Perceived and Actual Search Behaviors May Provide Markers for Healthcare Utilization and Severity of Illness
Bibliographic record
Abstract
A Review of: White, R. W., & Horvitz, E. (2014). From health search to healthcare: explorations of intention and utilization via query logs and user surveys. Journal of the American Medical Informatics Association, 21(1), 49-55. Retrieved from http://dx.doi/org10.1136/amiajnl-2012-001473 Abstract Objective – To gain an understanding of the relationship between online health information searching behaviour and healthcare utilization. Design – Survey and log data analysis. Setting – A software development campus and health information websites with servers in the United States of America. Subjects – Two separate subject groups were used for this study. For the search log analysis, participants were randomly selected English-speaking users of a Microsoft toolbar who had consented to provide their anonymous log data. 489 volunteers who indicated they could recall their last visit to a medical facility were invited to participate in the survey. Methods – To determine search behaviour, four months of data from 2011 were collected and analyzed from search engine logs. A unique user identifier allowed for analysis of individual search behaviour across multiple sessions, which then provided the opportunity to identify search behaviour changes over time. Search queries were labelled and annotated as symptoms, serious illnesses, and benign explanation based on curated lists identified in a related study. Erroneous synonymous entries were removed to increase labelling precision (e.g., astrology-related terms were removed for “cancer”). The researchers specifically noted searches signifying health utilization intent (HUI). Initial queries indicating HUI for each user were identified to determine whether or not there were changes in search behaviour prior to and following searches indicating HUI. Perceptions of motivators related to healthcare utilization (HU) were gathered through a validated, anonymous electronic survey. Through fifty open and closed questions, participants were asked how they search for medical information online, how they locate medical facilities and scheduled appointments, and how their search behaviour might differ before and after HU. Survey results were compared with search log data to identify and explain trends. Main Results – From log data, search queries focusing on symptoms increased prior to the first indication of HUI and decreased afterwards. The authors suggest that this increase may reflect a “heightened state of concern or uncertainty” (p. 51). As well, searches on relatively benign symptoms were observed to spike dramatically three weeks after the first identified HUI search, reflecting what the authors suggest may be related to users having been reassured through a visit with a health professional. The increase in benign symptom searching is supported by survey data. The number of symptom-related searches is shown to correlate with the number of HUI searches using Pearson’s correlation coefficient (r=0.64, t(78)=14.43, p
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".