Research Studies
Peer-reviewed publications
Peer-reviewed research is essential for improving the quality, accuracy, and safety of healthcare technology. Here we present research papers that showcase the value and impact of Infermedica’s AI-powered solutions for healthcare professionals and decision-makers.
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Peer Reviewed
Mental Health Symptom Reporting to a Virtual Triage Engine Prior to and During the COVID-19 Pandemic
Gellert et al. (2024)
Highlights
Virtual triage usage surged by 160% from the pre-pandemic to the post-vaccine period, reflecting growing reliance on and demand for digital health tools during disruption to global healthcare services
20% of users reported at least one mental health symptom
Virtual triage is effective in detecting and referring mental health issues, demonstrating scalability for early care
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Peer Reviewed
Impact of integrated virtual and live nurse triage on patient care seeking and health care delivery effectiveness and efficiency
Gellert et al. (2024)
Highlights
Infermedica’s virtual triage altered member care-seeking behavior in 83.9% of interactions
22.8% of members changed the level of care they intended to seek after using VT-informed nurse triage implemented by Infermedica and Médis
62.2% of those who changed intent de-escalated to lower-acuity care (telemedicine or self-care)
20% of interactions took place outside of regular operating hours, Infermedica’s virtual triage directed 48.4% of these cases to lower acuity care
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Peer Reviewed
An analysis of virtual triage utilization by pregnant women prior to and during the COVID-19 pandemic
Jaszczak et al. (2024)
Highlights
COVID-19 pandemic significantly influenced the use of VT by pregnant women
In the first six months of 2020, there was an increase in pregnant users of 213%
The percentage of pregnant users grew from 0.32% in the first half of 2019 to 0.85% in late 2021
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Peer Reviewed
Virtual triage early detection of inappropriate care-seeking intent among patients with life-threatening cardiac symptoms
Gellert et al. (2024)
Highlights
76.4% of users with symptoms of a heart attack didn't know the right level of care or planned to seek inappropriate care
This was observed across all age groups
By gender, there was a slightly higher percentage among female users
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Peer Reviewed
Using AI-based virtual triage to improve acuity-level alignment of patient care seeking in an ambulatory care setting
Gellert et al. (2024)
Highlights
35% of patients changed their care-seeking behavior
12.5% decrease in video and in-person consultations
19.1% increase in patient engagement through virtual care
Peer Reviewed
The potential of virtual triage AI to improve early detection, care acuity alignment, and emergent care referral of life-threatening conditions
Gellert et al. (2024)
Highlights
76% of users with symptoms of a heart attack didn't plan urgent care help
33.5% of users with symptoms of investigated acute conditions hadn’t planned medical consultation before completing virtual triage
2 in every 1000 virtual triages performed could lead to life-saving decisions
Peer Reviewed
A comparative performance analysis of live clinical triage using rules-based triage protocols versus artificial intelligence-based automated virtual triage
Gellert et al. (2023)
Highlights
Infermedica and Schmitt-Thompson produced zero critical mis-triage situations — cases where an “emergency” is identified as “self-care”
Infermedica provides triage advice as safe as Schmitt-Thompson
Infermedica collects 4x more patient initial symptoms than Schmitt-Thompson for more data-driven assistance
Infermedica provides reliable and accurate results and increases triage levels in case of risk factors (especially for children)
Peer Reviewed
How virtual triage can improve patient experience and satisfaction: a narrative review and look forward
Gellert et al. (2023)
Highlights
Delivery of earlier and faster, more acuity-level-appropriate care
Digital triage as a front door to reduce a patient’s need to visit a medical facility for low-acuity conditions
The use of VT to enhance clinical effectiveness through early detection and referral
VT as an opportunity for providers to make patient health care experiences more personalized
Peer Reviewed
The role of virtual triage in improving clinician experience and satisfaction: a narrative review
Gellert et al. (2023)
Highlights
Importance of clinician experience and satisfaction to organizational performance
Contributors to poor clinician satisfaction
Current and potential role of virtual triage in delivering positive clinician experiences and streamlining communication with patients
Benefits of using virtual triage technology for healthcare delivery organizations in general and with respect specifically to improving clinician experience and satisfaction
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Peer Reviewed
Cardiovascular disease prevention recommendations from an online chat-based AI model
Gellert, G. A., & Jaszczak, J. (2023)
Highlights
Generative AI vs. Symbolic AI in healthcare applications
Potential and limitations of large language models (LLMs)
Investment and research into healthcare AI
Peer Reviewed
A multinational survey of patient utilization of and value conveyed through virtual symptom triage and healthcare referral
Gellert et al. (2023)
Highlights
80.1% of patients indicated substantial overall satisfaction with the experience and value delivered
80% of users indicated that they will use triage recurrently in the future
25.4% decrease in post-triage patients who remained uncertain of their care path
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Peer Reviewed
The quality of diagnosis and triage advice provided by free online symptom checkers and apps in Australia
Highlights
Symptomate has the highest accuracy results:
77% correct possible conditions listed in the top 3 results
61% correct possible conditions listed as first
81% correct possible conditions listed in the top 10 results
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Peer Reviewed
Diagnostic accuracy of web-based COVID-19 symptom checkers: comparison study
Highlights
A good balance between sensitivity and specificity achieved only by two symptom checkers – COVID-RA by Infermedica and Symptoma
Infermedica and Symptoma were ranked at the top in terms of the accuracy for COVID-19 evaluations
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Peer Reviewed
Virtual care to increase military medical centre capacity in the primary health care setting: a prospective self-controlled pilot study of symptoms collection and telemedicine
Highlights
85.8% of patients were satisfied with the virtual care experience
85.7% of patients agreed that the symptom checker was easy to use
Both patients and physicians felt that the symptom checker allowed for clearer and faster consultations
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Peer Reviewed
Heuristic evaluation of COVID-19 chatbots
Highlights
Infermedica ranked in the top 3 best scoring chatbots
Infermedica as one of the web chat bots with the most attractive graphical user interface
Peer Reviewed
Application of a web-based self-assessment triage tool during the COVID-19 pandemic: descriptive study
Highlights
The CRA tool screened a vast audience—248,862 people with potentially worrisome symptoms indicating COVID-19
Getting results and next-step recommendations provided reassurance and evidence-based information
The CRA tool served as a preclinical triage for individuals with worrisome symptoms
How we develop, maintain, and test our technologies
A carefully curated medical information database lies at the very heart of the Infermedica platform. Get a better understanding of the processes used to build, maintain, and test our technologies.
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Content Development Process
The creation, validation, and maintenance of the medical knowledge base follow rigorous and well-established procedures.
Acceptance Test Cases
Learn how we test the quality of our inference engine by carrying out assessments on thousands of well-documented clinical cases.
Certifications
Selected modules marked as class I medical device
Compliant with the General Data Protection Regulation
LEI Code: 984500NIA7B8AFF75805
Quality Management System compliant with PN-EN ISO 13485:2016
Processes and Development Procedures certified with ISO 27001:2017
Compliant with standards for patient data protection
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