Personalized, Electronic Health Record–Integrated Decision Aid for Stroke Prevention in Atrial Fibrillation: A Small Cluster Randomized Trial and Qualitative Analysis of Efficacy and Acceptability

Abstract

Background: Shared decision-making in cardiology is increasingly recommended to improve patient-centeredness of care. Decision aids can improve patient knowledge and decisional quality but are infrequently used in real-world practice. This mixed-methods study tests the efficacy and acceptability of a decision aid integrated into the electronic health record (Integrated Decision Aid [IDeA]) and delivered by clinicians for patients with atrial fibrillation considering options to reduce stroke risk. We aimed to determine whether the IDeA improves patient knowledge, reduces decisional conflict, and is seen as acceptable by clinicians and patients. Methods: A small cluster randomized trial included 6 cardiovascular clinicians and 66 patients randomized either to the IDeA (HealthDecision) or usual care (clinician discretion) during a clinical encounter when stroke prevention treatment options were discussed. The primary outcome was patient knowledge of personalized stroke risk. Exploratory outcomes included decisional conflict, values concordance, trust, the presence of a shared decision-making process, and patient knowledge related to time spent using the IDeA. Additionally, we conducted semistructured interviews with clinicians and patients who used the IDeA were conducted to assess acceptability and predictions of future use. Results: The IDeA significantly increased patients’ knowledge of their stroke risk (odds ratio, 3.88 [95% CI, 1.39–10.78]; P <0.01]). Patients had less uncertainty about their final decision ( P =0.04). There were no significant differences in values concordance, trust in clinician or shared decision-making. Despite training, each clinician used the IDeA differently. Qualitative analysis revealed patients prefer using the IDeA earlier in their diagnosis. Clinicians were satisfied with the IDeA, yet varied in the contexts in which they planned to use it in the future. Conclusions: Using an Integrated Decision Aid, or IDeA, increases patient knowledge and lessens uncertainty for decision-making around stroke prevention in atrial fibrillation. Qualitative data provide insight into potential implementation strategies in real-world practice.

Atrial fibrillation: epidemiology, screening and digital health

“…Of those, 14 publications reported on integrated e-health enhanced management of AF, 47 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 5 on remote AF management platforms, 65 , 66 , 67 , 68 , 69 3 publications on mHealth-based heart rate/rhythm monitoring, 70 , 71 , 72 6 publications on mHealth supported patient self-care and medication adherence tools 73 , 74 , 75 , 76 , 77 , 78 and 5 publications reported on clinical decision making support tools. 79 , 80 , 81 , 82 , 83 …”

“… Cluster design Schott et al. 82 RCT IDeA Health Decision Health record OAC optimization Medication options CHA2S2-VASc and HAS-BLED assessment 33 (IC) 33 (UC) 72 ± 8.0 75 ± 11 36% 60% 6 months AF knowledge (IC vs UC): OR 3.88, 95% CI 1.39–10.78 Decision conflict: NS Patient satisfaction (n = 12): ease of use, acquisition of knowledge regarding stroke; limited ability to ask questions Clinician satisfaction (n = 9): helped center patient conversation, improved confidence in decision-making, saved time on calculating risk scales Cluster design Small sample size Single-center setting Differences in the provision of information by physicians Cox et al. 83 Canada RCT AF education Health record -Thromboprophylaxis guidance Heart rhythm control guidance Symptom tracker 590 (IC) 543 (UC) 73 ± 10 72 ± 9.9 60% 65% 12 months Unplanned emergency department visit/CV hospitalization (IC vs UC): NS Major bleeding: NS Cluster design Differences in OAC between groups Abbreviations: AF, atrial fibrillation; AFl, atrial flutter; BP, blood pressure; CC, case–control; CDSS, clinical decision support system; CI, coincidence interval; CV, cardiovascular; ECG, electrocardiogram; ED, emergency department; FU, follow up; HF, heart failure; HR, hazard ratio; IC, intervention care; NA, not applicable; ND, no data; NS, not significant; OAC, oral anticoagulation; OR, odd ratio; PC, prospective cohort; QoL, quality of life; RC, retrospective cohort; RCT, randomized controlled trial; RR, risk ratio; UC, usual care; y, year.…”

“… 81 On the other hand, in a small RCT including 6 clinicians and 66 patients, use of clinical decision-making support system (HealthDecision) improved patients’ knowledge about stroke risk (OR 3.88, 95% CI 1.39–10.78) as compared to usual care. 82 …”

Factors Influencing Participation in Shared Decision-Making Among Patients with Glaucoma in China: A Cross-Sectional Study

“… 1 , 2 Researches have indicated that SDM can enhance patients’ medical cognitive level, 3 , 4 relieve decisional conflict, and minimize anxiety and depression. 5 , 6 Clinicians also benefit, as the explicit communication and collaboration with their patients facilitate the establishment of a trusting relationship, which can promote treatment adherence and satisfaction with medical decisions. 7 The China Hospital Association encourages patients for patient safety.…”