Topic-specific Infobuttons Reduce Search Time but their Clinical Impact is Unclear
Bibliographic record
Abstract
A Review of: Del Fiol, Guilherme, Peter J. Haug, James J. Cimino, Scott P. Narus, Chuck Norlin, and Joyce A. Mitchell. ‚Effectiveness of Topic-specific Infobuttons: A Randomized Controlled Trial.‛ Journal of the American Medical Information Association 15.6 (2008): 752-9. Objective – To assess whether infobutton links that direct users to specific content topics (‚topic links‛) are more effective in answering clinical questions than links that direct users to general overview content (‚nonspecific links‛). Design – Randomized control trial. Setting – Intermountain Healthcare, an integrated system of 21 hospitals and over 120 outpatient clinics located in Utah and southeastern Idaho. Subjects – Ninety clinicians and 3,729 infobutton sessions. Methods – To ensure comparable group composition, subjects were paired and randomly allocated to the study groups. Clinicians in the intervention group had access to topic links, while those in the control group had access to nonspecific links. All subjects at Intermountain Healthcare use a Web-based electronic medical record system (EMR) called HELP2 Clinical Desktop with integrated infobutton links. An Infobutton Manager application defines the content topics and resources; in this case, Micromedex® (Thomson Healthcare, Englewood, CO) provided access to the topic links. The medication order entry module, the most popular of the outpatient modules, was selected to test the two configurations of infobuttons. A focus group of seven HELP2 users aided the researchers in determining the most salient topics to be displayed as a part of the intervention group's user-interface. The study measured infobutton session duration, or time spent seeking information, the number of infobutton sessions conducted, and the outcome and impact of the information seeking. A post-session questionnaire displayed randomly in 30% of sessions measured outcome and impact. The study was conducted between May and November, 2007. This project was funded in part by the National Library of Medicine. Main Results – Subjects in the intervention group spent 17.4% less time seeking information than those in the control group (35.5 seconds vs. 43 seconds, p = 0.008). The intervention group used infobuttons 20.5% more often (22 sessions vs. 17.5 sessions, p = 0.21) than those in the control group, a difference that was not statistically significant. Twenty-five subjects answered the post-session survey at least once for a total of 115 (9.9%) responses out of 1,161 possible sessions. The information seeking success rate was equally high in both groups (87.2% intervention vs. 89.4% control, p = .099). Subjects reported high positive clinical impact (i.e., decision enhancement or learning) in 62% of successful sessions. Subjects conveyed a moderate or high level of frustration in 80% of responses associated with unsuccessful sessions. Conclusion – Topic links provide a slight advantage in the clinical decision-making process by reducing the amount of time spent searching. But while the session length difference between the control and intervention groups is statistically significant, it is less clear whether the difference is clinically meaningful. As previous studies have indicated, infobuttons are able to answer clinical medication questions with a high success rate. It is unclear whether topic links have a clinically significant impact, or rather, whether they are more effective than nonspecific links. The authors believe that the study results ‚should generalize to high-frequency, medication-related infobutton users in other institutions‛ (758).
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".