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Digital Decision-Support Tools Enhance Atrial Fibrillation Treatment Decisions

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Digital tools are revolutionizing healthcare, especially in supporting patient decisions for complex conditions like atrial fibrillation (AF). A recent systematic review and meta-analysis assessed the impact of digital decision-support tools on treatment decisions in adults with AF, highlighting their potential to reduce decisional conflict and enhance patient knowledge.

Methodology and Findings

The systematic review included randomised controlled trials (RCTs) and quasi-experimental studies evaluating digital tools designed to aid AF treatment decisions. Thirteen articles, encompassing 11 studies, met the inclusion criteria, with a total of 2714 participants. The studies primarily focused on non-valvular AF, and the tools varied significantly in features and functions. Importantly, four tools were classified as patient decision aids, and the majority incorporated individualized risk calculations for thromboembolic stroke prevention.

Meta-analyses revealed that digital tools significantly reduced decisional conflict compared to usual care, with a standardized mean difference of -0.19, translating to a 12.4-unit decrease on a 0-100 scale. Additionally, improvements in patient knowledge were noted, albeit with low certainty evidence. However, socioeconomically disadvantaged groups were underrepresented, suggesting potential barriers to market access and the need for more inclusive research.

Implications for Healthcare Delivery

Of the 11 tools analyzed, only four were publicly available, and three had been implemented in healthcare settings, indicating room for broader market penetration. The review underscores the necessity for future tools to leverage digital capabilities for personalization and interactivity, considering health literacy and equity. Enhanced market access through better designed and widely available tools could democratize healthcare, offering equitable benefits across diverse patient populations.

The study also reported on the readability of content in only one instance, suggesting an area for further improvement. Ensuring that digital decision-support tools are accessible to individuals with varying levels of health literacy could enhance their effectiveness and reach.

Key Inferences

• Digital decision-support tools can significantly reduce decisional conflict in AF treatment.

• Improvements in patient knowledge are notable but need further robust evidence.

• Socioeconomically disadvantaged groups are underrepresented, indicating a need for inclusive tool design and broader market access.

• Readability and accessibility remain critical factors for effective tool implementation.

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In conclusion, digital patient decision-support tools show promise in improving treatment decisions for AF, particularly in reducing decisional conflict. While the tools also enhance patient knowledge, there is a need for further research to confirm these benefits. Future studies should focus on creating more personalized, interactive tools that consider health literacy and equity aspects, ultimately facilitating better market access and implementation in diverse healthcare settings.

Original Article:

BMJ Evid Based Med. 2024 Jun 29:bmjebm-2023-112820. doi: 10.1136/bmjebm-2023-112820. Online ahead of print.

ABSTRACT

OBJECTIVES: To assess the effects of digital patient decision-support tools for atrial fibrillation (AF) treatment decisions in adults with AF.

STUDY DESIGN: Systematic review and meta-analysis.

ELIGIBILITY CRITERIA: Eligible randomised controlled trials (RCTs) evaluated digital patient decision-support tools for AF treatment decisions in adults with AF.

INFORMATION SOURCES: We searched MEDLINE, EMBASE and Scopus from 2005 to 2023.Risk-of-bias (RoB) assessment: We assessed RoB using the Cochrane Risk of Bias Tool 2 for RCTs and cluster RCT and the ROBINS-I tool for quasi-experimental studies.

SYNTHESIS OF RESULTS: We used random effects meta-analysis to synthesise decisional conflict and patient knowledge outcomes reported in RCTs. We performed narrative synthesis for all outcomes. The main outcomes of interest were decisional conflict and patient knowledge.

RESULTS: 13 articles, reporting on 11 studies (4 RCTs, 1 cluster RCT and 6 quasi-experimental) met the inclusion criteria. There were 2714 participants across all studies (2372 in RCTs), of which 26% were women and the mean age was 71 years. Socioeconomically disadvantaged groups were poorly represented in the included studies. Seven studies (n=2508) focused on non-valvular AF and the mean CHAD2DS2-VASc across studies was 3.2 and for HAS-BLED 1.9. All tools focused on decisions regarding thromboembolic stroke prevention and most enabled calculation of individualised stroke risk. Tools were heterogeneous in features and functions; four tools were patient decision aids. The readability of content was reported in one study. Meta-analyses showed a reduction in decisional conflict (4 RCTs (n=2167); standardised mean difference -0.19; 95% CI -0.30 to -0.08; p=0.001; I2=26.5%; moderate certainty evidence) corresponding to a decrease in 12.4 units on a scale of 0 to 100 (95% CI -19.5 to -5.2) and improvement in patient knowledge (2 RCTs (n=1057); risk difference 0.72, 95% CI 0.68, 0.76, p<0.001; I2=0%; low certainty evidence) favouring digital patient decision-support tools compared with usual care. Four of the 11 tools were publicly available and 3 had been implemented in healthcare delivery.

CONCLUSIONS: In the context of stroke prevention in AF, digital patient decision-support tools likely reduce decisional conflict and may result in little to no change in patient knowledge, compared with usual care. Future studies should leverage digital capabilities for increased personalisation and interactivity of the tools, with better consideration of health literacy and equity aspects. Additional robust trials and implementation studies are warranted.

PROSPERO REGISTRATION NUMBER: CRD42020218025.

PMID:38950915 | DOI:10.1136/bmjebm-2023-112820

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