scholarly journals On the development of a FHIR-compliant backend for processing HTTP requests and APIbased management of healthcare documents

2021 ◽  
Vol 7 (2) ◽  
pp. 133-135
Author(s):  
Rene Happel ◽  
Pavel Larionov ◽  
Thomas Schanze
Keyword(s):  
Palliative Care ◽  
Digital Health ◽  
Health Data ◽  
Biomedical Data ◽  
User Identification ◽  
Affected Person

Abstract In palliative care, it is important to inform relatives or caring persons about the condition of the affected person, this could also include vital or biomedical data. Nonprofessional caregivers need information around the topic. However, the data should be stored in a backend system and be able to be viewed and edited by multiple caregivers on multiple platforms. Using the example of the backend solution described here, we will show how a FHIR-compliant server for health data, which could be provided from a digital health App (DiGA), can be established, as well as a second server for article database as a "CRUD"-API and user identification.

scholarly journals Assessing and piloting interoperability for population health research

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
F Estupiñán-Romero ◽  
J Gonzalez-García ◽  
E Bernal-Delgado
Keyword(s):  
Case Studies ◽  
Population Health ◽  
Health Research ◽  
Network Architecture ◽  
Digital Health ◽  
Data Extraction ◽  
Health Data ◽  
Research Network ◽  
Technical Solution ◽  
Privacy And Security

Abstract Issue/problem Interoperability is paramount when reusing health data from multiple data sources and becomes vital when the scope is cross-national. We aimed at piloting interoperability solutions building on three case studies relevant to population health research. Interoperability lies on four pillars; so: a) Legal frame (i.e., compliance with the GDPR, privacy- and security-by-design, and ethical standards); b) Organizational structure (e.g., availability and access to digital health data and governance of health information systems); c) Semantic developments (e.g., existence of metadata, availability of standards, data quality issues, coherence between data models and research purposes); and, d) Technical environment (e.g., how well documented are data processes, which are the dependencies linked to software components or alignment to standards). Results We have developed a federated research network architecture with 10 hubs each from a different country. This architecture has implied: a) the design of the data model that address the research questions; b) developing, distributing and deploying scripts for data extraction, transformation and analysis; and, c) retrieving the shared results for comparison or pooled meta-analysis. Lessons The development of a federated architecture for population health research is a technical solution that allows full compliance with interoperability pillars. The deployment of this type of solution where data remain in house under the governance and legal requirements of the data owners, and scripts for data extraction and analysis are shared across hubs, requires the implementation of capacity building measures. Key messages Population health research will benefit from the development of federated architectures that provide solutions to interoperability challenges. Case studies conducted within InfAct are providing valuable lessons to advance the design of a future pan-European research infrastructure.

scholarly journals Digital health interventions in palliative care: a systematic meta-review

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Anne M. Finucane ◽  
Hannah O’Donnell ◽  
Jean Lugton ◽  
Tilly Gibson-Watt ◽  
Connie Swenson ◽  
...  
Keyword(s):  
Decision Making ◽  
Palliative Care ◽  
Systematic Reviews ◽  
Psychological Symptoms ◽  
Digital Health ◽  
Evidence Synthesis ◽  
Information Provision ◽  
Health Interventions ◽  
Meta Review

AbstractDigital health interventions (DHIs) have the potential to improve the accessibility and effectiveness of palliative care but heterogeneity amongst existing systematic reviews presents a challenge for evidence synthesis. This meta-review applied a structured search of ten databases from 2006 to 2020, revealing 21 relevant systematic reviews, encompassing 332 publications. Interventions delivered via videoconferencing (17%), electronic healthcare records (16%) and phone (13%) were most frequently described in studies within reviews. DHIs were typically used in palliative care for education (20%), symptom management (15%), decision-making (13%), information provision or management (13%) and communication (9%). Across all reviews, mostly positive impacts were reported on education, information sharing, decision-making, communication and costs. Impacts on quality of life and physical and psychological symptoms were inconclusive. Applying AMSTAR 2 criteria, most reviews were judged as low quality as they lacked a protocol or did not consider risk of bias, so findings need to be interpreted with caution.

scholarly journals MAPPING HEALTH: HOW DANES EXPERIENCE THEIR DIGITAL HEALTH DATA

2021 ◽  
Author(s):  
Martina Skrubbeltrang Mahnke ◽  
Mikka Nielsen
Keyword(s):  
Empirical Analysis ◽  
Digital Health ◽  
Health Data ◽  
Medical Data ◽  
Emotional Experiences ◽  
Structural Effects ◽  
Primary Analysis ◽  
Personal Experiences ◽  
User Data ◽  
The Empirical Analysis

This paper explores how Danish citizens experience digital health data and how these in turn affect their understanding of digital health data and their self-understanding as a patient. Previous research on digital health data examines primarily opportunities and challenges as well as structural effects concluding that having access to one's medical data is generally beneficial for patients but also comes with literacy challenges. The aim of this research is to look deeper into personal experiences with digital health data in order to understand what is at stake when people become digitally mapped patients and how experiences of empowerment, independence, perplexity, and doubt intermingle when reading one’s own health data. Taking a user’s view, the paper draws theoretically on the concept of ‘assemblage’ understanding digital health data as a complex nexus of user-data relationships. The empirical analysis draws on 16 in-depth purposefully sampled interviews that have been coded thematically. The primary analysis shows that digital health data creates unique, deeply emotional experiences that lead towards a variety of existential questions. Combining the theoretical lens with the empirical analysis this paper contributes with what we call ‘health assemblages’ that highlight the emerging relationships and personal emotional attachments users make with their digital health data. In conclusion, it can be stated that seeing oneself mapped in data creates unique experiences, often challenging the self-understanding of the patient.

Privacy Preserving in Digital Health

2021 ◽  
pp. 1503-1526
Author(s):  
Zakariae El Ouazzani ◽  
Hanan El Bakkali ◽  
Souad Sadki
Keyword(s):  
Information And Communication Technologies ◽  
Digital Health ◽  
Health Sector ◽  
Privacy Preserving ◽  
Health Data ◽  
Patient Privacy ◽  
Healthcare Organizations ◽  
Related Data ◽  
Healthcare Data ◽  
Privacy Violation

Recently, digital health solutions are taking advantage of recent advances in information and communication technologies. In this context, patients' health data are shared with other stakeholders. Moreover, it's now easier to collect massive health data due to the rising use of connected sensors in the health sector. However, the sensitivity of this shared healthcare data related to patients may increase the risks of privacy violation. Therefore, healthcare-related data need robust security measurements to prevent its disclosure and preserve patients' privacy. However, in order to make well-informed decisions, it is often necessary to allow more permissive security policies for healthcare organizations even without the consent of patients or against their preferences. The authors of this chapter concentrate on highlighting these challenging issues related to patient privacy and presenting some of the most significant privacy preserving approaches in the context of digital health.

scholarly journals Opportunities for Artificial Intelligence in Advancing Precision Medicine

2019 ◽  
Vol 7 (4) ◽  
pp. 208-213 ◽  
Author(s):  
Fabian V. Filipp
Keyword(s):  
Artificial Intelligence ◽  
Precision Medicine ◽  
Large Scale ◽  
Digital Health ◽  
Disease Classification ◽  
Biomedical Data ◽  
Cell Clustering ◽  
Genome Wide ◽  
Definition Of ◽  
Disease Signatures

Abstract Purpose of Review We critically evaluate the future potential of machine learning (ML), deep learning (DL), and artificial intelligence (AI) in precision medicine. The goal of this work is to show progress in ML in digital health, to exemplify future needs and trends, and to identify any essential prerequisites of AI and ML for precision health. Recent Findings High-throughput technologies are delivering growing volumes of biomedical data, such as large-scale genome-wide sequencing assays; libraries of medical images; or drug perturbation screens of healthy, developing, and diseased tissue. Multi-omics data in biomedicine is deep and complex, offering an opportunity for data-driven insights and automated disease classification. Learning from these data will open our understanding and definition of healthy baselines and disease signatures. State-of-the-art applications of deep neural networks include digital image recognition, single-cell clustering, and virtual drug screens, demonstrating breadths and power of ML in biomedicine. Summary Significantly, AI and systems biology have embraced big data challenges and may enable novel biotechnology-derived therapies to facilitate the implementation of precision medicine approaches.

scholarly journals ‘We are very individual’: anticipated effects on stroke survivors of using their person-generated health data

2020 ◽  
Vol 27 (3) ◽  
pp. e100149
Author(s):  
Gerardo Luis Dimaguila ◽  
Frances Batchelor ◽  
Mark Merolli ◽  
Kathleen Gray
Keyword(s):  
Health Services ◽  
Information Technologies ◽  
Digital Health ◽  
Healthcare Providers ◽  
Health Behaviours ◽  
Stroke Care ◽  
Health Data ◽  
Care Process ◽  
Stroke Survivors ◽  
Patient Reported

BackgroundPerson-generated health data (PGHD) are produced by people when they use health information technologies. People who use PGHD may experience changes in their health and care process, such as engagement with their own healthcare, and their sense of social support and connectedness. Research into evaluating those reported effects has not kept up; thus, a method for measuring PGHD outcomes was previously designed and applied to the exemplar case of Kinect-based stroke rehabilitation systems. A key step of the method ensures that the patient’s voice is included. Allowing stroke survivors to participate in the development and evaluation of health services and treatment can inform healthcare providers on decisions about stroke care, and thereby improve health outcomes.ObjectiveThis paper presents the perspectives of stroke survivors and clinicians on the anticipated effects of stroke survivors’ use of PGHD from a poststroke simulated rehabilitation technology.MethodsThis study gathered the perspectives of stroke survivors and clinicians through three focus groups and three interviews, recruited for convenience. Participants were also asked questions intended to encourage them to comment on the initial items of the patient-reported outcome measure-PGHD. Deductive thematic analysis was performed.ResultsThis paper has further demonstrated that outcomes of using PGHD can be measured. For instance, stroke survivors described that using PGHD could result in positive, negative and nil effects on their health behaviours. Survivors and clinicians had varying perspectives in three of the six themes presented, and emphasise the importance of allowing stroke survivors to participate in the evaluation of digital health services.

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