Perceived and Actual Search Behaviors May Provide Markers for Healthcare Utilization and Severity of Illness
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».