The Future of Patient-Centered Care: Digital Tools and Strategies for Shared Decision-Making

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Digital Health Technologies".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2472

Editor


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Guest Editor
Department of Public Health Policy, University of West Attica-Greece, 11521 Athens, Greece
Interests: mass media and public health; health literacy and public health; media literacy and public health; fake news and disinformation; health communication; risk communication; doctor–patient communication and shared decision making; leadership and negotiation skills in health care settings

Special Issue Information

Dear Colleagues,

Shared decision-making (SDM) is a structured, evidence-based process through which patients and clinicians collaborate to make healthcare decisions that incorporate the most reliable clinical evidence and the patient’s values, needs, and preferences. This approach applies to a broad spectrum of healthcare decisions, ranging from the selection of diagnostic tests and therapeutic interventions to the determination of whether to proceed with surgical procedures or other treatment options.

The landscape of patient-centered care is experiencing a significant transformation due to the rapid integration of digital technologies into healthcare systems. In the current digital environment, patients are increasingly taking an active role in their decision-making processes, aided by various innovative tools, including mobile health applications, wearable devices, telemedicine platforms, and interactive decision aids.

This Special Issue aims to provide a comprehensive platform for authors worldwide to share innovative ideas and research that advances patient-centered care in the digital era. Ultimately, the goal is to improve patient outcomes, safety, quality of care, and the overall effectiveness of healthcare systems.

Research areas may include (but are not limited to) the following:

  1. Digital decision aids and shared decision-making models and strategies in various clinical contexts (e.g., chronic diseases, oncology, primary care, prevention, and vaccines).
  2. Validation studies of existing Patient Decision Aid tools.
  3. Patient engagement, empowerment, and health literacy (e.g., mobile health apps, wearables, and digital self-management tools).
  4. The impact of digital health tools on clinical outcomes. (e.g., patient satisfaction, adherence, quality of life, and health equity).
  5. Communication strategies for integrating digital solutions into patient-centered care.
  6. Policy frameworks and governance supporting SDM and digital health.
  7. Future directions for education and training.

We are pleased to invite you to contribute to this Special Issue and share your insights on shaping the future of patient-centered care in the digital era.

Dr. Effie Simou
Guest Editor

Manuscript Submission Information

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Keywords

  • shared decision-making (SDM)
  • patient-centered care
  • digital health
  • mobile health (mHealth)
  • telemedicine
  • digital health literacy
  • patient empowerment
  • decision aids
  • artificial intelligence in healthcare

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Published Papers (2 papers)

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22 pages, 1600 KB  
Article
Development of a Web-Based Multimedia Patient Decision Aid for Rheumatoid Arthritis: A User-Centered Design
by Effie Simou, Dimitrios Tseronis, Konstantina Zoupidou and Dimitrios Boumpas
Healthcare 2026, 14(8), 983; https://doi.org/10.3390/healthcare14080983 - 9 Apr 2026
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Abstract
Background: Shared decision-making (SDM) is particularly relevant in rheumatoid arthritis (RA), where multiple treatment options with distinct benefit–risk profiles require alignment with patient values and preferences. This study describes the development of a web-based PtDA to support treatment decision-making in RA and represents [...] Read more.
Background: Shared decision-making (SDM) is particularly relevant in rheumatoid arthritis (RA), where multiple treatment options with distinct benefit–risk profiles require alignment with patient values and preferences. This study describes the development of a web-based PtDA to support treatment decision-making in RA and represents the first structured, standards-aligned PtDA in the Greek healthcare context. Methods: Guided by the Ottawa Decision Support Framework and the International Patient Decision Aid Standards, a multistage, user-centered methodology was applied, including evidence synthesis, iterative prototyping, and alpha and beta testing. Qualitative methods, including focus group discussions, semi-structured interviews, and think-aloud protocols, were used, while usability was assessed with the System Usability Scale (SUS). Methodological quality was evaluated using IPDASi v3 and UCD-11 criteria. Results: The final PtDA provides a three-step pathway supporting values clarification, comparison of medication options, and reflection on decisional confidence. It was developed as a publicly accessible, web-based tool compatible with multiple devices, with core elements also available in printable format. The tool showed good usability (mean SUS: 75.93) and strong alignment with IPDASi (83.3/100), and user-centered design criteria (11/11). Conclusions: Developing digital PtDAs is inherently complex, underscoring the importance of established methodological frameworks. The findings demonstrate acceptable usability and alignment with established standards within this early-stage development study. Further research is required to examine the tool’s impact on decision-making processes, value–choice concordance, and longer-term clinical outcomes. Full article
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11 pages, 914 KB  
Review
Artificial Intelligence and Innovation in Oral Health Care Sciences: A Conceptual Review
by Marco Dettori, Demetrio Lamloum, Peter Lingström and Guglielmo Campus
Healthcare 2025, 13(24), 3327; https://doi.org/10.3390/healthcare13243327 - 18 Dec 2025
Cited by 2 | Viewed by 1437
Abstract
Background/Objectives: Artificial intelligence (AI) has rapidly evolved from experimental algorithms to transformative tools in clinical dentistry. Between 2020 and 2025, advances in machine learning (ML) and deep learning (DL) have reshaped diagnostic imaging, caries detection, prosthodontic design, and teledentistry, while raising new [...] Read more.
Background/Objectives: Artificial intelligence (AI) has rapidly evolved from experimental algorithms to transformative tools in clinical dentistry. Between 2020 and 2025, advances in machine learning (ML) and deep learning (DL) have reshaped diagnostic imaging, caries detection, prosthodontic design, and teledentistry, while raising new ethical and regulatory challenges. This study aimed to provide a comprehensive bibliometric and conceptual review of AI applications in dental care, highlighting research trends, thematic clusters, and future directions for equitable and responsible integration of AI technologies. In addition, the review further considers the implications of AI adoption for patient-centered care, including its potential role in supporting shared decision-making processes in oral healthcare. Methods: A comprehensive search was conducted in PubMed, Scopus and Embase for articles published between January 2020 and October 2025 using AI-related keywords in dentistry. Eligible records were analyzed using VOSviewer (v.1.6.20) to map co-occurrence networks of keywords, authors, and citations. A narrative synthesis complemented the bibliometric mapping, emphasizing conceptual and ethical dimensions of AI adoption in oral health care. Results: A total of 50 documents met the inclusion criteria. Bibliometric network visualization identified that the largest and most interconnected clusters were centered around the keywords “artificial intelligence,” “machine learning,” and “deep learning,” reflecting the technological backbone of AI-based applications in dentistry. Thematic evolution analysis indicated increasing interest in generative and multimodal AI models, explainability, and fairness in clinical deployment. Conclusions: AI has become a core driver of innovation in dentistry, enabling precision diagnostics and personalized care. However, responsible translation requires robust validation, transparency, and ethical oversight. Future research should integrate interdisciplinary approaches linking AI performance, patient outcomes, and equity in oral health. Full article
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