Healthcare Informatics suffers from the lack of information exchange among domain partners. Allowing cooperation among distributed and heterogeneous applications is a major need of current healthcare information systems. Beyond the communication and integration problems, medical information itself is by nature complex, combined with data and knowledge. The increasing number of standards and representation of the same data in different structures using these standards constitute another problem in the domain. Platform and implementation independency makes Web service technology the natural way to solve the interoperability problems in the healthcare domain. Standardizing the access to data through WSDL and SOAP rather than standardizing the electronic health record will help to overcome the integration problems among different standards in medical information systems. However, introducing Web services to the healthcare systems will not suffice to solve the problems in the domain unless the semantics of the services are exploited. This thesis aims to show that by generating web services and classifying these services through their functionalities, it is possible to achieve the interoperability among healthcare institutes, such as hospitals. The designed system is based on Artemis P2P Framework, and the annotation of the system is realized in the same framework.

Yes / The use of Information and Communications Technology (ICTs) in healthcare has the potential of minimizing medical errors, reducing healthcare cost and improving collaboration between healthcare systems which can dramatically improve the healthcare service quality. However interoperability within different healthcare systems (clinics/hospitals/pharmacies) remains an issue of further research due to a lack of collaboration and exchange of healthcare information. To solve this problem, cross healthcare system collaboration is required. This paper proposes a conceptual semantic based healthcare collaboration framework based on Internet of Things (IoT) infrastructure that is able to offer a secure cross system information and knowledge exchange between different healthcare systems seamlessly that is readable by both machines and humans. In the proposed framework, an intelligent semantic gateway is introduced where a web application with restful Application Programming Interface (API) is used to expose the healthcare information of each system for collaboration. A case study that exposed the patient's data between two different healthcare systems was practically demonstrated where a pharmacist can access the patient's electronic prescription from the clinic. / British Council Institutional Links grant under the BEIS-managed Newton Fund.

An average of 3 million deaths occurs each year in high-income countries due to unsafe care, with causes including diagnostic and communication failures. These failures are related to clinical information overload, the extraction of essential unstructured data, and complex health data analytics for deriving insights. The use case of this dissertation focuses on emergency room (ER) physicians, as they are the initial point of contact for patients, and time-sensitive situations occur frequently in the ER. The goal is to develop an intelligent clinical information platform (ICIP) for ER physicians, assisting patients’ care pathways using machine learning (ML). This platform provides a new, multidimensional view to represent patients’ medical conditions, focused on heart diseases. To achieve the platform’s implementation, three technical components are developed and published within this dissertation: first, a component for data extraction from remote video consultations via WebRTC; second, a data classification component using a Faster Region-Based Convolutional Neural Network (R-CNN) model together with active learning (AL); and third, a data search component with an implemented Elasticsearch pipeline and data storage unified in the FHIR standard. The research for a newly developed clinical platform is practically and industrially based on building a future clinical product. For this product, ML models are developed to analyze data from past clinical treatments using an R-CNN model for text classification and to access verbal audio data through a speech-to-text (STT) engine employing an RNN TensorFlow model and a large language model (LLM) from NLP.js. Additionally, JSON object-based rule-based reasoning from FHIR is used. It has been demonstrated that a three-tier architecture (AngularJS, Java Spring Boot, and PostgreSQL), consisting of components involving neural networks such as R-CNN, RNN (recurrent neural network), and LLM, can be implemented as a data platform for assisting heart disease care pathways. This allows physicians to interpret patients’ vital parameters, pathways, and timelines via diagrams presented in widgets on the AngularJS frontend.

Background: In the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium, an IT-based clinical trial recruitment support system was developed based on the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Currently, OMOP CDM is populated with German Fast Healthcare Interoperability Resources (FHIR) using an Extract-Transform-Load (ETL) process, which was designed as a bulk load. However, the computational effort that comes with an everyday full load is not efficient for daily recruitment. Objective: The aim of this study is to extend our existing ETL process with the option of incremental loading to efficiently support daily updated data. Methods: Based on our existing bulk ETL process, we performed an analysis to determine the requirements of incremental loading. Furthermore, a literature review was conducted to identify adaptable approaches. Based on this, we implemented three methods to integrate incremental loading into our ETL process. Lastly, a test suite was defined to evaluate the incremental loading for data correctness and performance compared to bulk loading. Results: The resulting ETL process supports bulk and incremental loading. Performance tests show that the incremental load took 87.5% less execution time than the bulk load (2.12 min compared to 17.07 min) related to changes of 1 day, while no data differences occurred in OMOP CDM. Conclusions: Since incremental loading is more efficient than a daily bulk load and both loading options result in the same amount of data, we recommend using bulk load for an initial load and switching to incremental load for daily updates. The resulting incremental ETL logic can be applied internationally since it is not restricted to German FHIR profiles.