An experimental search strategy retrieves more precise results than PubMed and Google for questions about medical interventions

A simple heuristic for Internet-based evidence search in primary care: a randomized controlled trial

BACKGROUND

General practitioners (GPs) are confronted with a wide variety of clinical questions, many of which remain unanswered.

METHODS

In order to assist GPs in finding quick, evidence-based answers, we developed a learning program (LP) with a short interactive workshop based on a simple three-step-heuristic to improve their search and appraisal competence (SAC). We evaluated the LP effectiveness with a randomized controlled trial (RCT). Participants (intervention group [IG] n=20; control group [CG] n=31) rated acceptance and satisfaction and also answered 39 knowledge questions to assess their SAC. We controlled for previous knowledge in content areas covered by the test.

RESULTS

Main outcome - SAC: within both groups, the pre-post test shows significant (P=0.00) improvements in correctness (IG 15% vs CG 11%) and confidence (32% vs 26%) to find evidence-based answers. However, the SAC difference was not significant in the RCT.

OTHER MEASURES

Most workshop participants rated "learning atmosphere" (90%), "skills acquired" (90%), and "relevancy to my practice" (86%) as good or very good. The LP-recommendations were implemented by 67% of the IG, whereas 15% of the CG already conformed to LP recommendations spontaneously (odds ratio 9.6, P=0.00). After literature search, the IG showed a (not significantly) higher satisfaction regarding "time spent" (IG 80% vs CG 65%), "quality of information" (65% vs 54%), and "amount of information" (53% vs 47%).

CONCLUSION

Long-standing established GPs have a good SAC. Despite high acceptance, strong learning effects, positive search experience, and significant increase of SAC in the pre-post test, the RCT of our LP showed no significant difference in SAC between IG and CG. However, we suggest that our simple decision heuristic merits further investigation.

Automated Summarization of Publications Associated with Adverse Drug Reactions from PubMed

Academic literature provides rich and up-to-date information concerning adverse drug reactions (ADR), but it is time consuming and labor intensive for physicians to obtain information of ADRs from academic literature because they would have to generate queries, review retrieved articles and summarize the results. In this study, a method is developed to automatically detect and summarize ADRs from journal articles, rank them and present them to physicians in a user-friendly interface. The method studied ADRs for 6 drugs and returned on average 4.8 ADRs that were correct. The results demonstrated this method was feasible and effective. This method can be applied in clinical practice for assisting physicians to efficiently obtain information about ADRs associated with specific drugs. Automated summarization of ADR information from recent publications may facilitate translation of academic research into actionable information at point of care.