Search Results (11)

The integration of systems for Adaptive Business Intelligence (ABI) in the healthcare industry has the potential to revolutionize and reform the way organizations approach data analysis and decision-making. By providing real-time actionable insights and enabling organizations to continuously adapt and evolve, ABI has the potential to drive better outcomes, reduce costs, and improve the overall quality of patient care. The ABI Interoperability System was designed to facilitate the usage and integration of ABI systems in healthcare environments through interoperability resources like Health Level 7 (HL7) or Fast Healthcare Interoperability Resources (FHIR). The present article briefly describes both versions of this software, learning about their differences and improvements, and how they affect the solution. The changes introduced in the new version of the system will tackle code quality with automated tests, development workflow, and developer experience, with the introduction of Continuous Integration and Delivery pipelines in the development workflow, new support for the FHIR pattern, and address a few security concerns about the architecture. The second revision of the system features a more refined, modern, and secure architecture and has proven to be more performant and efficient than its predecessor. As it stands, the Interoperability System poses a significant step forward toward interoperability and ease of integration in the healthcare ecosystem. Full article

(1) Background: Tumor-specific standardized data are essential for AI-based progress in research, e.g., for predicting adverse events in patients with melanoma. Although there are oncological Fast Healthcare Interoperability Resources (FHIR) profiles, it is unclear how well these can represent malignant melanoma. (2) Methods: We created a methodology pipeline to assess to what extent an oncological FHIR profile, in combination with a standard FHIR specification, can represent a real-world data set. We extracted Electronic Health Record (EHR) data from a data platform, and identified and validated relevant features. We created a melanoma data model and mapped its features to the oncological HL7 FHIR Basisprofil Onkologie [Basic Profile Oncology] and the standard FHIR specification R4. (3) Results: We identified 216 features. Mapping showed that 45 out of 216 (20.83%) features could be mapped completely or with adjustments using the Basisprofil Onkologie [Basic Profile Oncology], and 129 (60.85%) features could be mapped using the standard FHIR specification. A total of 39 (18.06%) new, non-mappable features could be identified. (4) Conclusions: Our tumor-specific real-world melanoma data could be partially mapped using a combination of an oncological FHIR profile and a standard FHIR specification. However, important data features were lost or had to be mapped with self-defined extensions, resulting in limited interoperability. Full article

With digital patient data increasing due to new diagnostic methods and technology, showing the right data in the context of decision support at the point of care becomes an even greater challenge. Standard operating procedures (SOPs) modeled in BPMN (Business Process Model and Notation) contain evidence-based treatment guidance for all phases of a certain diagnosis, while physicians need the parts relevant to a specific patient at a specific point in the clinical process. Therefore, integration of patient data from electronic health records (EHRs) providing context to clinicians is needed, which is stored and communicated in HL7 (Health Level Seven) FHIR (Fast Healthcare Interoperability Resources). To address this issue, we propose a method combining an integration of stored data into BPMN and a loss-free transformation from BPMN into FHIR, and vice versa. Based on that method, an identification of the next necessary decision point in a specific patient context is possible. We verified the method for treatment of malignant melanoma by using an extract of a formalized SOP document with predefined decision points and validated FHIR references with real EHR data. The patient data could be stored and integrated into the BPMN element ‘DataStoreReference’. Our loss-free transformation process therefore is the foundation for combining evidence-based knowledge from formalized clinical guidelines or SOPs and patient data from EHRs stored in FHIR. Processing the SOP with the available patient data can then lead to the next upcoming decision point, which will be displayed to the physician integrated with the corresponding data. Full article

An accurate and compact electrocardiogram (ECG) device will greatly assist doctors in diagnosing heart diseases. It will also help to address the increasing number of deaths caused by heart disease. Accordingly, the goal of the project is to design and construct an easy-to-use compact 12-lead electrocardiogram device that communicates with a computer to create a system that can continuously monitor heart rate and which can be connected to allied medical systems. The design is based on an ECG receiver circuit utilizing an IC ADS1293 and an Arduino Nano. The ADS1293 has built-in input Electromagnetic Interference (EMI) filters, quantizers, and digital filters, which help in reducing the size of the device. The software has been created using the C# programming language, with Windows Presentation Foundation (WPF), aiding the collection of the ECG signals from the receiving circuit via the computer port. An ECG Multiparameter Simulator has been used to calibrate the ECG device. Finally, a plan has been developed to connect the arrangement to health systems according to HL7 FHIR (Health Level Seven Fast Healthcare Interoperability Resources) through Representational State Transfer Application Programming Interface (Rest API). The ECG device, completed at the cost of U$169 excluding labor, allows for the signal of 12 leads of ECG signal to be obtained from 10 electrodes mounted on the body. The processed ECG data was written to a JSON file with a maximum recording time of up to three days, managed by a Structured Query Language Server (SQL) Server database. The software retrieves patient data from electrical medical records in accordance with HL7 FHIR standards. A compact and easy-to-use ECG device was successfully designed to record ECG signals. An in-house developed software was also completed to display and store the ECG signals. Full article

Although Health Level Seven (HL 7) message standards (v2, v3, Clinical Document Architecture (CDA)) have been commonly adopted, there are still issues associated with them, especially the semantic interoperability issues and lack of support for smart devices (e.g., smartphones, fitness trackers, and smartwatches), etc. In addition, healthcare organizations in many countries are still using proprietary electronic health record (EHR) message formats, making it challenging to convert to other data formats—particularly the latest HL7 Fast Health Interoperability Resources (FHIR) data standard. The FHIR is based on modern web technologies such as HTTP, XML, and JSON and would be capable of overcoming the shortcomings of the previous standards and supporting modern smart devices. Therefore, the FHIR standard could help the healthcare industry to avail the latest technologies benefits and improve data interoperability. The data representation and mapping from the legacy data standards (i.e., HL7 v2 and EHR) to the FHIR is necessary for the healthcare sector. However, direct data mapping or conversion from the traditional data standards to the FHIR data standard is challenging because of the nature and formats of the data. Therefore, in this article, we propose a framework that aims to convert proprietary EHR messages into the HL7 v2 format and apply an unsupervised clustering approach using the DBSCAN (density-based spatial clustering of applications with noise) algorithm to automatically group a variety of these HL7 v2 messages regardless of their semantic origins. The proposed framework’s implementation lays the groundwork to provide a generic mapping model with multi-point and multi-format data conversion input into the FHIR. Our experimental results show the proposed framework’s ability to automatically cluster various HL7 v2 message formats and provide analytic insight behind them. Full article

Currently in hospitals, there are several separate information systems that manage, very often autonomously, the patient’s personal, clinical and diagnostic data. An electronic health record system has been specifically developed for a cardiology ward and it has been designed “ab initio” to be fully integrated into the hospital information system and to exchange data with the regional health information infrastructure. All documents have been given as Health Level 7 (HL7) clinical document architecture and messages are sent as HL7-Version 2 (V2) and/or HL7 Fast Healthcare Interoperability Resources (FHIR). Specific decision support sections for specific aspects have also been included. The system has been used for more than three years with a good level of satisfaction by the users. In the future, the system can be the basis for secondary use for clinical studies, further decision support systems and clinical trials. Full article

Heterogeneity is a problem in storing and exchanging data in a digital health information system (HIS) following semantic and structural integrity. The existing literature shows different methods to overcome this problem. Fast healthcare interoperable resources (FHIR) as a structural standard may explain other information models, (e.g., personal, physiological, and behavioral data from heterogeneous sources, such as activity sensors, questionnaires, and interviews) with semantic vocabularies, (e.g., Systematized Nomenclature of Medicine—Clinical Terms (SNOMED-CT)) to connect personal health data to an electronic health record (EHR). We design and develop an intuitive health coaching (eCoach) smartphone application to prove the concept. We combine HL7 FHIR and SNOMED-CT vocabularies to exchange personal health data in JavaScript object notion (JSON). This study explores and analyzes our attempt to design and implement a structurally and logically compatible tethered personal health record (PHR) that allows bidirectional communication with an EHR. Our eCoach prototype implements most PHR-S FM functions as an interoperability quality standard. Its end-to-end (E2E) data are protected with a TSD (Services for Sensitive Data) security mechanism. We achieve 0% data loss and 0% unreliable performances during data transfer between PHR and EHR. Furthermore, this experimental study shows the effectiveness of FHIR modular resources toward flexible management of data components in the PHR (eCoach) prototype. Full article

The “Coherent Data Set” is a novel synthetic data set that leverages structured data from Synthea™ to create a longitudinal, “coherent” patient-level electronic health record (EHR). Comprised of synthetic patients, the Coherent Data Set is publicly available, reproducible using Synthea™, and free of the privacy risks that arise from using real patient data. The Coherent Data Set provides complex and representative health records that can be leveraged by health IT professionals without the risks associated with de-identified patient data. It includes familial genomes that were created through a simulation of the genetic reproduction process; magnetic resonance imaging (MRI) DICOM files created with a voxel-based computational model; clinical notes in the style of traditional subjective, objective, assessment, and plan notes; and physiological data that leverage existing System Biology Markup Language (SBML) models to capture non-linear changes in patient health metrics. HL7 Fast Healthcare Interoperability Resources (FHIR^®) links the data together. The models can generate clinically logical health data, but ensuring clinical validity remains a challenge without comparable data to substantiate results. We believe this data set is the first of its kind and a novel contribution to practical health interoperability efforts. Full article

Personal health records (PHRs) have lots of benefits for things such as health surveillance, epidemiological surveillance, self-control, links to various services, public health and health management, and international surveillance. The implementation of an international standard for interoperability is essential to accessing personal health records. In Taiwan, the nationwide exchange platform for electronic medical records (EMRs) has been in use for many years. The Health Level Seven International (HL7) Clinical Document Architecture (CDA) was used as the standard of the EMRs. However, the complication of implementing CDA became a barrier for many hospitals to realize the standard EMRs. In this study, we implemented a Fast Healthcare Interoperability Resources (FHIR)-based PHR transformation process including a user interface module to review the contents of PHRs. We used “My Health Bank, MHB”, a PHR data book developed and issued to all people by the Taiwan National Health Insurance, as the PHRs contents in this study. Network Time Protocol (NTP)/Simple Network Time Protocol (SNTP) was used in the security and user authentication mechanism when processing and applying personal health information. Transport Layer Security (TLS) 1.2 (such as HyperText Transfer Protocol Secure (HTTPS) was used for protection in data communication. User authentication is important in the platform. OAuth (OAuth 2.0) was used as a user authentication mechanism to confirm legitimate user access to ensure data security. The contents of MHB were analyzed and mapped to the FHIR, and then converted to FHIR format according to the mapping logic template. The function of format conversion was carried out by using ASP.NET. XPath and JSPath technologies filtered out specific information tags. The converted data structure was verified through an HL7 Application Programming Interface (HAPI) server, and a new JSON file was finally created. This platform can not only capture any PHR based on the FHIR format but also publish FHIR-based MHB records to any other platform to bridge the interoperability gap between different PHR systems. Therefore, our implementation/application with the automatic transformation from MHB to FHIR format provides an innovative method for people to access their own PHRs (MHB). No one has published a similar application like us using a nationwide PHR standard, MHB, in Taiwan. The application we developed will be very useful for a single person to use or for other system developers to implement their own standard PHR software. Full article

This paper is an extension of work originally presented to pHealth 2019—16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. To provide an efficient decision support, it is necessary to integrate clinical decision support systems (CDSSs) in information systems routinely operated by healthcare professionals, such as hospital information systems (HISs), or by patients deploying their personal health records (PHR). CDSSs should be able to use the semantics and the clinical context of the data imported from other systems and data repositories. A CDSS platform was developed as a set of separate microservices. In this context, we implemented the core components of a CDSS platform, namely its communication services and logical inference components. A fast healthcare interoperability resources (FHIR)-based CDSS platform addresses the ease of access to clinical decision support services by providing standard-based interfaces and workflows. This type of CDSS may be able to improve the quality of care for doctors who are using HIS without CDSS features. The HL7 FHIR interoperability standards provide a platform usable by all HISs that are FHIR enabled. The platform has been implemented and is now productive, with a rule-based engine processing around 50,000 transactions a day with more than 400 decision support models and a Bayes Engine processing around 2000 transactions a day with 128 Bayesian diagnostics models. Full article

The development of electronic health records, wearable devices, health applications and Internet of Things (IoT)-empowered smart homes is promoting various applications. It also makes health self-management much more feasible, which can partially mitigate one of the challenges that the current healthcare system is facing. Effective and convenient self-management of health requires the collaborative use of health data and home environment data from different services, devices, and even open data on the Web. Although health data interoperability standards including HL7 Fast Healthcare Interoperability Resources (FHIR) and IoT ontology including Semantic Sensor Network (SSN) have been developed and promoted, it is impossible for all the different categories of services to adopt the same standard in the near future. This study presents a method that applies Semantic Web technologies to integrate the health data and home environment data from heterogeneously built services and devices. We propose a Web Ontology Language (OWL)-based integration ontology that models health data from HL7 FHIR standard implemented services, normal Web services and Web of Things (WoT) services and Linked Data together with home environment data from formal ontology-described WoT services. It works on the resource integration layer of the layered integration architecture. An example use case with a prototype implementation shows that the proposed method successfully integrates the health data and home environment data into a resource graph. The integrated data are annotated with semantics and ontological links, which make them machine-understandable and cross-system reusable. Full article