gms | German Medical Science

Introduction: Laypersons with symptoms indicative of COVID-19 infection, commonly seek guidance on whether and where to seek medical care, and whether self-isolation is required of them. Numerous web-based decision support tools (DSTs) have been developed to assist in decision-making. Few studies already report benefits of such tools [1], [2], [3]. Though most of these tools’ underlying algorithms are simple and similar, reproducing the local health authority’s guidelines (eg, Center for Disease Control (CDC) [4]), their mode of presentation differs: some DSTs present the decision tree algorithm as a static flowchart [5], [6], [7], [8], [9], while others are designed as interactive chatbots [10], [11], [12], [13], guiding the user through the decision tree’s nodes step-by-step. Our study assesses whether interactive DSTs for laypersons with suspected COVID-19 disease are superior to static ones in terms of their quality of decision support.

Methods: We developed a static flowchart and an interactive prototype designed with InVision [14] as mock DSTs. Their underlying algorithm was identical and based on the CDC’s official guidelines. The layout mimicked a patient-facing, freely available DST [11]. We recruited adult US residents for an online survey in which participants evaluated seven fictitious descriptions of patients (case vignettes) constructed by us, some with and some without signs indicative of COVID-19. Participants were tasked with appraising the appropriate social and help-seeking behavior for each vignette. Participants randomly assigned to the control group had to solve the case vignettes without support while participants in the experimental groups received either of the two mock DST as support. The main outcome measures were perceived certainty in making decisions (measured with the Decisional Conflict Subscale [15]) and accuracy of decisions according to health authority recommendations. Additionally, the experimental groups were asked about their perceptions of the DSTs (i.a., self-reported trust). We used ANOVAs and subsequent t-tests to determine statistical significance.

Results: We recruited 196 participants. The mean accuracy of participants’ decisions was higher in the experimental group (mean (SD) of total number of correct decision (max=14): Static DST=11.45 (2.48); Interactive DST=11.71 (2.37)) than in the control group (10.17 (2.0)). Decisional certainty was significantly higher in the experimental groups, too. Differences for both measures between the two experimental groups were non-significant in post-hoc t-tests. Scores for user’s trust in the tool did not differ significantly between DST groups either.

Discussion: Benefits of the static mock DST were equal to that of the interactive mock DST when providing the same advice. As static flowcharts reveal their underlying decision algorithm more transparently, they might prove to be more suitable in guiding laypersons through the healthcare systems by communicating their reasoning and thereby empowering patients. Further research should investigate when decisions become too complex for static DST to be on par with interactive ones.

Conclusions: Our experiment shows that DSTs guiding laypersons in their decision-making on currently common decisions improve users’ accuracy and certainty in decision-making. When the decision space is limited, a static flowchart is potentially as suitable as an interactive tool in enhancing the decision quality of laypersons.

The authors declare that they have no competing interests.

The authors declare that a positive ethics committee vote has been obtained.