In an era where the digitization of healthcare services is accelerating at unprecedented speeds, remote consultations through electronic visits (eVisits) have emerged as a pivotal innovation. These platforms promise increased accessibility, allowing patients to connect with healthcare providers without geographic or temporal constraints. However, the surge in demand associated with eVisits places considerable pressure on healthcare staff, potentially increasing workload and creating bottlenecks in care delivery. Addressing these challenges, recent research has focused on harnessing the power of artificial intelligence (AI) to optimize eVisit workflows while enhancing patient outcomes.
A groundbreaking qualitative study conducted across primary care settings in northwest England and London delved into the perspectives of both healthcare staff and patients regarding the integration of AI into eVisits. The investigators engaged 16 primary care professionals and 37 patients from 14 different practices to explore their attitudes toward potential AI functionalities, analyzing perceptions of risks, benefits, safety concerns, and practical barriers. This nuanced exploration sheds light on the complex dynamics underpinning AI adoption in everyday clinical practice.
Central to the findings was an initial atmosphere of uncertainty and skepticism about AI’s role, with common misconceptions prevalent among both cohorts. Patients expressed anxiety over AI potentially making autonomous diagnostic or prescribing decisions, fearing loss of personalized medical judgment. Concurrently, healthcare staff exhibited apprehensions from a safety standpoint, questioning AI’s reliability in clinical decision-making and the robustness of digital data privacy safeguards. Such reservations underscore the necessity for transparent communication about AI’s capabilities and limitations.
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Despite these initial concerns, participants acknowledged distinct advantages that AI could confer if appropriately integrated. Foremost among these was the potential for AI to accelerate response times to patient inquiries through automation of routine tasks. This efficiency could translate into a lighter workload for overburdened clinicians, allowing them to dedicate more time to complex cases. The emphasis was consistently on AI as a complementary tool rather than a replacement for human expertise, preserving the core tenets of personalized care.
However, apprehensions lingered regarding several risks associated with AI-enabled eVisits. Among these were the dangers of depersonalization in care delivery, where mechanized interactions might erode the empathic and relational aspects intrinsic to medicine. Patient data privacy emerged as a significant concern, reflecting broader societal anxieties about digital security. Moreover, the efficacy of AI triage systems was questioned in situations where patients may not accurately or completely enter symptom information, raising safety flags about erroneous prioritization or misclassification.
The research crystallized seven strategic opportunities for AI integration within the eVisit workflow that garnered general support from staff and patients alike. Firstly, AI-powered workflow routing could promptly identify the nature of each request and direct it to the most suitable healthcare team member, enhancing operational efficiency. Secondly, AI could play a pivotal role in crisis management by rerouting urgent cases to emergency services while delegating minor concerns to community pharmacies, thereby optimizing resource allocation.
Thirdly, AI-driven prioritization mechanisms could flag urgent requests for expedited clinician attention, improving patient outcomes by fostering timely interventions. Fourthly, AI could dynamically generate follow-up queries—such as requests for photographs, standardized questionnaires, or clarifying details—after initial submissions, refining diagnostic accuracy and reducing back-and-forth communication delays. Fifthly, AI might support staff by suggesting customizable response templates for common conditions, including sensitive areas like mental health, thereby streamlining communications and maintaining consistency.
Another promising avenue involves AI delivering trusted, evidence-based self-help educational resources directly to patients with minimal clinician involvement, empowering individuals to manage their health proactively. Lastly, AI could facilitate seamless booking of face-to-face appointments whenever a physical examination or in-person intervention is deemed essential, bridging the virtual and traditional care settings effectively.
These targeted interventions collectively form a conceptual framework for AI applications that enhance eVisit efficacy without compromising clinician oversight. Underpinning this approach is a clear consensus that AI tools should serve to augment, not supplant, human judgment. This alignment between technology and clinical decision-making forms the cornerstone of the study’s translational implications, suggesting pathways for responsible AI deployment in primary care.
Beyond operational efficiencies, the study highlights that fostering patient and staff trust will be paramount to successful AI adoption. Addressing misconceptions through comprehensive education, transparent algorithmic design, and demonstrable safety validations can mitigate fears and build confidence in AI-assisted care. Furthermore, ensuring robust data governance practices and safeguarding privacy are non-negotiable prerequisites to maintain the integrity of digital healthcare ecosystems.
This research provides an actionable blueprint for healthcare innovators and policy-makers, outlining both opportunities and challenges inherent in embedding AI into everyday primary care workflows. By foregrounding the lived experiences and concerns of frontline users, it advocates for a patient-centered, ethically grounded approach to technological innovation. Such insights are valuable to developers aiming to create clinically valid, user-friendly AI applications that resonate with practitioner workflows and patient expectations.
As healthcare systems globally contend with rising demand and resource constraints, leveraging AI to optimize remote care delivery emerges as a strategic imperative. This study affirms that thoughtfully designed AI features—mindful of clinical context, user trust, and safety—hold the promise to transform eVisits into more efficient, responsive, and patient-centered experiences. Continuing research and iterative testing in real-world settings will be essential to refine these tools and realize their full potential.
In summation, the integration of artificial intelligence into primary care eVisits stands at the threshold of significant transformation. With proper safeguards, patient and provider engagement, and a clear emphasis on enhancing rather than replacing human clinical judgment, AI could reshape how remote healthcare is delivered. The holistic insights from this study offer a timely roadmap for stakeholders seeking to navigate this promising frontier responsibly and effectively.
Subject of Research: Perspectives of patients and primary care staff on the integration of artificial intelligence in electronic visits (eVisits) to improve workflow and patient care.
Article Title: Seven Opportunities for Artificial Intelligence in Primary Care Electronic Visits: Qualitative Study of Staff and Patient Views
News Publication Date: 27-May-2025
Web References:
– Permanent Link: https://www.annfammed.org/content/23/3/214
– Pre-Embargo Link: https://www.annfammed.org/sites/default/files/additional_assets/PDF%20Documents/PDF/TEMPORARY_LINK_EXPIRES_MAY_27_2025/moschogianis.pdf
Keywords: Family medicine, Artificial intelligence, eVisits, Primary care, Digital health, Clinical workflows, Patient safety, Healthcare automation
Tags: artificial intelligence in healthcarebenefits of digital health innovationschallenges of remote healthcare deliveryenhancing patient outcomes with technologyeVisits in primary carehealthcare staff workload managementoptimizing healthcare workflows with AIpatient attitudes towards AI in medicinepatient-provider digital consultationsperspectives on AI in healthcareprimary care practice transformationsafety concerns in AI healthcare applications
