Evidence-based health information from the users' perspective--a qualitative analysis

Evidence-based information is a precondition for informed decision-making and participation in health. There are several recommendations and definitions available on the generation and assessment of so called evidence-based health information for patients and consumers (EBHI). They stress the importance of objectively informing people about benefits and harms and any uncertainties in health-related procedures. There are also studies on the comprehensibility, relevance and user-friendliness of these informational materials. But to date there has been little research on the perceptions and cognitive reactions of users or lay people towards EBHI. The aim of our study is to define the spectrum of consumers' reaction patterns to written EBHI in order to gain a deeper understanding of their comprehension and assumptions, as well as their informational needs and expectations.

This study is based on an external user evaluation of EBHI produced by the German Institute for Quality and Efficiency in Health Care (IQWiG), commissioned by the IQWiG. The EBHI were examined within guided group discussions, carried out with lay people. The test readers' first impressions and their appraisal of the informational content, presentation, structure, comprehensibility and effect were gathered. Then a qualitative text analysis of 25 discussion transcripts involving 94 test readers was performed.

Based on the qualitative text analysis a framework for reaction patterns was developed, comprising eight main categories: (i) interest, (ii) satisfaction, (iii) reassurance and trust, (iv) activation, (v) disinterest, (vi) dissatisfaction and disappointment, (vii) anxiety and worry, (viii) doubt.

Many lay people are unfamiliar with core characteristics of this special information type. Two particularly critical issues are the description of insufficient evidence and the attendant absence of clear-cut recommendations. Further research is needed to examine strategies to explain the specific character of EBHI so as to minimize unintended or adverse reaction patterns. The presented framework describes the spectrum of users' reaction patterns to EBHI. It may support existing best practice models for editing EBHI.