Skip to main content
Journal of Education and Health Promotion logoLink to Journal of Education and Health Promotion
letter
. 2026 Sep 30;15:430. doi: 10.4103/jehp.jehp_2296_25

Triage in the digital age: Has cyberchondria altered resource allocation patterns in the emergency department?

Mir Amirhossein Seyednazari 1,2, Amir Mohammad Dorosti 1,✉
PMCID: PMC13690165  PMID: 42924357

Dear Editor,

The traditional “physician-patient” paradigm, which was previously based on information asymmetry, has undergone a fundamental transformation due to the convergence of digital health information seeking and emergency medicine. A “crisis of certainty” has emerged in the Emergency Department (ED) as a result of the disruption of clinical decision-making dynamics caused by widespread Internet access and the proliferation of medical content. This letter makes the case that “Cyberchondria”—anxiety brought on by obsessive and repetitive health-related Internet searches—combined with defensive medicine has sparked a new and unstable pattern of resource allocation that we refer to as “Defensive Triage,”[1] based on recent research findings.

This letter presents cyberchondria as a systemic disruptor that initiates “Defensive Triage,” changing the basic mechanisms of resource allocation in emergency care, in contrast to earlier research that only considers cyberchondria as a patient-centered anxiety condition. Additionally, we propose that the interplay between patient anxieties and the healthcare provider’s reaction is what causes the disturbance in the ED rather than cyberchondria alone. We contend that “Defensive Medicine” serves as a crucial mediator, turning digital worry into a concrete pattern of maladaptive resource allocation.

The Collapse of Clinical Filters and the Failure of Self-Triage

Search engines are optimized for “engagement” rather than “clinical accuracy,” which is the root of the issue. Current algorithms often give catastrophic but uncommon diagnoses precedence over common conditions[2]; for example, a simple tension headache is frequently associated with a brain tumor before being attributed to exhaustion. Patients without clinical expertise make systematic mistakes during the “self-triage” process due to this algorithmic bias.[3]

According to recent studies, laypeople are about 82.2% sensitive when using the Internet to identify emergency situations but only 46.7% specific when it comes to the effectiveness of self-care. This enormous “specificity deficit” results in a surge of ED patients who, despite their physiological stability, believe they are in immediate danger because of false information they have found online and an “intolerance of uncertainty.”[4,5] Research demonstrates that cyberchondria is a reliable indicator of higher healthcare service utilization, with the likelihood of service use increasing with each unit increase in anxiety scores.[1,5]

The Emergence of Defensive Triage: A Systemic Response to Digital Pressure

Healthcare professionals are put in a defensive position when dealing with patients who regularly bring “Internet printouts” and irrational demands for complete certainty. According to data, more than 90% of emergency room doctors acknowledge using defensive medicine to avoid legal issues and confrontation with nervous patients.[6]

In order to control patient anxiety and protect themselves from potential online diagnoses, nurses may assign higher acuity scores—which may not be in line with the patient’s vital signs—at the ED entrance and nursing triage level. This behavior is known as “Triage Inflation.”[7,8,9] “The systemic cycle and the interplay between these factors are illustrated in Figure 1.”

Figure 1.

Figure 1

The conceptual cycle of defensive triage. The systemic route between provider-driven fear of lawsuit (Defensive Medicine) and patient-driven digital anxiety (Cyberchondria) is depicted in this figure. In the end, this interplay causes “Triage Inflation,” which results in the maladaptive distribution of emergency department resources, including needless imaging and longer wait times

The High Cost of False Certainty: Evidence from Imaging

The concerning data about the use of imaging resources demonstrate the concrete effects of this paradigm change. Numerous requested CT scans and ultrasounds lacked appropriate clinical indications, according to studies. Unsettlingly, a large proportion of these unsuitable scans produced no meaningful clinical results.

These findings demonstrate that enormous amounts of radiation, radiologist time, and emergency department space are being used as tools for “treating anxiety” and creating a legal safety margin rather than for pathology diagnosis. Due to its high rates of inappropriateness, ultrasound has effectively evolved into a tool for “bargaining” with patients who demand investigations. This allows doctors to avoid the radiation risks associated with CT scans while still satisfying the patient.[10,11]

This excessive imaging usage is the result of “Defensive Triage.” In a feedback cycle when technology is utilized to address the provider’s fear of lawsuit rather than the patient’s illness, doctors are trained to verify this urgency through objective testing when triage nurses exaggerate acuity scores out of anxiousness.

Systemic Consequences and the Necessity for a Paradigm Shift

This resource diversion is a zero-sum game. Every hour spent on defensive scans for the “worried well” lengthens the wait times for patients in critical condition, making overcrowding and “boarding” worse. Additionally, one of the main causes of burnout and “compassion fatigue” among healthcare workers is the constant pressure to satisfy digitally demanding patients.[9,10,11]

The health system must switch from its current strategy to “Precision Triage” in order to break through this deadlock. Three levels of intervention are needed to address this: first, the incorporation of AI-based pre-hospital triage tools that can screen for the actual need for emergency care with higher probabilistic accuracy; for instance, pre-hospital risk classification has shown potential in trial programs using AI symptom checkers like Babylon Health or Ada; second, providing “e-Health Literacy” education to enable the community to evaluate Internet material critically and reduce needless concern before it reaches the hospital, in order to adopt a health promotion-centered strategy; and third, legislative changes to enact “Safe Harbor” laws that shield doctors from lawsuits resulting from their refusal to perform unnecessary tests. Although switching to “Precision Triage” is essential, there are still limitations to overcome. Data privacy and algorithmic transparency are limitations to the use of AI technologies, and strong political will is needed to enact “Safe Harbor” legislation. Furthermore, it is crucial to recognize that these observations (limitation) could change among healthcare systems with various insurance structures and medico-legal frameworks.

The Emergency Department will continue to be the main victim of societal digital anxiety in the absence of these structural measures, giving up its essential resources to the combined pressures of defensive medicine and cyberchondria.

Future research suggestions

Future study should concentrate on qualitative evaluations of nursing triage decision-making processes in the context of digitally worried patients as well as longitudinal studies measuring the relationship between regional Internet search patterns and ED triage scores.

AI disclosure

To enhance language clarity and structure, the authors employed generative AI technologies throughout the development of this paper. Following the use of this tool, the writers took full responsibility for the publication’s content and reviewed and amended it as necessary.

Ethical approval

Not applicable. This manuscript is a Letter to the Editor and did not involve any human or animal subjects.

Author’s contributions

MASN: Conceptualization, Writing – original draft, Writing – review & editing. AD: Writing – original draft, Writing – review & editing.

Data availability statement

Data sharing is not applicable to this article as no new primary data were created or analyzed in this study.

Conflicts of interest

Nil.

Acknowledgment

Not applicable.

Funding Statement

Nil.

References

  • 1.Kobryn M, Duplaga M. Cyberchondria severity and utilization of health services in Polish society: A cross-sectional study. BMC Public Health. 2024;24:902. doi: 10.1186/s12889-024-18399-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Mansour RF, Fatouh AH. Measurement of bias in the contents of web search for health information retrieval. J Scientometric Res. 2023;12:621–30. [Google Scholar]
  • 3.Wallace W, Chan C, Chidambaram S, Hanna L, Iqbal FM, Acharya A, et al. The diagnostic and triage accuracy of digital and online symptom checker tools: A systematic review. NPJ Digit Med. 2022;5:118. doi: 10.1038/s41746-022-00667-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Monteith S, Glenn T, Geddes JR, Whybrow PC, Achtyes ED, Bauer M. Implications of online self-diagnosis in psychiatry. Pharmacopsychiatry. 2024;57:45–52. doi: 10.1055/a-2268-5441. [DOI] [PubMed] [Google Scholar]
  • 5.Aggar C, Raddi S, Sorwar G, Nasrawi D, Sogalad M, Dandagi S, et al. Evaluation of undergraduate nurses’ acceptance and adoption of mLearning in India. J Educ Health Promot. 2025;14:472. doi: 10.4103/jehp.jehp_1851_24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Doğan A, İncealtın O, Oğuz A. Effect of defensive medicine on the clinical practices of emergency medicine physicians. Glob Emerg Crit Care. 2023;2:117–21. [Google Scholar]
  • 7.Abadian L, Alavi NM, Tagharrobi Z. Nurses’ viewpoints on nursing competencies during epidemics: A Q methodology study. J Educ Health Promot. 2025;14:475. doi: 10.4103/jehp.jehp_1047_24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Lasman L, Islamy A, Farida F, Nurhidayati N, Rohmawati I, Osella D. Relationship between emergency level (Triage) and anxiety level of the patient’s family. Prisma Sains. 2023;11:463–70. [Google Scholar]
  • 9.Soyler S, Cavmak S, Cavmak D. The impact of health anxiety and distrust in the healthcare system on cyberchondria: A cross-sectional study. Br J Healthc Manag. 2023;29:1–8. [Google Scholar]
  • 10.Dijk SW, Wollny C, Kroencke T, Myriam Hunink MG. Sex differences in inappropriate imaging requests: Insights from the medical imaging decision and support (MIDAS) study. Eur Radiol. 2025 doi: 10.1007/s00330-025-12088-w. doi: 10.1007/s00330-025-12088-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Kwee RM, Toxopeus R, Kwee TC. Imaging overuse in the emergency department: The view of radiologists and emergency physicians. Eur J Radiol. 2024;176:111536. doi: 10.1016/j.ejrad.2024.111536. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Data sharing is not applicable to this article as no new primary data were created or analyzed in this study.


Articles from Journal of Education and Health Promotion are provided here courtesy of Wolters Kluwer -- Medknow Publications

RESOURCES