ArticleJournal of medical Internet research2020
Assessment of the Frequency of Online Searches for Symptoms Before Diagnosis: Analysis of Archival Data.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 58 citations in OpenAlex.
- "I never thought to bring them up to the doctor": examining symptom appraisal, help-seeking behaviors, and diagnostic delays among young adults with cancer.Cancer causes & control : CCC · 2026Article
- [The internet as a source of health information: results of a household survey in Brazilian municipalities in the South and Central-West regions].Cadernos de saude publica · 2026Article
- Benchmark analysis of myopia-related issues using large language models: a comparison of ChatGPT-4o and deepseek.BMC ophthalmology · 2025Article
- Accuracy of chat-based artificial intelligence for patient education on orthostatic hypotension.Clinical autonomic research : official journal of the Clinical Autonomic Research Society · 2025Article
- A Mixed Methods Exploration of Social Media Use for Health Information in Under-Resourced Communities.International journal of environmental research and public health · 2025Article
- LLM enabled classification of patient self-reported symptoms and needs in health systems across the USA.NPJ digital medicine · 2025Article
- Cyberchondria, somatic symptoms, and internet-based self-diagnosis among the Saudi population.Scientific reports · 2025Article
- Patient Expectations: A Qualitative Study on What Patients Want From Their Healthcare Providers to Support Them in Healthy Aging.American journal of lifestyle medicine · 2025Article
- Reply to RE: Can ChatGPT provide high-quality patient information on male lower urinary tract symptoms suggestive of benign prostate enlargement?Prostate cancer and prostatic diseases · 2025Article
- Harnessing Internet Search Data as a Potential Tool for Medical Diagnosis: Literature Review.JMIR mental health · 2025Review
- Is Information About Musculoskeletal Malignancies From Large Language Models or Web Resources at a Suitable Reading Level for Patients?Clinical orthopaedics and related research · 2025Article
- Self-diagnosis and self-medication based on internet search among Non-Medical University students of Karachi.Annals of medicine and surgery (2012) · 2024Article
- Triage Accuracy and the Safety of User-Initiated Symptom Assessment With an Electronic Symptom Checker in a Real-Life Setting: Instrument Validation Study.JMIR human factors · 2024Article
- Differences in Fear and Negativity Levels Between Formal and Informal Health-Related Websites: Analysis of Sentiments and Emotions.Journal of medical Internet research · 2024Article
- Evaluating the diagnostic and triage performance of digital and online symptom checkers for the presentation of myocardial infarction; A retrospective cross-sectional study.PLOS digital health · 2024Article
- Deep learning and predictive modelling for generating normalised muscle function parameters from signal images of mandibular electromyography.Medical & biological engineering & computing · 2024Article
- A content-quality and optimization analysis of YouTube as a source of patient information for bipolar disorder.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2024Article
- Need an AI-Enabled, Next-Generation, Advanced ChatGPT or Large Language Models (LLMs) for Error-Free and Accurate Medical Information.Annals of biomedical engineering · 2024Article
- Measles and Pertussis outbreaks in England and Wales: a time-series analysis.NIHR open research · 2024Article
- Popular large language model chatbots' accuracy, comprehensiveness, and self-awareness in answering ocular symptom queries.iScience · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSurveys suggest that a large proportion of people use the internet to search for information on medical symptoms they experience and that around one-third of the people in the United States self-diagnose using online information. However, surveys are known to be biased, and the true rates at which people search for information on their medical symptoms before receiving a formal medical diagnosis are unknown.
objectiveThis study aimed to estimate the rate at which people search for information on their medical symptoms before receiving a formal medical diagnosis by a health professional.
methodsWe collected queries made on a general-purpose internet search engine by people in the United States who self-identified their diagnosis from 1 of 20 medical conditions. We focused on conditions that have evident symptoms and are neither screened systematically nor a part of usual medical care. Thus, they are generally diagnosed after the investigation of specific symptoms. We evaluated how many of these people queried for symptoms associated with their medical condition before their formal diagnosis. In addition, we used a survey questionnaire to assess the familiarity of laypeople with the symptoms associated with these conditions.
resultsOn average, 15.49% (1792/12,367, SD 8.4%) of people queried about symptoms associated with their medical condition before receiving a medical diagnosis. A longer duration between the first query for a symptom and the corresponding diagnosis was correlated with an increased likelihood of people querying about those symptoms (rho=0.6; P=.005); similarly, unfamiliarity with the association between a condition and its symptom was correlated with an increased likelihood of people querying about those symptoms (rho=-0.47; P=.08). In addition, worrying symptoms were 14% more likely to be queried about.
conclusionsOur results indicate that there is large variability in the percentage of people who query the internet for their symptoms before a formal medical diagnosis is made. This finding has important implications for systems that attempt to screen for medical conditions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.