Logo: to the web site of Uppsala University

Background: As Parkinson disease (PD) rates increase, so does interest in finding new technological solutions for PD management. Despite substantial efforts to explore potential applications of artificial intelligence (AI) in PD management, research from the perspectives of people with PD on AI remains limited.

Objective: This study aims to explore the ethical considerations of AI in PD management from the perspective of people with PD.

Methods: A qualitative triangulation of 13 interviews and 2 focus groups (FGs) with a panel of expert-by-experience people with PD from 6 European countries was carried out using abductive thematic analysis. The 6 biomedical ethical principles conceptualized by Beauchamp and Childress guided the analysis. Participants varied in diagnosis, disease experiences, and technological backgrounds. A researcher with PD was involved from start to finish, providing valuable insights into data collection and analysis.

Results: Although optimistic that AI could enhance autonomy and beneficence through personalized, actionable insights for people with PD and their health care professionals, concerns arose over patient involvement, model accuracy and privacy, ethical injustices, and the psychological impact. Risk prediction, prognosis, and medication response were viewed differently in terms of potential value and ethical considerations, with risk prediction being perceived as the most ethically complex. To uphold autonomy, it was considered important for AI insights to be patient-accessible, and sensitive insights should be communicated by a health care professional who recognizes individual differences in desiring and responding to AI predictions.

Conclusions: While people with PD felt AI could personalize (self-)care and increase autonomy, concerns about psychological harm and widening inequalities highlight the importance of ethical safeguards. Our findings underscore the importance of AI integrations that prioritize individual needs, actively engage people with PD in the development, implementation, and interpretation of predictive AI, and establish guidelines to support health care professionals and minimize patient harm. Different forms of implementation and precautions should be taken for risk, progression, and medication response prediction.

Patients' use of generative AI (GenAI) independently from healthcare is increasing across diseases. Little is known about its use among people with Parkinson's disease (PwP). This exploratory convenience-sample, mixed-methods online survey (n = 149, 19 countries) explored PwP's use of GenAI. Among our respondents, 65% had used GenAI, of which 40% had used it for disease-specific inquiries. Qualitative analysis identified informational, interpretive, and preparational uses of GenAI. As PwP increasingly bring AI-assisted data to consultations, clinicians must now actively engage in discussions about these tools, to support shared decision-making, safety and transparency.

Background: Parkinson disease (PD) is a progressive neurodegenerative disorder that poses complex challenges for persons with PD, informal caregivers, and health care professionals. With growing interest in digital and predictive artificial intelligence (AI) tools for disease management, understanding the needs and digital readiness of these stakeholder groups is crucial. Objective: This work aims to (1) identify digital practices for PD management among persons with PD, at-risk individuals, caregivers, and health care professionals; (2) compare these practices across groups; (3) explore stakeholder desires for AI-based tools; and (4) assess alignments and gaps to inform tailored AI solutions. Methods: An anonymous cross-sectional online survey of an exploratory nature was distributed (from December 2024 to October 2025) in 5 languages and completed by 255 respondents. Descriptive statistics summarized responses to 41 questions, including stakeholder-specific items. χ2 tests were performed to examine stakeholder differences in desired AI features. Results: Interest in predictive AI was high across stakeholder groups. Symptom tracking was the most desired feature (selected by more than 76% of the respondents), and personalized treatment recommendations came second for both persons with PD and health care professionals; however, stakeholder priorities diverged in other areas. Health care professionals rated improving patient and informal caregiver engagement as significantly more important than persons with PD did, χ21 (n=205)=34.78, P<.001, and Cramer V=0.41. Despite considerable interest, the reported use of digital tools was limited, as most persons with PD did not use symptom-tracking apps or wearables, nor were they currently monitoring their condition, although many expressed intentions to begin. Conclusions: While predictive AI tools were viewed positively across groups, there were significant gaps in stakeholder preferences, highlighting the importance of tailored, context-aware design. Early diagnosis was not prioritized by persons with PD or health care professionals, likely reflecting the complexity of diagnosing PD in the absence of disease-modifying therapies. Coupled with the emphasis placed on preventive lifestyle guidance by persons with PD and those at risk, this highlights the importance of actionability in AI-based monitoring and prediction. Such actionability may also enhance perceived relevance and uptake, given that reported interest in digital health tools and self-tracking exceeded actual use. These findings offer early-stage insight to guide the development of future AI-based solutions for PD.

BACKGROUND: Digitalization and mobile health (mHealth) technologies hold promise for improving home care delivery. However, many mHealth initiatives fail to achieve their goals. Understanding the reasons behind these failures is critical for informing the successful implementation of mHealth in primary and home care settings.

OBJECTIVE: This study aimed to explore the implementation process of a tablet computer with an mHealth app providing mobile access to the electronic health record (EHR) in home care, identifying barriers and facilitators to its uptake.

METHODS: A tablet with EHR access was introduced at 4 primary care centers and 1 municipal home care organization in Sweden. Participants were nurses and physicians working at the study sites. Focus group discussions and interviews were conducted to obtain a rich understanding of implementation-related issues experienced by the health care professionals. Qualitative content analysis was conducted using the Consolidated Framework for Implementation Research to guide interpretation.

RESULTS: Eighteen health care professionals (16 nurses and 2 physicians) participated in the study. The implementation of the mHealth app was largely unsuccessful. Key barriers included limited functionality of the app, technological immaturity, and unstable infrastructure. Organizational context influenced uptake, especially due to differing EHR systems and varying levels of user engagement. Users who were involved in the development process were more positive, despite the absence of certain functionalities, while those excluded struggled with adoption. Long development and implementation timelines and limited training reduced enthusiasm and negatively affected user engagement. Additional challenges included insufficient implementation planning, lack of leadership engagement, and inadequate resources for support and training.

CONCLUSIONS: For mHealth implementations to succeed, tools must meet users' needs and integrate seamlessly with existing eHealth ecosystems and infrastructures. Premature implementations can lead to change fatigue and diminish future engagement. Investments in user-centered design, thorough testing, organizational readiness, and sustained support are essential to realize the potential of mHealth in home care.

Background:

Video consultations (VCs) are effective and beneficial, yet their use is being discontinued, and there is a preference for face-to-face consultations.

Objective:

This study investigates how patients and health care professionals (HCPs) perceive patients’ introduction to VCs, who use them, and what drives their use in Swedish primary care.

Methods:

Six focus group interviews with 27 HCPs and 13 individual interviews with patients in primary care were conducted between August 2022 and May 2023. The interviews examined VC implementation and were analyzed using rapid assessment procedures.

Results:

A total of five themes were identified: (1) challenging start with unprepared users and immature technology; (2) users and nonusers are perceived to have different characteristics, needs, and circumstances; (3) patient-related drivers: based on patients’ preferences and opportunities; (4) HCP-related drivers: clinical suitability, assessment of patient needs, and preferences; and (5) societal and organizational drivers: the pandemic, demographics, and infrastructure. Patients and HCPs described the introduction of VCs as rushed and confusing, with limited guidance and support (theme 1). HCPs struggled to assist patients due to a lack of training and limited access to the patient-facing interface (theme 1). VC users were typically perceived as younger, digitally literate, and motivated by convenience or urgency, while older adults and those with language or cognitive barriers were often assumed to be nonusers (theme 2). VC use was shaped by patient preferences, accessibility, and clinical urgency (theme 3), as well as by HCPs’ professional judgment and convenience (theme 4). Assumptions held by HCPs about patients’ digital skills and preferences influenced whether VCs were offered, while patients’ own assumptions about complexity or suitability affected whether they accepted them. Broader factors, such as digital infrastructure, platform usability, reimbursement policies, and the COVID-19 pandemic, also significantly influenced use (theme 5).

Conclusions:

The rushed implementation potentially deterred some patients and HCPs from use. Misguided preconceptions and biases negatively influenced VC use and risked reinforcing existing disparities and contributing to digital exclusion. In addition, HCPs’ and patients’ preferences, which were related to their needs, waiting times, and different circumstances, and potentially misguided judgments of appropriateness, influenced VC use. Lastly, infrastructure, reimbursement, sociodemographics, and organizational type also drive VC use. To support more sustainable and equitable use of VC in primary care, developers should optimize VC applications’ usability, implementers should deploy multiple strategies, health care providers should consider the potential of VC in care delivery, and policymakers should increase digital readiness. Further research should evaluate the effectiveness of different strategies for introducing patients to VCs, explore younger patients’ and nonusers’ perspectives, characteristics of HCP users, and differences between professional roles, as well as between consultation types.