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Healthcare Goes Digital: Mobile Health and Electronic Health Technology in the 21st Century: Second Edition

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A special issue of Healthcare (ISSN 2227-9032).

Deadline for manuscript submissions: closed (15 April 2026) | Viewed by 14741

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Dr. Daniele Giansanti

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Centre Tisp, Istituto Superiore di Sanità, 00161 Rome, Italy
Interests: biomedical engineering; robotics; artificial intelligence; digital health; rehabilitation; smart technology; cybersecurity; mental health; animal-assisted therapy; social robotics; acceptance; diagnostic pathology and radiology; medical imaging; patient safety; healthcare quality; health assessment; chronic disease
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Dear Colleagues,

After the success of the Special Issue The 10th Anniversary of Healthcare—TeleHealth and Digital Healthcare, I invite you to contribute to this Special Issue, Healthcare Goes Digital: Mobile Health and Electronic Health Technology in the 21st Century: Second Edition.

In an era of rapid technological advancement, healthcare is experiencing a profound shift toward digital solutions, including the integration of artificial intelligence (AI) and conversational agents such as ChatGPT.

This Special Issue has the following aims:

Explore the convergence of mobile health (mHealth), electronic health technology (eHealth) and AI in the 21st century; we invite contributions that delve into innovative applications, challenges and future prospects in these domains.
To foster a comprehensive dialogue on the evolving landscape of digital healthcare, specifically in regards to eHealth and mHealth, also recognizing the role of AI and conversational agents such as ChatGPT. Researchers, practitioners and experts are encouraged to contribute their perspectives to shape the future of healthcare in the digital age.

Topics of interest:

Mobile Health Applications: Assessing the impact of mobile apps on healthcare delivery.
Wearable Health Technology: Exploring the role of wearables in monitoring and managing health.
Telemedicine: Analyzing the evolution and effectiveness of telehealth services, with an emphasis on AI integration.
Electronic Health Records (EHRs): Examining the integration and implications of EHR systems with AI-driven analytics.
Data Security and Privacy: Addressing concerns and solutions in the digital healthcare space, particularly in AI-driven applications.
Patient Engagement: Investigating strategies to enhance patient involvement through digital platforms, including AI-driven chat interfaces.
Emerging Technologies: Exploring the influence of AI, IoT and other emerging tech in healthcare, with a specific focus on ChatGPT and conversational AI.

Prof. Dr. Daniele Giansanti
Guest Editor

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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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access semimonthly 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 2700 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

telemedicine
eHealth
mHealth

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Related Special Issue

Healthcare Goes Digital: Mobile Health and Electronic Health Technology in the 21st Century in Healthcare (14 articles)

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Editorial

Jump to: Research, Other

4 pages, 154 KB

Open AccessEditorial

Healthcare Goes Digital: mHealth, eHealth, Artificial Intelligence, and Emerging Digital Technologies Within Digital Health Transformation

by Daniele Giansanti

Healthcare 2026, 14(9), 1173; https://doi.org/10.3390/healthcare14091173 - 28 Apr 2026

Viewed by 837

Abstract

Following the success of the first Special Issue, “The 10th Anniversary of Healthcare—TeleHealth and Digital Healthcare” [...] Full article

(This article belongs to the Special Issue Healthcare Goes Digital: Mobile Health and Electronic Health Technology in the 21st Century: Second Edition)

Research

Jump to: Editorial, Other

21 pages, 924 KB

Open AccessArticle

Acceptance of Medical History-Taking Supported by Artificial Intelligence and Chatbots: A Population-Based Survey in Germany

by Sonja Haug, Edda Currle and Karsten Weber

Healthcare 2026, 14(7), 905; https://doi.org/10.3390/healthcare14070905 - 31 Mar 2026

Viewed by 859

Abstract

Background/Objectives: Digital anamnesis tools, including chatbots, are increasingly being developed and evaluated, yet their implementation in German medical practices remains limited. This study examines the acceptance of medical history-taking assisted by artificial intelligence (AI) among the German population. The objective is to derive implications for integrating such systems into digitalization strategies of medical practices. Methods: This study is based on an online survey of the German population, aged between 18 and 74 years, conducted in two independent cross-sectional waves (trend design) in 2024 and 2025 with n = 1000 respondents in each year. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), three hypotheses regarding the use of AI in medical history-taking were developed and tested using linear regression models. Results: Both waves reveal a high acceptance level of AI-supported anamnesis systems for people aged between 18 and 74, regardless of whether a chatbot is used in medical practice (Scenario 1) or at home (Scenario 2). The latter received slightly less approval for the intention to use (mean intention scores: 3.50 and 3.45, range from 1.0 to 5.0) than Scenario 1 (3.59, 3.56). The indices of Performance Expectancy (PE), Effort Expectancy (EE), and perceived Social Influence (SI) determine the intention to use a chatbot with the strongest correlation of the PE index (Scenario 1: ß =0.466, Scenario 2: ß = 0.475). Most respondents (73% and 75%) expressed a favorable opinion for digitally storing medical history data within their electronic health record (EHR). Conclusions: The findings suggest that gender- and age-specific differentiation—aside from considering the needs of older adults—may be less relevant for designing digitalization strategies than previously assumed. Instead, the focus of medical practices should lie on the practicability of the tool used. Despite currently low EHR utilization rates in Germany, medical practices may expect broad patient approval regarding the digital storage of medical history data. Full article

(This article belongs to the Special Issue Healthcare Goes Digital: Mobile Health and Electronic Health Technology in the 21st Century: Second Edition)

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15 pages, 673 KB

Open AccessArticle

Democratizing Specialized Care in the Digital Age: Project ECHO as a Learning Environment for Continuing Professional Development

by Ilian Cruz-Panesso, Lucie Fuzeau, Brenda Lécuyer and Mélanie Demers

Healthcare 2026, 14(7), 824; https://doi.org/10.3390/healthcare14070824 - 24 Mar 2026

Viewed by 729

Abstract

Background: Digital health technologies have reshaped continuing professional development (CPD) in healthcare. However, learning in digitally mediated programs is often assumed rather than explicitly designed and assessed. Project ECHO^® (Extension for Community Healthcare Outcomes), a globally implemented telementoring model, expands access to specialized expertise through videoconferencing-based, case-oriented learning. While prior literature has documented program reach, implementation, and clinical outcomes, comparatively less attention has been paid to the interactional mechanisms through which learning unfolds within ECHO sessions. Objectives: This article conceptualizes Project ECHO as a structured learning environment and proposes a theoretically grounded framework for examining and assessing learning processes in digital CPD. Methods: Using situated learning, communities of practice, and cognitive apprenticeship as analytical lenses, this conceptual analysis examines participation structures, distributed expertise, facilitation practices, and case-based dialogue in ECHO sessions. Principles of constructive alignment inform a process-oriented assessment approach aligned with CPD evaluation models such as Moore’s framework. Conceptual framework: This article develops a theory-informed framework that conceptualizes Project ECHO as a structured learning architecture for digital continuing professional development. The framework identifies how participation, distributed expertise, facilitation, and case-based dialogue support learning processes during ECHO sessions. It also proposes process-oriented indicators to make learning dynamics more visible alongside outcome-based evaluation approaches. Conclusions: By foregrounding learning processes, this analysis offers a conceptual foundation to strengthening pedagogical alignment, faculty development, and assessment design in ECHO programs. The framework contributes to digital CPD scholarship by clarifying how learning develops within telementoring environments and by guiding future research and program refinement. More specifically, the article contributes a process-oriented evaluation perspective that helps make learning quality more visible within telementoring environments, thereby complementing dominant outcome-focused CPD models. Full article

(This article belongs to the Special Issue Healthcare Goes Digital: Mobile Health and Electronic Health Technology in the 21st Century: Second Edition)

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12 pages, 1531 KB

Open AccessArticle

Digitalization of Comprehensive Geriatric Assessments for Nursing Practice: A Feasibility and Proof-of-Concept Study Toward Nursing Home Implementation

by Uijin Park, Midori Miyagi, Xinze Wu, Makoto Ito, Manabu Chikai, Fuminori Sakai, Tomofumi Miura, Hiroshi Sato, Akihiko Murai, Shannon Freeman and Satoru Ebihara

Healthcare 2026, 14(4), 528; https://doi.org/10.3390/healthcare14040528 - 19 Feb 2026

Cited by 1 | Viewed by 916

Abstract

Background/Objectives: Comprehensive Geriatric Assessment (CGA) is essential for maintaining quality of life (QOL) and independence in older adults. Still, its implementation is labor-intensive and difficult to sustain in aging societies such as Japan. Digital technologies may enable continuous, scalable CGA in daily living environments. This study aimed to develop and preliminarily evaluate a digital CGA (D-CGA) framework by integrating data from multiple monitoring devices, as a preparatory step toward Artificial Intelligence (AI)-supported personalized care planning. Methods: Four devices (Handy, Apple Watch, Withings Sleep, and Vieureka) were selected. Due to ethical constraints in Japan, a pilot study was conducted with graduate students. Participants underwent continuous monitoring for five weekdays. Common and device-specific measurement items were extracted, visualized, and compared across devices. Heart rate data were examined using correlation-based analyses. Baseline CGA was conducted before monitoring. Results: Distributional and temporal characteristics of physiological measures were explored separately for daytime and nocturnal periods. Continuous heart rate and respiratory rate data were successfully collected across monitoring days, demonstrating the feasibility of real-life data acquisition using the selected devices. Heart and respiratory rates showed distinct distributional patterns between daytime and nocturnal periods, supporting context-specific physiological characterization. Conclusions: This pilot study demonstrates the feasibility of integrating multi-device data for D-CGA and provides foundational reference data for future studies of older adults. The results support the potential of D-CGA to inform personalized care and guide subsequent large-scale and clinical investigations. Full article

(This article belongs to the Special Issue Healthcare Goes Digital: Mobile Health and Electronic Health Technology in the 21st Century: Second Edition)

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14 pages, 711 KB

Open AccessArticle

CognoStroke: Automated Cognitive and Mood Assessment on the Hyper-Acute Stroke Unit

by Simon M. Bell, Bahman Mirheidari, Kirsty A. C. Harkness, Emma Richards, Mary Sikaonga, Madalina Roman, Jonathan Gardner, India Lunn, Isabela Ramnarine, Udit Gupta, Hamish Patel, Larissa Chapman, Katie Raine, Caitlin Illingworth, Dorota Braun, Heidi Christensen and Daniel J. Blackburn

Healthcare 2025, 13(22), 2885; https://doi.org/10.3390/healthcare13222885 - 13 Nov 2025

Cited by 1 | Viewed by 998

Abstract

Background: Cognitive and mood impairments are common in Stroke Survivors (SSs), leading to worse outcomes and poorer quality of life measures. Current methods of assessment of mood and cognitive performance are time consuming and rely on health care professionals. This makes assessment in hyper-acute stroke units (HASU) difficult. Here we describe the use of CognoStroke, an automated assessment of mood and cognitive impairment in the HASU. Methods: Using conversational interaction delivered through a virtual, web-based agent (CognosStroke), speech analysis was performed using three large language models (GPT2, Facebook.BART-based, and RobERTa-base) to classify thresholds levels of MoCA (threshold: 22,23,24,25,26), GAD-7 (above 5 and 10), and PHQ-9 (above 5 and 10). Results are presented as Macro F1-scores (MFSs). Patients were asked about barriers to using CogonStroke. Results: A total of 151 SSs agreed to perform CognoStroke, with 75 completing the full assessment. The best MFS of 0.723 was achieved using CognoStroke for thresholding a MoCA of 26. The MFS improved further to 0.783 when single prompts or a smaller combination of prompts from the CognoStroke bank were used. For the PHQ-9 a MFS of 0.686 was achieved thresholding above 10 and on the GAD-7 a MFS of 0.617 was achieved for thresholding above 5. Single prompts or smaller prompt combinations again achieved higher MFSs. Discussion: CognoStroke has potential to classify SSs into groups with high or low cognitive and mood thresholds, highlighting benefits for improving post-stroke cognitive assessment. Challenges of automated assessment on the HASU include patient computer access, anxiety in using technology, post-stroke fatigue, and computer literacy. Full article

(This article belongs to the Special Issue Healthcare Goes Digital: Mobile Health and Electronic Health Technology in the 21st Century: Second Edition)

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14 pages, 470 KB

Open AccessArticle

Effects of an mHealth Occupational Therapy Intervention on Functional Performance: A Pilot Study

by Irene Pérez-Díaz, Mario Arnáiz-González and Estíbaliz Jiménez-Arberas

Healthcare 2025, 13(16), 2015; https://doi.org/10.3390/healthcare13162015 - 15 Aug 2025

Viewed by 1875

Abstract

Neurodevelopmental disorders are one of the most prevalent conditions today, and among the limitations in activity and restrictions in the participation of children and their families, we find intervention in activities of daily living; therefore, research focused on outcome measurement is one of the most active lines, and after COVID-19, telerehabilitation has garnered special interest. Background/Objectives: The study objective was to evaluate the effectiveness of a mobile health (mHealth) application in improving the performance of activities of daily living in children with neurodevelopmental disorders. Methods: The study employed a quasi-experimental design with a control group, using a fully remote mHealth-based intervention. The instruments used were a sociodemographic ad hoc, Pediatric Evaluation of Disability Inventory Computer, Family Outcomes Survey, Family Confidence Scale, and System Usability Scale. The final sample consisted of 13 participants. Results: The mHealth intervention showed significant improvements in occupational performance in the experimental group, especially in the global score and in the Responsibility dimension of the PEDI-CAT. No relevant differences were observed in the CON-FAN and FOS scales between groups, although the latter showed improvements over time. The usability of the app was rated positively (SUS = 69.75). Conclusions: The developed application presents good usability for families of children with neurodevelopmental disorders, but to obtain better outcome measures, the intervention should combine face-to-face sessions and the use of mHealth, as well as employing the family-centered model. Full article

(This article belongs to the Special Issue Healthcare Goes Digital: Mobile Health and Electronic Health Technology in the 21st Century: Second Edition)

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16 pages, 351 KB

Open AccessArticle

Assessment of Telehealth Literacy in Users: Survey and Analysis of Demographic and Behavioral Determinants

by Marcela Hechenleitner-Carvallo, Jacqueline Ibarra-Peso and Sergio V. Flores

Healthcare 2025, 13(15), 1825; https://doi.org/10.3390/healthcare13151825 - 26 Jul 2025

Viewed by 1705

Abstract

Background: Telehealth is an essential component of modern healthcare, and it was especially relevant during the COVID-19 pandemic, but disparities in digital and technological literacy among health professionals may limit its equitable adoption and impact. Objective: This study seeks to validate an eight-item telehealth literacy survey among health professionals in Central–South Chile and to examine demographic and behavioral determinants of literacy levels, developing predictive models to identify key factors. Methods: In this cross-sectional study, 2182 health professionals from urban and rural centers in Central–South Chile completed the adapted survey along with questions on age, gender, nationality, and frequency of telehealth use. We assessed internal consistency (Cronbach’s α), explored factor structure via exploratory factor analysis (EFA), and tested associations using Pearson correlations, t-tests, one-way ANOVA, and both linear and multinomial logistic regressions. Results: The instrument demonstrated high reliability (Cronbach’s α = 0.92) and a two-factor structure explaining 65% of variance. Age negatively correlated with literacy (r = −0.26; p < 0.001), while the frequency of telehealth use showed a positive correlation (r = 0.26; p < 0.001). Female professionals and those in urban settings scored significantly higher on telehealth literacy (p = 0.005 and p < 0.001, respectively). The reduced multinomial model achieved moderate classification accuracy (51.65%) in distinguishing low, medium, and high literacy groups. Conclusions: The validated survey is a reliable tool for assessing telehealth literacy among health professionals in Chile. The findings highlight age, gender, and geographic disparities, and support targeted digital literacy interventions to promote equitable telehealth practice. Full article

(This article belongs to the Special Issue Healthcare Goes Digital: Mobile Health and Electronic Health Technology in the 21st Century: Second Edition)

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28 pages, 776 KB

Open AccessArticle

Surveying the Digital Cytology Workflow in Italy: An Initial Report on AI Integration Across Key Professional Roles

by Daniele Giansanti, Elisabetta Carico, Andrea Lastrucci and Enrico Giarnieri

Healthcare 2025, 13(8), 903; https://doi.org/10.3390/healthcare13080903 - 14 Apr 2025

Cited by 3 | Viewed by 1266

Abstract

Background: The integration of artificial intelligence (AI) in healthcare, particularly in digital cytology, has the potential to enhance diagnostic accuracy and workflow efficiency. However, AI adoption remains limited due to technological and human-related barriers. Understanding the perceptions and experiences of healthcare professionals is essential for overcoming these challenges and facilitating effective AI implementation. Objectives: This study aimed to assess AI integration in digital cytology workflows by evaluating professionals’ perspectives on its benefits, challenges, and requirements for successful adoption. Methods: A survey was conducted among 150 professionals working in public and private healthcare settings in Italy, including laboratory technicians (35%), medical doctors (25%), biologists (20%), and specialists in diagnostic technical sciences (20%). Data were collected through a structured Computer-Assisted Web Interview (CAWI) and a Virtual Focus Group (VFG) to capture quantitative and qualitative insights on AI familiarity, perceived advantages, and barriers to adoption. Results: The findings indicated varying levels of AI familiarity among professionals. While many recognized AI’s potential to improve diagnostic accuracy and streamline workflows, concerns were raised regarding resistance to change, implementation costs, and doubts about AI reliability. Participants emphasized the need for structured training and continuous support to facilitate AI adoption in digital cytology. Conclusions: Addressing barriers such as resistance, cost, and trust is essential for the successful integration of AI in digital cytology workflows. Tailored training programs and ongoing professional support can enhance AI adoption, ultimately optimizing diagnostic processes and improving clinical outcomes. Full article

(This article belongs to the Special Issue Healthcare Goes Digital: Mobile Health and Electronic Health Technology in the 21st Century: Second Edition)

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Other

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14 pages, 286 KB

Open AccessProtocol

Home-Based, Telematic Gradual Exercise for Permanent Colostomy Patients: Protocol for a Randomized Controlled Trial

by Ángel Antequera-Antequera, Geraldine Valenza-Peña, Julia Raya-Benítez, Alba Navas-Otero, Marie Carmen Valenza, Andrés Calvache-Mateo and Irene Cabrera-Martos

Healthcare 2025, 13(21), 2742; https://doi.org/10.3390/healthcare13212742 - 29 Oct 2025

Viewed by 1404

Abstract

Background/Objectives: Permanent colostomy requires significant physical and psychological adaptation. Patients often experience reduced physical activity, impaired quality of life, and fear of movement. Current exercise recommendations are inconsistent, and no consensus exists on safe return to activity. This study aims to evaluate the effect of a 12-week home-based graded exercise programme on physical activity, quality of life, kinesiophobia, exercise capacity, and self-efficacy in patients with permanent colostomies. Methods: This randomized controlled trial will recruit 51 adults with permanent colostomies, beginning six weeks post-surgery. Participants will be randomized (1:1) to an intervention or control group. The intervention group will receive a 12-week home-based exercise programme including patient education, resistance and core training, and progressive aerobic walking. The control group will receive standard medical care and an informational leaflet. Primary outcomes include physical activity (steps/day), quality of life (Stoma-QoL), kinesiophobia (Tampa Scale), exercise capacity (6-Minute Walk Test), and self-efficacy (General Self-Efficacy Questionnaire). Follow-up will be conducted at baseline, post-intervention, and six months. Data will be analyzed using intention-to-treat principles with a significance threshold of p < 0.05. Conclusions: This trial will be the first to assess the effects of a structured, home-based graded exercise programme in individuals with permanent colostomies. The findings are expected to provide evidence on the efficacy of exercise for improving physical and psychological outcomes in this population and to inform clinical guidelines for safe, individualized activity resumption. Full article

(This article belongs to the Special Issue Healthcare Goes Digital: Mobile Health and Electronic Health Technology in the 21st Century: Second Edition)

38 pages, 1030 KB

Open AccessSystematic Review

Dynamic Computer-Aided Navigation System in Dentoalveolar Surgery and Maxillary Bone Augmentation in a Dental Setting: A Systematic Review

by Federica Di Spirito, Roberta Gasparro, Maria Pia Di Palo, Alessandra Sessa, Francesco Giordano, Iman Rizki, Gianluca Allegretti and Alessia Bramanti

Healthcare 2025, 13(14), 1730; https://doi.org/10.3390/healthcare13141730 - 17 Jul 2025

Cited by 5 | Viewed by 2288

Abstract

Background: Dynamic computer-aided navigation systems are a real-time motion tracking technology widely applied in oral implantology and endodontics to enhance precision and reduce complications. However, their reliability, accuracy, and usability in dentoalveolar surgery and maxillary bone augmentation remain underinvestigated. Methods: A systematic review following PRISMA guidelines was conducted and registered on PROSPERO (CRD42024610153). PubMed, Scopus, Web of Science, and Cochrane Library databases were searched until October 2024 to retrieve English eligible studies, without restrictions on the publication year, on dynamic computer-assisted navigation systems in dentoalveolar and bone augmentation surgeries. Exclusion criteria were surgery performed without dynamic computer-assisted navigation systems; dental implant placement; endodontic surgery; and maxillo-facial surgery. The outcomes were reliability, accuracy, post-operative course, surgical duration, complications, patient- and clinician-reported usability, acceptability, and satisfaction. Included studies were qualitatively synthetized and judged using dedicated tools for the different study designs. Results: Twenty-nine studies with 214 patients were included, showing high reliability in dentoalveolar and bone augmentation surgeries comparable to or superior to freehand surgeries, higher accuracy in dentoalveolar surgery compared to maxillary bone augmentation, and reduced complication rates across all surgeries. While overall surgical duration slightly increased due to technology installation, operative time was reduced in third molar extractions. Patient-reported outcomes were poorly investigated. Clinician-reported outcomes were mixed, but difficulties in the differentiation of soft tissue from hard tissue were recorded, especially in sinus floor elevation. Conclusions: Dynamic computer-assisted navigation systems enhance accuracy and safety in dentoalveolar and bone augmentation surgery. Further studies are needed to assess the underinvestigated patient-reported outcomes and standardize protocols. Full article

(This article belongs to the Special Issue Healthcare Goes Digital: Mobile Health and Electronic Health Technology in the 21st Century: Second Edition)

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