta de accesso a dados, alteramos a tarefa principal deste trabalho para previsão de diágnosticos, onde exploramos o problema de insuficiência de dados e dese- quilíbrio de classes (por exemplo, doenças raras e outras ocorrências pouco frequentes nos dados de treino), integrando ontologias de conceitos médicos por meio de redes neu- ronais de gráfos. Exploramos também outras tarefas e o impacto que elas têm entre si. Para além disso, durante o desenvolvimento desta dissertação identificamos questões a nivel de reproducibilidade da literatura estudada, onde detalhamos e implementamos os conceitos em falta. Com o objectivo de reproducibilidade em mente, nós libertamos o nosso código, introduzindo um biblioteca que permite desenvlver sistemas semelhantes ao nosso

Linking Research and Policy: Assessing a Framework for Organic Agricultural Support in Ireland

This paper links social science research and agricultural policy through an analysis of support for organic agriculture and food. Globally, sales of organic food have experienced 20% annual increases for the past two decades, and represent the fastest growing segment of the grocery market. Although consumer interest has increased, farmers are not keeping up with demand. This is partly due to a lack of political support provided to farmers in their transition from conventional to organic production. Support policies vary by country and in some nations, such as the US, vary by state/province. There have been few attempts to document the types of support currently in place. This research draws on an existing Framework tool to investigate regionally specific and relevant policy support available to organic farmers in Ireland. This exploratory study develops a case study of Ireland within the framework of ten key categories of organic agricultural support: leadership, policy, research, technical support, financial support, marketing and promotion, education and information, consumer issues, inter-agency activities, and future developments. Data from the Irish Department of Agriculture, Fisheries and Food, the Irish Agriculture and Food Development Authority (Teagasc), and other governmental and semi-governmental agencies provide the basis for an assessment of support in each category. Assessments are based on the number of activities, availability of information to farmers, and attention from governmental personnel for each of the ten categories. This policy framework is a valuable tool for farmers, researchers, state agencies, and citizen groups seeking to document existing types of organic agricultural support and discover policy areas which deserve more attention

Patient-Centric Cellular Networks Optimization using Big Data Analytics

Big data analytics is one of the state-of-the-art tools to optimize networks and transform them from merely being a blind tube that conveys data, into a cognitive, conscious, and self-optimizing entity that can intelligently adapt according to the needs of its users. This, in fact, can be regarded as one of the highest forthcoming priorities of future networks. In this paper, we propose a system for Out-Patient (OP) centric Long Term Evolution-Advanced (LTE-A) network optimization. Big data harvested from the OPs' medical records, along with current readings from their body sensors are processed and analyzed to predict the likelihood of a life-threatening medical condition, for instance, an imminent stroke. This prediction is used to ensure that the OP is assigned an optimal LTE-A Physical Resource Blocks (PRBs) to transmit their critical data to their healthcare provider with minimal delay. To the best of our knowledge, this is the first time big data analytics are utilized to optimize a cellular network in an OP-conscious manner. The PRBs assignment is optimized using Mixed Integer Linear Programming (MILP) and a real-time heuristic. Two approaches are proposed, the Weighted Sum Rate Maximization (WSRMax) approach and the Proportional Fairness (PF) approach. The approaches increased the OPs' average SINR by 26.6% and 40.5%, respectively. The WSRMax approach increased the system's total SINR to a level higher than that of the PF approach, however, the PF approach reported higher SINRs for the OPs, better fairness and a lower margin of error