Associations Between Online Search Trends and Outpatient Visits for Common Medical Symptoms in the United States from 2004 to 2019: Time Series Ecological Study
Bibliographic record
Abstract
Background: The seemingly endless amount of information available on the internet at the touch of a few buttons has increasingly served as a resource for individuals to find health information over the last 20+ years. Previous research in the field of infodemiology suggests that this change in use of the internet by the public to find health information has an impact on their interaction with the health care system. However, to this date, no study has directly juxtaposed the growth of internet searching and the number of visits to ambulatory care centers for the same concerns over the last two decades of internet growth. Objective: This study aimed to explore the relationship between online searches for three common primary care concerns and ambulatory visits for those same concerns during 2004-2019. Methods: The National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey data were searched between the years 2004 and 2019 in order to estimate the number of annual ambulatory care visits for cough, sore throat, and abdominal pain. Google Trends data were explored to analyze the number of people searching for information about the same complaints over the same years. Cross correlation, time series regression, and Granger causality analysis were performed to analyze patterns and causality. Results: Google Trends data showed that the number of searches for the common primary care symptoms "cough," "sore throat," and "stomach pain" in the United States grew by 208%, 290%, and 490%, respectively, between 2004 and 2019. However, over the same time, United States population-adjusted outpatient visits for cough and sore throat decreased by 41.5% and 40%, respectively, and stomach pain visits remained unchanged. According to conservative estimation, analysis concluded that an increase in internet searches for cough can predict a decline in ambulatory visits for that symptom. Conclusions: This supports that, on a population level, exploring online health information about some common, acute symptoms did not lead to increase in care seeking, but instead, suggests that a substantial portion of the population found the information to be reassuring or informative enough to not feel the need to seek care from a primary health care provider. With the rapid evolution and availability of more detailed and personalized information from various large language models, it is likely that internet search habits of users will continue to grow, and with it, continue to transform interactions with the health care system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".