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Selected Papers from the pHealth 2022 Conference, Oslo, Norway, 8–10 November 2022

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A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Omics/Informatics".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 26789

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Prof. Dr. Bernd Blobel

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Faculty of Medicine, University of Regensburg, 93053 Regensburg, Germany
Interests: systems medicine; interoperability; data security; HL7; eHealth; medical informatics; electronic health records; health informatics; healthcare IT; oncology
Special Issues, Collections and Topics in MDPI journals

Dr. Mauro Giacomini

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Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, 16145 Genoa, Italy
Interests: antibiotics; environment; infection; oncology; biodiversity; analysis; neural networks; classification; information technology; artificial neural networks
Special Issues, Collections and Topics in MDPI journals

Dr. Bian Yang

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Guest Editor

Department of Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU), 2815 Gjøvik, Norway
Interests: information security; identity management; privacy enhancing technologies; biometrics; health informatics and security; steganography; multimedia processing; human-environment interaction measurement and analysis

Special Issue Information

Dear Colleagues,

pHealth 2022 is the 19^th event in the pHealth conferences series, starting in 2003 as a Dissemination Activity in the framework of a European Project on Wearable Micro and Nano Technologies for Personalized Health with personal health management systems. Since then, pHealth conferences have evolved to truly interdisciplinary, global scientific events covering the medical, technological, political, administrative, and social domains, and even philosophical or linguistic challenges of personalized health in transforming health systems. In the last 2–3 years, the focus has turned towards the 5P medicine paradigm of personalized, preventive, predictive, and participative precision medicine. This pHealth 2022 Special Issue presents the best papers selected from the conference, which took place on 8–10 November 2022 in Oslo. It includes the Main Keynote to the conference, seven invited papers, and four regular papers. Furthermore, it contains two papers awarded with the Case-Mix Young Scientists Best Paper Award, as well as one international project paper especially acknowledged by the pHealth 2022 SPC. Thus, standardization in the field, security, privacy and trustworthiness, learning systems and AI, and challenges for low- and middle-income countries are covered, but practical solutions are also demonstrated.

Prof. Dr. Bernd Blobel
Dr. Mauro Giacomini
Dr. Bian Yang
Guest Editors

Publisher’s notice:
As stated above, the central purpose of this Special Issue is to present research from "Selected Papers from the pHealth 2022 Conference, Oslo, Norway, 8–10 November 202". Given this purpose, the Guest Editors’ contribution to this Special Issue may be greater than standard Special Issues published by MDPI. Further details on MDPI's Special Issue guidelines can be found here: https://www.mdpi.com/special_issues_guidelines. The Editorial Office and Editor-in-Chief of JPM has approved this and MDPI’s standard manuscript editorial processing procedure (https://www.mdpi.com/editorial_process) will be applied to all submissions. As per our standard procedure, Guest Editors are excluded from participating in the editorial process for their submission and/or for submissions from persons with whom a potential conflict of interest may exist. More details on MDPI’s Conflict of Interest policy for reviewers and editors can be found here: https://www.mdpi.com/ethics#_bookmark22.

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

5P medicine
transformed health ecosystems
systems integration and interoperability
knowledge representation and management

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Published Papers (12 papers)

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Editorial

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3 pages, 152 KiB

Open AccessEditorial

Selected Papers from the pHealth 2022 Conference, Oslo, Norway, 8–10 November 2022

by Bernd Blobel

J. Pers. Med. 2024, 14(9), 947; https://doi.org/10.3390/jpm14090947 - 6 Sep 2024

Viewed by 604

Abstract

This Special Issue of the Journal of Personalized Medicine presents extended versions of selected contributions to pHealth 2022, the 19th International Conference on Wearable Micro and Nano Technologies for Personalized Health, held on 8–10 November 2022 in Oslo, Norway [...] Full article

(This article belongs to the Special Issue Selected Papers from the pHealth 2022 Conference, Oslo, Norway, 8–10 November 2022)

Research

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14 pages, 3543 KiB

Open AccessArticle

Transforming Ontology Web Language Elements into Common Terminology Service 2 Terminology Resources

by Sara Mora, Roberta Gazzarata, Bernd Blobel, Ylenia Murgia and Mauro Giacomini

J. Pers. Med. 2024, 14(7), 676; https://doi.org/10.3390/jpm14070676 - 24 Jun 2024

Cited by 1 | Viewed by 994

Abstract

Communication and cooperation are fundamental for the correct deployment of P5 medicine, and this can be achieved only by correct comprehension of semantics so that it can aspire to medical knowledge sharing. There is a hierarchy in the operations that need to be performed to achieve this goal that brings to the forefront the complete understanding of the real-world business system by domain experts using Domain Ontologies, and only in the last instance acknowledges the specific transformation at the pure information and communication technology level. A specific feature that should be maintained during such types of transformations is versioning that aims to record the evolution of meanings in time as well as the management of their historical evolution. The main tool used to represent ontology in computing environments is the Ontology Web Language (OWL), but it was not created for managing the evolution of meanings in time. Therefore, we tried, in this paper, to find a way to use the specific features of Common Terminology Service—Release 2 (CTS2) to perform consistent and validated transformations of ontologies written in OWL. The specific use case managed in the paper is the Alzheimer’s Disease Ontology (ADO). We were able to consider all of the elements of ADO and map them with CTS2 terminological resources, except for a subset of elements such as the equivalent class derived from restrictions on other classes. Full article

(This article belongs to the Special Issue Selected Papers from the pHealth 2022 Conference, Oslo, Norway, 8–10 November 2022)

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14 pages, 2605 KiB

Open AccessArticle

Analysis of Prevalence and Clinical Features of Aortic Stenosis in Patients with and without Bicuspid Aortic Valve Using Machine Learning Methods

by Olga Irtyuga, Mary Babakekhyan, Anna Kostareva, Vladimir Uspensky, Michail Gordeev, Giuseppe Faggian, Anna Malashicheva, Oleg Metsker, Evgeny Shlyakhto and Georgy Kopanitsa

J. Pers. Med. 2023, 13(11), 1588; https://doi.org/10.3390/jpm13111588 - 9 Nov 2023

Cited by 3 | Viewed by 1177

Abstract

Aortic stenosis (AS) is the most commonly diagnosed valvular heart disease, and its prevalence increases with the aging of the general population. However, AS is often diagnosed at a severe stage, necessitating surgical treatment, due to its long asymptomatic period. The objective of this study was to analyze the frequency of AS in a population of cardiovascular patients using echocardiography (ECHO) and to identify clinical factors and features associated with these patient groups. We utilized machine learning methods to analyze 84,851 echocardiograms performed between 2010 and 2018 at the National Medical Research Center named after V.A. Almazov. The primary indications for ECHO were coronary artery disease (CAD) and hypertension (HP), accounting for 33.5% and 14.2% of the cases, respectively. The frequency of AS was found to be 13.26% among the patients (n = 11,252). Within our study, 1544 patients had a bicuspid aortic valve (BAV), while 83,316 patients had a tricuspid aortic valve (TAV). BAV patients were observed to be younger compared to TAV patients. AS was more prevalent in the BAV group (59%) compared to the TAV group (12%), with a p-value of <0.0001. By employing a machine learning algorithm, we randomly identified significant features present in AS patients, including age, hypertension (HP), aortic regurgitation (AR), ascending aortic dilatation (AscAD), and BAV. These findings could serve as additional indications for earlier observation and more frequent ECHO in specific patient groups for the earlier detection of developing AS. Full article

(This article belongs to the Special Issue Selected Papers from the pHealth 2022 Conference, Oslo, Norway, 8–10 November 2022)

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23 pages, 5557 KiB

Open AccessArticle

Principles and Standards for Designing and Managing Integrable and Interoperable Transformed Health Ecosystems

by Bernd Blobel, Pekka Ruotsalainen, Frank Oemig, Mauro Giacomini, Pier Angelo Sottile and Frederik Endsleff

J. Pers. Med. 2023, 13(11), 1579; https://doi.org/10.3390/jpm13111579 - 4 Nov 2023

Cited by 9 | Viewed by 1830

Abstract

The advancement of sciences and technologies, economic challenges, increasing expectations, and consumerism result in a radical transformation of health and social care around the globe, characterized by foundational organizational, methodological, and technological paradigm changes. The transformation of the health and social care ecosystems aims at ubiquitously providing personalized, preventive, predictive, participative precision (5P) medicine, considering and understanding the individual’s health status in a comprehensive context from the elementary particle up to society. For designing and implementing such advanced ecosystems, an understanding and correct representation of the structure, function, and relations of their components is inevitable, thereby including the perspectives, principles, and methodologies of all included disciplines. To guarantee consistent and conformant processes and outcomes, the specifications and principles must be based on international standards. A core standard for representing transformed health ecosystems and managing the integration and interoperability of systems, components, specifications, and artifacts is ISO 23903:2021, therefore playing a central role in this publication. Consequently, ISO/TC 215 and CEN/TC 251, both representing the international standardization on health informatics, declared the deployment of ISO 23903:2021 mandatory for all their projects and standards addressing more than one domain. The paper summarizes and concludes the first author’s leading engagement in the evolution of pHealth in Europe and beyond over the last 15 years, discussing the concepts, principles, and standards for designing, implementing, and managing 5P medicine ecosystems. It not only introduces the theoretical foundations of the approach but also exemplifies its deployment in practical projects and solutions regarding interoperability and integration in multi-domain ecosystems. The presented approach enables comprehensive and consistent integration of and interoperability between domains, systems, related actors, specifications, standards, and solutions. That way, it should help overcome the problems and limitations of data-centric approaches, which still dominate projects and products nowadays, and replace them with knowledge-centric, comprehensive, and consistent ones. Full article

(This article belongs to the Special Issue Selected Papers from the pHealth 2022 Conference, Oslo, Norway, 8–10 November 2022)

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14 pages, 1963 KiB

Open AccessArticle

Assessing the Potential Risks of Digital Therapeutics (DTX): The DTX Risk Assessment Canvas

by Kerstin Denecke, Richard May, Elia Gabarron and Guillermo H. Lopez-Campos

J. Pers. Med. 2023, 13(10), 1523; https://doi.org/10.3390/jpm13101523 - 23 Oct 2023

Cited by 4 | Viewed by 2778

Abstract

Motivation: Digital therapeutics (DTX), i.e., health interventions that are provided through digital means, are increasingly available for use; in some countries, physicians can even prescribe selected DTX following a reimbursement by health insurances. This results in an increasing need for methodologies to consider and monitor DTX’s negative consequences, their risks to patient safety, and possible adverse events. However, it is completely unknown which aspects should be subject to surveillance given the missing experiences with the tools and their negative impacts. Objective: Our aim is to develop a tool—the DTX Risk Assessment Canvas—that enables researchers, developers, and practitioners to reflect on the negative consequences of DTX in a participatory process. Method: Taking the well-established business model canvas as a starting point, we identified relevant aspects to be considered in a risk assessment of a DTX. The aspects or building blocks of the canvas were constructed in a two-way process: first, we defined the aspects relevant for discussing and reflecting on how a DTX might bring negative consequences and risks for its users by considering ISO/TS 82304-2, the scientific literature, and by reviewing existing DTX and their listed adverse effects. The resulting aspects were grouped into thematic blocks and the canvas was created. Second, six experts in health informatics and mental health provided feedback and tested the understandability of the initial canvas by individually applying it to a DTX of their choice. Based on their feedback, the canvas was modified. Results: The DTX Risk Assessment Canvas is organized into 15 thematic blocks which are in turn grouped into three thematic groups considering the DTX itself, the users of the DTX, and the effects of the DTX. For each thematic block, questions have been formulated to guide the user of the canvas in reflecting on the single aspects. Conclusions: The DTX Risk Assessment Canvas is a tool to reflect the negative consequences and risks of a DTX by discussing different thematic blocks that together constitute a comprehensive interpretation of a DTX regarding possible risks. Applied during the DTX design and development phase, it can help in implementing countermeasures for mitigation or means for their monitoring. Full article

(This article belongs to the Special Issue Selected Papers from the pHealth 2022 Conference, Oslo, Norway, 8–10 November 2022)

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20 pages, 5842 KiB

Open AccessArticle

Multidimensional Machine Learning Model to Calculate a COVID-19 Vulnerability Index

by Paula Andrea Rosero Perez, Juan Sebastián Realpe Gonzalez, Ricardo Salazar-Cabrera, David Restrepo, Diego M. López and Bernd Blobel

J. Pers. Med. 2023, 13(7), 1141; https://doi.org/10.3390/jpm13071141 - 15 Jul 2023

Cited by 2 | Viewed by 1471

Abstract

In Colombia, the first case of COVID-19 was confirmed on 6 March 2020. On 13 March 2023, Colombia registered 6,360,780 confirmed positive cases of COVID-19, representing 12.18% of the total population. The National Administrative Department of Statistics (DANE) in Colombia published in 2020 a COVID-19 vulnerability index, which estimates the vulnerability (per city block) of being infected with COVID-19. Unfortunately, DANE did not consider multiple factors that could increase the risk of COVID-19 (in addition to demographic and health), such as environmental and mobility data (found in the related literature). The proposed multidimensional index considers variables of different types (unemployment rate, gross domestic product, citizens’ mobility, vaccination data, and climatological and spatial information) in which the incidence of COVID-19 is calculated and compared with the incidence of the COVID-19 vulnerability index provided by DANE. The collection, data preparation, modeling, and evaluation phases of the Cross-Industry Standard Process for Data Mining methodology (CRISP-DM) were considered for constructing the index. The multidimensional index was evaluated using multiple machine learning models to calculate the incidence of COVID-19 cases in the main cities of Colombia. The results showed that the best-performing model to predict the incidence of COVID-19 in Colombia is the Extra Trees Regressor algorithm, obtaining an R-squared of 0.829. This work is the first step toward a multidimensional analysis of COVID-19 risk factors, which has the potential to support decision making in public health programs. The results are also relevant for calculating vulnerability indexes for other viral diseases, such as dengue. Full article

(This article belongs to the Special Issue Selected Papers from the pHealth 2022 Conference, Oslo, Norway, 8–10 November 2022)

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18 pages, 843 KiB

Open AccessArticle

Future pHealth Ecosystem-Holistic View on Privacy and Trust

by Pekka Ruotsalainen and Bernd Blobel

J. Pers. Med. 2023, 13(7), 1048; https://doi.org/10.3390/jpm13071048 - 26 Jun 2023

Cited by 1 | Viewed by 1428

Abstract

Modern pHealth is an emerging approach to collecting and using personal health information (PHI) for personalized healthcare and personalized health management. For its products and services, it deploys advanced technologies such as sensors, actuators, computers, mobile phones, etc. Researchers have shown that today’s networked information systems, such as pHealth ecosystems, miss appropriate privacy solutions, and trust is only an illusion. In the future, the situation will be even more challenging because pHealth ecosystems will be highly distributed, dynamic, increasingly autonomous, and multi-stakeholder, with the ability to monitor the person’s regular life, movements, emotions, and health-related behavior in real time. In this paper, the authors demonstrate that privacy and trust in ecosystems are system-level problems that need a holistic, system-focused solution. To make future pHealth ethically acceptable, privacy-enabled, and trustworthy, the authors have developed a conceptual five-level privacy and trust model as well as a formula that describes the impact of privacy and trust factors on the level of privacy and trust. Furthermore, the authors have analyzed privacy and trust challenges and possible solutions at each level of the model. Based on the analysis performed, a proposal for future ethically acceptable, trustworthy, and privacy-enabled pHealth is developed. The solution combines privacy as personal property and trust as legally binding fiducial duty approaches and uses a blockchain-based smart contract agreement to store people’s privacy and trust requirements and service providers’ promises. Full article

(This article belongs to the Special Issue Selected Papers from the pHealth 2022 Conference, Oslo, Norway, 8–10 November 2022)

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17 pages, 2669 KiB

Open AccessArticle

HL7-FHIR-Based ContSys Formal Ontology for Enabling Continuity of Care Data Interoperability

by Subhashis Das and Pamela Hussey

J. Pers. Med. 2023, 13(7), 1024; https://doi.org/10.3390/jpm13071024 - 21 Jun 2023

Cited by 5 | Viewed by 2478

Abstract

The rapid advancement of digital technologies and recent global pandemic-like scenarios have pressed our society to reform and adapt health and social care toward personalizing the home care setting. This transformation assists in avoiding treatment in crowded secondary health care facilities and improves the experience and impact on both healthcare professionals and service users alike. The interoperability challenge through standards-based roadmaps is the lynchpin toward enabling the efficient interconnection between health and social care services. Hence, facilitating safe and trustworthy data workflow from one healthcare system to another is a crucial aspect of the communication process. In this paper, we showcase a methodology as to how we can extract, transform and load data in a semi-automated process using a common semantic standardized data model (CSSDM) to generate a personalized healthcare knowledge graph (KG). CSSDM is based on a formal ontology of ISO 13940:2015 ContSys for conceptual grounding and FHIR-based specification to accommodate structural attributes to generate KG. The goal of CSSDM is to offer an alternative pathway to discuss interoperability by supporting a unique collaboration between a company creating a health information system and a cloud-enabled health service. The resulting pathway of communication provides access to multiple stakeholders for sharing high-quality data and information. Full article

(This article belongs to the Special Issue Selected Papers from the pHealth 2022 Conference, Oslo, Norway, 8–10 November 2022)

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12 pages, 4194 KiB

Open AccessArticle

Machine Learning Methods for Pregnancy and Childbirth Risk Management

by Georgy Kopanitsa, Oleg Metsker and Sergey Kovalchuk

J. Pers. Med. 2023, 13(6), 975; https://doi.org/10.3390/jpm13060975 - 10 Jun 2023

Cited by 4 | Viewed by 1697

Abstract

Machine learning methods enable medical systems to automatically generate data-driven decision support models using real-world data inputs, eliminating the need for explicit rule design. In this research, we investigated the application of machine learning methods in healthcare, specifically focusing on pregnancy and childbirth risks. The timely identification of risk factors during early pregnancy, along with risk management, mitigation, prevention, and adherence management, can significantly reduce adverse perinatal outcomes and complications for both mother and child. Given the existing burden on medical professionals, clinical decision support systems (CDSSs) can play a role in risk management. However, these systems require high-quality decision support models based on validated medical data that are also clinically interpretable. To develop models for predicting childbirth risks and due dates, we conducted a retrospective analysis of electronic health records from the perinatal Center of the Almazov Specialized Medical Center in Saint-Petersburg, Russia. The dataset, which was exported from the medical information system, consisted of structured and semi-structured data, encompassing a total of 73,115 lines for 12,989 female patients. Our proposed approach, which includes a detailed analysis of predictive model performance and interpretability, offers numerous opportunities for decision support in perinatal care provision. The high predictive performance achieved by our models ensures precise support for both individual patient care and overall health organization management. Full article

(This article belongs to the Special Issue Selected Papers from the pHealth 2022 Conference, Oslo, Norway, 8–10 November 2022)

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32 pages, 10714 KiB

Open AccessArticle

Using EfficientNet-B7 (CNN), Variational Auto Encoder (VAE) and Siamese Twins’ Networks to Evaluate Human Exercises as Super Objects in a TSSCI Images

by Yoram Segal, Ofer Hadar and Lenka Lhotska

J. Pers. Med. 2023, 13(5), 874; https://doi.org/10.3390/jpm13050874 - 22 May 2023

Cited by 5 | Viewed by 2986

Abstract

In this article, we introduce a new approach to human movement by defining the movement as a static super object represented by a single two-dimensional image. The described method is applicable in remote healthcare applications, such as physiotherapeutic exercises. It allows researchers to label and describe the entire exercise as a standalone object, isolated from the reference video. This approach allows us to perform various tasks, including detecting similar movements in a video, measuring and comparing movements, generating new similar movements, and defining choreography by controlling specific parameters in the human body skeleton. As a result of the presented approach, we can eliminate the need to label images manually, disregard the problem of finding the start and the end of an exercise, overcome synchronization issues between movements, and perform any deep learning network-based operation that processes super objects in images in general. As part of this article, we will demonstrate two application use cases: one illustrates how to verify and score a fitness exercise. In contrast, the other illustrates how to generate similar movements in the human skeleton space by addressing the challenge of supplying sufficient training data for deep learning applications (DL). A variational auto encoder (VAE) simulator and an EfficientNet-B7 classifier architecture embedded within a Siamese twin neural network are presented in this paper in order to demonstrate the two use cases. These use cases demonstrate the versatility of our innovative concept in measuring, categorizing, inferring human behavior, and generating gestures for other researchers. Full article

(This article belongs to the Special Issue Selected Papers from the pHealth 2022 Conference, Oslo, Norway, 8–10 November 2022)

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Review

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44 pages, 1953 KiB

Open AccessReview

Patient-Generated Health Data (PGHD): Understanding, Requirements, Challenges, and Existing Techniques for Data Security and Privacy

by Pankaj Khatiwada, Bian Yang, Jia-Chun Lin and Bernd Blobel

J. Pers. Med. 2024, 14(3), 282; https://doi.org/10.3390/jpm14030282 - 3 Mar 2024

Cited by 8 | Viewed by 6027

Abstract

The evolution of Patient-Generated Health Data (PGHD) represents a major shift in healthcare, fueled by technological progress. The advent of PGHD, with technologies such as wearable devices and home monitoring systems, extends data collection beyond clinical environments, enabling continuous monitoring and patient engagement in their health management. Despite the growing prevalence of PGHD, there is a lack of clear understanding among stakeholders about its meaning, along with concerns about data security, privacy, and accuracy. This article aims to thoroughly review and clarify PGHD by examining its origins, types, technological foundations, and the challenges it faces, especially in terms of privacy and security regulations. The review emphasizes the role of PGHD in transforming healthcare through patient-centric approaches, their understanding, and personalized care, while also exploring emerging technologies and addressing data privacy and security issues, offering a comprehensive perspective on the current state and future directions of PGHD. The methodology employed for this review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and Rayyan, AI-Powered Tool for Systematic Literature Reviews. This approach ensures a systematic and comprehensive coverage of the available literature on PGHD, focusing on the various aspects outlined in the objective. The review encompassed 36 peer-reviewed articles from various esteemed publishers and databases, reflecting a diverse range of methodologies, including interviews, regular articles, review articles, and empirical studies to address three RQs exploratory, impact assessment, and solution-oriented questions related to PGHD. Additionally, to address the future-oriented fourth RQ for PGHD not covered in the above review, we have incorporated existing domain knowledge articles. This inclusion aims to provide answers encompassing both basic and advanced security measures for PGHD, thereby enhancing the depth and scope of our analysis. Full article

(This article belongs to the Special Issue Selected Papers from the pHealth 2022 Conference, Oslo, Norway, 8–10 November 2022)

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Other

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11 pages, 2507 KiB

Open AccessBrief Report

Challenges and Strategies for Enhancing eHealth Capacity Building Programs in African Nations

by Flora Nah Asah and Jens Johan Kaasbøll

J. Pers. Med. 2023, 13(10), 1463; https://doi.org/10.3390/jpm13101463 - 5 Oct 2023

Cited by 3 | Viewed by 2157

Abstract

eHealth applications play a crucial role in achieving Universal Health Coverage. (1) Background: To ensure successful integration and use, particularly in developing and low/middle-income countries (LMIC), it is vital to have skilled healthcare personnel. The purpose of this study was to describe challenges that hinder capacity-building initiatives among healthcare personnel in developing and LMIC and suggest interventions to mitigate them. (2) Methods: Adopted a descriptive research design and gathered empirical data through an online survey from 37 organizations. (3) Results: The study found that in developing and LMIC, policymakers and eHealth specialists face numerous obstacles integrating and using eHealth including limited training opportunities. These obstacles include insufficient funds, inadequate infrastructure, poor leadership, and governance, which are specific to each context. The study suggests implementing continuous in-service training, computer-based systems, and academic modules to address these challenges. Additionally, the importance of having solid and appropriate eHealth policies and committed leaders were emphasized. (4) Conclusions: These findings are consistent with previous research and highlight the need for practical interventions to enhance eHealth capacity-building in LMICs. However, it should be noted that the data was collected only from BETTEReHEALTH partners. Therefore, the results only represent their respective organizations and cannot be generalized to the larger population. Full article

(This article belongs to the Special Issue Selected Papers from the pHealth 2022 Conference, Oslo, Norway, 8–10 November 2022)

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