JMIR: Features of Computer-Based Decision Aids: Systematic Review

CITATION: Syrowatka A, Krömker D, Meguerditchian AN, Tamblyn R

Features of Computer-Based Decision Aids: Systematic Review, Thematic Synthesis, and Meta-Analyses

J Med Internet Res 2016;18(1):e20

DOI: 10.2196/jmir.4982


Background: Patient information and education, such as decision aids, are gradually moving toward online, computer-based environments. Considerable research has been conducted to guide content and presentation of decision aids. However, given the relatively new shift to computer-based support, little attention has been given to how multimedia and interactivity can improve upon paper-based decision aids.

Objective: The first objective of this review was to summarize published literature into a proposed classification of features that have been integrated into computer-based decision aids. Building on this classification, the second objective was to assess whether integration of specific features was associated with higher-quality decision making.

Methods: Relevant studies were located by searching MEDLINE, Embase, CINAHL, and CENTRAL databases. The review identified studies that evaluated computer-based decision aids for adults faced with preference-sensitive medical decisions and reported quality of decision-making outcomes. A thematic synthesis was conducted to develop the classification of features. Subsequently, meta-analyses were conducted based on standardized mean differences (SMD) from randomized controlled trials (RCTs) that reported knowledge or decisional conflict. Further subgroup analyses compared pooled SMDs for decision aids that incorporated a specific feature to other computer-based decision aids that did not incorporate the feature, to assess whether specific features improved quality of decision making.

Results: Of 3541 unique publications, 58 studies met the target criteria and were included in the thematic synthesis. The synthesis identified six features: content control, tailoring, patient narratives, explicit values clarification, feedback, and social support. A subset of 26 RCTs from the thematic synthesis was used to conduct the meta-analyses. As expected, computer-based decision aids performed better than usual care or alternative aids; however, some features performed better than others. Integration of content control improved quality of decision making (SMD 0.59 vs 0.23 for knowledge; SMD 0.39 vs 0.29 for decisional conflict). In contrast, tailoring reduced quality of decision making (SMD 0.40 vs 0.71 for knowledge; SMD 0.25 vs 0.52 for decisional conflict). Similarly, patient narratives also reduced quality of decision making (SMD 0.43 vs 0.65 for knowledge; SMD 0.17 vs 0.46 for decisional conflict). Results were varied for different types of explicit values clarification, feedback, and social support.

Conclusions: Integration of media rich or interactive features into computer-based decision aids can improve quality of preference-sensitive decision making. However, this is an emerging field with limited evidence to guide use. The systematic review and thematic synthesis identified features that have been integrated into available computer-based decision aids, in an effort to facilitate reporting of these features and to promote integration of such features into decision aids. The meta-analyses and associated subgroup analyses provide preliminary evidence to support integration of specific features into future decision aids. Further research can focus on clarifying independent contributions of specific features through experimental designs and refining the designs of features to improve effectiveness.

SELECTED EXTRACTS (selected by Neil PW)

‘Overall, content control improved quality of decision making. All types of content control performed better than other features, with the exception of navigation. Content control is intended to provide patients with control over order, detail, and type of evidence presented.’

‘Tailoring reduced quality of decision making, with all subgroups performing worse than other features. In general, tailoring is intended to translate evidence into patient-specific information to improve engagement.’

‘Patient narratives reduced quality of decision making. Patient narratives are intended to provide insight into patient experiences and bring attention to important evidence to consider throughout the decision-making process. In addition, information presented through patient narratives is processed differently than written information and can improve understanding and retention of evidence. However, narratives can unintentionally present biased or unbalanced information, which may result in lower-quality decision making.’

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