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BackgroundThere is increasing interest in finding novel approaches to reduce health disparities in readmissions for acute decompensated heart failure (ADHF). Text messaging is a promising platform for improving chronic disease self-management in low-income populations, yet is largely unexplored in ADHF. ObjectiveThe purpose of this pre-post study was to assess the feasibility and acceptability of a text message–based (SMS: short message service) intervention in a largely African American population with ADHF and explore its effects on self-management. MethodsHospitalized patients with ADHF were enrolled in an automated text message–based heart failure program for 30 days following discharge. Messages provided self-care reminders and patient education on diet, symptom recognition, and health care navigation. Demographic and cell phone usage data were collected on enrollment, and an exit survey was administered on completion. The Self-Care of Heart Failure Index (SCHFI) was administered preintervention and postintervention and compared using sample t tests (composite) and Wilcoxon rank sum tests (individual). Clinical data were collected through chart abstraction. ResultsOf 51 patients approached for recruitment, 27 agreed to participate and 15 were enrolled (14 African-American, 1 White). Barriers to enrollment included not owning a personal cell phone (n=12), failing the Mini-Mental exam (n=3), needing a proxy (n=2), hard of hearing (n=1), and refusal (n=3). Another 3 participants left the study for health reasons and 3 others had technology issues. A total of 6 patients (5 African-American, 1 White) completed the postintervention surveys. The mean age was 50 years (range 23-69) and over half had Medicaid or were uninsured (60%, 9/15). The mean ejection fraction for those with systolic dysfunction was 22%, and at least two-thirds had a prior hospitalization in the past year. Participants strongly agreed that the program was easy to use (83%), reduced pills missed (66%), and decreased salt intake (66%). Maintenance (mean composite score 49 to 78, P=.003) and management (57 to 86, P=.002) improved at 4 weeks, whereas confidence did not change (57 to 75, P=.11). Of the 6 SCHFI items that showed a statistically significant improvement, 5 were specifically targeted by the texting intervention. ConclusionsOver half of ADHF patients in an urban, largely African American community were eligible and interested in participating in a text messaging program following discharge. Access to mobile phones was a significant barrier that should be addressed in future interventions. Among the participants who completed the study, we observed a high rate of satisfaction and preliminary evidence of improvements in heart failure self-management.

BackgroundStudies report higher diagnostic accuracy using the collective intelligence (CI) of multiple clinicians compared with individual clinicians. However, the diagnostic process is iterative, and unexplored is the value of CI in improving clinical recommendations leading to a final diagnosis.MethodsTo compare the appropriateness of diagnostic recommendations advised by individual physicians versus the CI of physicians, we entered actual consultation requests sent by primary care physicians to specialists onto a web-based CI platform capable of collecting diagnostic recommendations (next steps for care) from multiple physicians. We solicited responses to 35 cases (12 endocrinology, 13 gynecology, 10 neurology) from ≥3 physicians of any specialty through the CI platform, which aggregated responses into a CI output. The primary outcome was the appropriateness of individual physician recommendations versus the CI output recommendations, using recommendations agreed upon by 2 specialists in the same specialty as a gold standard. The secondary outcome was the recommendations' potential for harm.ResultsA total of 177 physicians responded. Cases had a median of 7 respondents (interquartile range: 5-10). Diagnostic recommendations in the CI output achieved higher levels of appropriateness (69%) than recommendations from individual physicians (45%; χ2 = 5.95, P = 0.015). Of the CI recommendations, 54% were potentially harmful, as compared with 41% of individuals' recommendations (χ2 = 2.49, P = 0.11).LimitationsCases were from a single institution. CI was solicited using a single algorithm/platform.ConclusionsWhen seeking specialist guidance, diagnostic recommendations from the CI of multiple physicians are more appropriate than recommendations from most individual physicians, measured against specialist recommendations. Although CI provides useful recommendations, some have potential for harm. Future research should explore how to use CI to improve diagnosis while limiting harm from inappropriate tests/therapies.

ObjectiveThe study sought to evaluate if peer input on outpatient cases impacted diagnostic confidence.Materials and methodsThis randomized trial of a peer input intervention occurred among 28 clinicians with case-level randomization. Encounters with diagnostic uncertainty were entered onto a digital platform to collect input from ≥5 clinicians. The primary outcome was diagnostic confidence. We used mixed-effects logistic regression analyses to assess for intervention impact on diagnostic confidence.ResultsAmong the 509 cases (255 control; 254 intervention), the intervention did not impact confidence (odds ratio [OR], 1.46; 95% confidence interval [CI], 0.999-2.12), but after adjusting for clinician and case traits, the intervention was associated with higher confidence (OR, 1.53; 95% CI, 1.01-2.32). The intervention impact was greater in cases with high uncertainty (OR, 3.23; 95% CI, 1.09- 9.52).ConclusionsPeer input increased diagnostic confidence primarily in high-uncertainty cases, consistent with findings that clinicians desire input primarily in cases with continued uncertainty.

PurposeAdaptive learning emerges when precise assessment informs delivery of educational materials. This study will demonstrate how data from Human Dx, a case-based e-learning platform, can characterize an individual's diagnostic reasoning skills, and deliver tailored content to improve accuracy.MethodsPearson Chi-square analysis was used to assess variability in accuracy across three groups of participants (attendings, residents, and medical students) and three categories of cases (core medical, surgical, and other). Logistic regression analyses were conducted to explore the relationship between solve duration and accuracy. Mean accuracy and duration were calculated for 370 individuals. Repeated measures analysis of variance (ANOVA) were used to assess variability for an individual solver across the three categories.ResultsThere were significant differences in accuracy across the three groups and the three categories (p