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BACKGROUND Sharing health data is challenging because of several technical, ethical, and regulatory issues. The Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles have been conceptualized to enable data interoperability. Many studies provide implementation guidelines, assessment metrics, and software to achieve FAIR-compliant data, especially for health data sets. Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) is a health data content modeling and exchange standard. OBJECTIVE Our goal was to devise a new methodology to extract, transform, and load existing health data sets into HL7 FHIR repositories in line with FAIR principles, develop a Data Curation Tool to implement the methodology, and evaluate it on health data sets from 2 different but complementary institutions. We aimed to increase the level of compliance with FAIR principles of existing health data sets through standardization and facilitate health data sharing by eliminating the associated technical barriers. METHODS Our approach automatically processes the capabilities of a given FHIR end point and directs the user while configuring mappings according to the rules enforced by FHIR profile definitions. Code system mappings can be configured for terminology translations through automatic use of FHIR resources. The validity of the created FHIR resources can be automatically checked, and the software does not allow invalid resources to be persisted. At each stage of our data transformation methodology, we used particular FHIR-based techniques so that the resulting data set could be evaluated as FAIR. We performed a data-centric evaluation of our methodology on health data sets from 2 different institutions. RESULTS Through an intuitive graphical user interface, users are prompted to configure the mappings into FHIR resource types with respect to the restrictions of selected profiles. Once the mappings are developed, our approach can syntactically and semantically transform existing health data sets into HL7 FHIR without loss of data utility according to our privacy-concerned criteria. In addition to the mapped resource types, behind the scenes, we create additional FHIR resources to satisfy several FAIR criteria. According to the data maturity indicators and evaluation methods of the FAIR Data Maturity Model, we achieved the maximum level (level 5) for being Findable, Accessible, and Interoperable and level 3 for being Reusable. CONCLUSIONS We developed and extensively evaluated our data transformation approach to unlock the value of existing health data residing in disparate data silos to make them available for sharing according to the FAIR principles. We showed that our method can successfully transform existing health data sets into HL7 FHIR without loss of data utility, and the result is FAIR in terms of the FAIR Data Maturity Model. We support institutional migration to HL7 FHIR, which not only leads to FAIR data sharing but also eases the integration with different research networks.

Due to population ageing and medical advances, people with advanced chronic diseases (ACD) live longer. Such patients are even more likely to face either temporary or permanent reduced functional reserve, which typically further increases their healthcare resource use and the burden of care on their caregiver(s). Accordingly, these patients and their caregiver(s) may benefit from integrated supportive care provided via digitally supported interventions. This approach may either maintain or improve their quality of life, increase their independence, and optimize the healthcare resource use from early stages. ADLIFE is an EU-funded project, aiming to improve the quality of life of older people with ACD by providing integrated personalized care via a digitally enabled toolbox. Indeed, the ADLIFE toolbox is a digital solution which provides patients, caregivers, and health professionals with digitally enabled, integrated, and personalized care, supporting clinical decisions, and encouraging independence and self-management. Here we present the protocol of the ADLIFE study, which is designed to provide robust scientific evidence on the assessment of the effectiveness, socio-economic, implementation, and technology acceptance aspects of the ADLIFE intervention compared to the current standard of care (SoC) when applied in real-life settings of seven different pilot sites across six countries. A quasi-experimental trial following a multicenter, non-randomized, non-concurrent, unblinded, and controlled design will be implemented. Patients in the intervention group will receive the ADLIFE intervention, while patients in the control group will receive SoC. The assessment of the ADLIFE intervention will be conducted using a mixed-methods approach.

Chronic diseases introduce challenges for the patients to manage the changing requirements of the disease. Patient empowerment activities are a critical component to assist patients in their long-term care journey. In order to be effective, patient empowerment tools need to be well-integrated with the chronic disease management tools used at the clinical sites. This paper explores and analyzes the exploitation of HL7 FHIR to design and implement an interoperable patient empowerment platform that can be seamlessly integrated with external chronic disease management and Electronic Health Record (EHR) systems.

[Background] FAIR (findability, accessibility, interoperability, and reusability) guiding principles seek the reuse of data and other digital research input, output, and objects (algorithms, tools, and workflows that led to that data) making them findable, accessible, interoperable, and reusable. GO FAIR - a bottom-up, stakeholder driven and self-governed initiative - defined a seven-step FAIRification process focusing on data, but also indicating the required work for metadata. This FAIRification process aims at addressing the translation of raw datasets into FAIR datasets in a general way, without considering specific requirements and challenges that may arise when dealing with some particular types of data., [Objectives] This scientific contribution addresses the architecture design of an open technological solution built upon the FAIRification process proposed by “GO FAIR” which addresses the identified gaps that such process has when dealing with health datasets., [Methods] A common FAIRification workflow was developed by applying restrictions on existing steps and introducing new steps for specific requirements of health data. These requirements have been elicited after analyzing the FAIRification workflow from different perspectives: technical barriers, ethical implications, and legal framework. This analysis identified gaps when applying the FAIRification process proposed by GO FAIR to health research data management in terms of data curation, validation, deidentification, versioning, and indexing., [Results] A technological architecture based on the use of Health Level Seven International (HL7) FHIR (fast health care interoperability resources) resources is proposed to support the revised FAIRification workflow., [Discussion] Research funding agencies all over the world increasingly demand the application of the FAIR guiding principles to health research output. Existing tools do not fully address the identified needs for health data management. Therefore, researchers may benefit in the coming years from a common framework that supports the proposed FAIRification workflow applied to health datasets., [Conclusion] Routine health care datasets or data resulting from health research can be FAIRified, shared and reused within the health research community following the proposed FAIRification workflow and implementing technical architecture., This work was performed in the scope of FAIR4Health project31. FAIR4Health has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 824666.

Older age is associated with an increased accumulation of multiple chronic conditions. The clinical management of patients suffering from multiple chronic conditions is very complex, disconnected and time-consuming with the traditional care settings. Integrated care is a means to address the growing demand for improved patient experience and health outcomes of multimorbid and long-term care patients. Care planning is a prevalent approach of integrated care, where the aim is to deliver more personalized and targeted care creating shared care plans by clearly articulating the role of each provider and patient in the care process. In this paper, we present a method and corresponding implementation of a semi-automatic care plan management tool, integrated with clinical decision support services which can seamlessly access and assess the electronic health records (EHRs) of the patient in comparison with evidence based clinical guidelines to suggest personalized recommendations for goals and interventions to be added to the individualized care plans. We also report the results of usability studies carried out in four pilot sites by patients and clinicians., Graphical abstract Unlabelled Image

Introduction Persons with Advanced Chronic Diseases can greatly benefit from digitally supported interventions to improve or maintain their health, avoid unnecessary deterioration, extend their independence and optimize health resources utilization. Digitalisation is expected to lead a profound transformation of health services. It is important to evaluate its impact. The coronavirus disease (COVID-19) pandemic has pushed forward the paradigm shift towards telehealth, highlighting the need worldwide for enabling and promoting digital care. Theory/Methods The project ADLIFE (H2020, SC1-DTH-11-2019, 875209) aims to provide innovative integrated intelligent personalized care to aged patients with complex chronic diseases. Care will be supported by an ICT solution, the ADLIFE Toolbox. The target population is senior (55+) patients with severe Heart failure and/or COPD. In total in the seven pilot sites, 882 patients, 1243 caregivers and 577 healthcare professionals from specialized and primary care services will be involved in pilot operation and evaluation activities. Care delivery models, level of integration, scope and content of the Clinical Decision Support Services (CDSS) and health outcomes has been explored and defined involving relevant stakeholders at each pilot site. Results and Discussion Each pilot has identified improvement areas required to enable the delivery of care proposed in ADLIFE. A conceptual framework has been developed to that includes and categorize health outcomes. Data will be used for evaluation and CDSS. A set of PROMs has been proposed to capture the information on the effectiveness and the quality of the care delivered as perceived by the patients. CDSS are based on computable flowcharts as defined in evidence based clinical guidelines (single disease and reconciled for multimorbidity). AI algorithms will be developed and trained to identify Potentially Preventable Situations. Conclusions (comprising key findings) The project seeks to demonstrate that a personalized care model that integrates clinical and patient stakeholders´ analysis with ICT tools is effective and flexible and can be deployed and replicated in different health care systems. Lessons learned Assessing care models and having trusted personalised digital solutions are key for ADLIFE implementation and operation in the seven ICT health systems. Limitations Multimorbidity covers a wide range of conditions. ADLIFE focuses on COPD and/or Heart Failure with or without co morbidities. Further analytical and development work will be needed to be done. Suggestions for future research The effectiveness and efficiency of deploying digitally enabled holistic and integrated supportive care will be assessed by evaluating health gain, quality of life, use of resources and economic costs. The authors are on behalf of the ADLIFE Consortium. The ADLIFE project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875209.

Supported by the European Commission under Horizon 2020, mHealth4Afrika is co-designing and validating a modular, multilingual, state-of-the-art health information system addressing primary healthcare requirements in resource constrained environments. mHealth4Afrika has co-designed a comprehensive range of functionality and medical programs in partnership with Ministries of Health, district health officers, clinic managers and primary healthcare workers from urban, rural and deep rural health facilities in Ethiopia, Kenya, Malawi and South Africa. This paper provides insights into how mHealth4Afrika is leveraging HL7 FHIR to support standards-based data exchange and interoperability between Electronic Medical Records and DHIS2. This work is currently being validated in the field.

mHealth4Afrika has introduced the use of CE approved medical sensors at the point of care in primary healthcare facilities in Africa as part of an integrated platform supporting primary health care services. This paper shares insights into the standards-based architecture and HL7 FHIR service developed to support data transfer from sensors with proprietary standards to populate the mHealth4Afrika electronic patient record via custom Android and Windows applications. The current iteration is being validated in healthcare facilities in Ethiopia, Kenya, Malawi and South Africa.

ObjectiveWe aim to deliver a framework with 2 main objectives: 1) facilitating the design of theory-driven, adaptive, digital interventions addressing chronic illnesses or health problems and 2) producing personalized intervention delivery strategies to support self-management by optimizing various intervention components tailored to people’s individual needs, momentary contexts, and psychosocial variables.Materials and MethodsWe propose a template-based digital intervention design mechanism enabling the configuration of evidence-based, just-in-time, adaptive intervention components. The design mechanism incorporates a rule definition language enabling experts to specify triggering conditions for interventions based on momentary and historical contextual/personal data. The framework continuously monitors and processes personal data space and evaluates intervention-triggering conditions. We benefit from reinforcement learning methods to develop personalized intervention delivery strategies with respect to timing, frequency, and type (content) of interventions. To validate the personalization algorithm, we lay out a simulation testbed with 2 personas, differing in their various simulated real-life conditions.ResultsWe evaluate the design mechanism by presenting example intervention definitions based on behavior change taxonomies and clinical guidelines. Furthermore, we provide intervention definitions for a real-world care program targeting diabetes patients. Finally, we validate the personalized delivery mechanism through a set of hypotheses, asserting certain ways of adaptation in the delivery strategy, according to the differences in simulation related to personal preferences, traits, and lifestyle patterns.ConclusionWhile the design mechanism is sufficiently expandable to meet the theoretical and clinical intervention design requirements, the personalization algorithm is capable of adapting intervention delivery strategies for simulated real-life conditions.

Older age is associated with an increased accumulation of multiple chronic conditions. The clinical management of patients suffering from multiple chronic conditions is very complex, disconnected and time-consuming with the traditional care settings. Integrated care is a means to address the growing demand for improved patient experience and health outcomes of multimorbid and long-term care patients. Care planning is a prevalent approach of integrated care, where the aim is to deliver more personalized and targeted care creating shared care plans by clearly articulating the role of each provider and patient in the care process. In this paper, we present a method and corresponding implementation of a semi-automatic care plan management tool, integrated with clinical decision support services which can seamlessly access and assess the electronic health records (EHRs) of the patient in comparison with evidence based clinical guidelines to suggest personalized recommendations for goals and interventions to be added to the individualized care plans.

Postmarketing drug surveillance is a crucial aspect of the clinical research activities in pharmacovigilance and pharmacoepidemiology. Successful utilization of available Electronic Health Record (EHR) data can complement and strengthen postmarketing safety studies. In terms of the secondary use of EHRs, access and analysis of patient data across different domains are a critical factor; we address this data interoperability problem between EHR systems and clinical research systems in this paper. We demonstrate that this problem can be solved in an upper level with the use of common data elements in a standardized fashion so that clinical researchers can work with different EHR systems independently of the underlying information model. Postmarketing Safety Study Tool lets the clinical researchers extract data from different EHR systems by designing data collection set schemas through common data elements. The tool interacts with a semantic metadata registry through IHE data element exchange profile. Postmarketing Safety Study Tool and its supporting components have been implemented and deployed on the central data warehouse of the Lombardy region, Italy, which contains anonymized records of about 16 million patients with over 10-year longitudinal data on average. Clinical researchers in Roche validate the tool with real life use cases.

In this study, we address two major problems of spontaneous reporting systems for adverse drug events (ADEs): underreporting and low report content quality. In the scope of WEB-RADR project, we make use of relevant patient information available in electronic health record (EHR) systems to facilitate ADE reporting process and promote spontaneous reporting on mobile devices. By semi-automatically extracting patient context (e.g. medical history, tests, allergies, drug therapies, etc.) from EHRs, we are able to provide a less time consuming reporting experience and much richer report content. Our tests show that 60% of the patient context residing in E2B reports can easily be extracted using epSOS Patient Summary and Consolidated CDA templates as the EHR source. Proposed implementation can also be extended to integrate further EHR profiles without much effort.

Use of medical terminologies and mappings across them are considered to be crucial pre-requisites for achieving interoperable eHealth applications. Built upon the outcomes of several research projects, we introduce a framework for evaluating and utilizing terminology mappings that offers a platform for i) performing various mappings strategies, ii) representing terminology mappings together with their provenance information, and iii) enabling terminology reasoning for inferring both new and erroneous mappings. We present the results of the introduced framework from SALUS project where we evaluated the quality of both existing and inferred terminology mappings among standard terminologies.

Summary Objectives: The objective of this paper is to describe some of the major healthcare information technology (IT) infrastructures in Turkey, namely, Sağlık-Net (Turkish for “Health-Net”), the Centralized Hospital Appointment System, the Basic Health Statistics Module, the Core Resources Management System, and the e-prescription system of the Social Security Institution. International collaboration projects that are integrated with Sağlık-Net are also briefly summarized. Methods: The authors provide a survey of the some of the major healthcare IT infrastructures in Turkey. Results: Sağlık-Net has two main components: the National Health Information System (NHIS) and the Family Medicine Information System (FMIS). The NHIS is a nation-wide infrastructure for sharing patients’ Electronic Health Records (EHRs). So far, EHRs of 78.9 million people have been created in the NHIS. Similarly, family medicine is operational in the whole country via FMIS. Centralized Hospital Appointment System enables the citizens to easily make appointments in healthcare providers. Basic Health Statistics Module is used for collecting information about the health status, risks and indicators across the country. Core Resources Management System speeds up the flow of information between the headquarters and Provincial Health Directorates. The e-prescription system is linked with Sağlık-Net and seamlessly integrated with the healthcare provider information systems. Finally, Turkey is involved in several international projects for experience sharing and disseminating national developments. Conclusion: With the introduction of the “Health Transformation Program” in 2003, a number of successful healthcare IT infrastructures have been developed in Turkey. Currently, work is going on to enhance and further improve their functionality.

Adverse drug events (ADEs) are common, costly and a public health issue. Today, their detection relies on medical chart review and spontaneous reports, but this is known to be rather ineffective. Along with the increasing availability of clinical patient data in electronic health records (EHRs), a computer-based ADE detection has a tremendous potential to contribute to patient safety. Current ADE detection systems are very specific, usually built directly on top of clinical information systems through proprietary interfaces. Thus, it is not possible to run different ADE detection tools on top of already existing systems in an ad-hoc manner. The European project "SALUS" aims at providing the necessary infrastructure and toolset for accessing and analyzing clinical patient data of heterogeneous clinical information systems. This paper highlights the SALUS ADE notification system as the key tool to enable a semi-automatic ADE detection and notification. In contrast to previous work, the ADE notification system is not restricted to a specific clinical environment. It can be run on different clinical data models with different levels of data quality. The system is equipped with innovative features, building up an intelligent, comprehensive ADE detection and notification system that promises a profound impact in the domain of computer-based ADE detection.