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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.

Background: Studies 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. Methods: To 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. Results: A 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). Limitations: Cases were from a single institution. CI was solicited using a single algorithm/platform. Conclusions: When 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. [ABSTRACT FROM AUTHOR]

Objective: The study sought to evaluate if peer input on outpatient cases impacted diagnostic confidence.Materials and Methods: This 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.Results: Among 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).Conclusions: Peer input increased diagnostic confidence primarily in high-uncertainty cases, consistent with findings that clinicians desire input primarily in cases with continued uncertainty. [ABSTRACT FROM AUTHOR]

Purpose: Adaptive 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. Methods: Pearson 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. Results: There were significant differences in accuracy across the three groups and the three categories (p < 0.001). Individual solvers have significant variance in accuracy across the three categories. Shorter solve duration predicted higher accuracy. Patterns of performance were identified; four profiles are highlighted to demonstrate potential adaptive learning interventions. Conclusions: Human Dx can assess diagnostic reasoning skills. When weaknesses are identified, adaptive learning strategies can push content to promote skill development. This has implications for customizing curricular elements to improve the diagnostic skills of healthcare professionals. [ABSTRACT FROM AUTHOR]

This Viewpoint proposes expanding the quadruple aim (improving population health, enhancing the care experience, reducing costs, and addressing clinician burnout) to the quintuple aim, adding a fifth aim of advancing health equity. [ABSTRACT FROM AUTHOR]

Even with the best health care available, patients with chronic illnesses typically spend no more than a few hours a year in a health care setting, while their outcomes are largely determined by their activities during the remaining 5,000 waking hours of the year. As a widely available, low-cost technology, mobile phones are a promising tool to use in engaging patients in behavior change and facilitating self-care between visits. We examined the impact of a six-month mobile health (mHealth) demonstration project among adults with diabetes who belonged to an academic medical center's employee health plan. In addition to pre-post improvements in glycemic control (p = 0.01) and patients' satisfaction with overall care (p = 0.04), we observed a net cost savings of 8.8 percent. Those early results suggest that mHealth programs can support health care organizations' pursuit of the triple aim of improving patients' experiences with care, improving population health, and reducing the per capita cost of health care. [ABSTRACT FROM AUTHOR]

Background. Self-management support and team-based care are essential elements of the Chronic Care Model but are often limited by staff availability and reimbursement. Mobile phones are a promising platform for improving chronic care but there are few examples of successful health system implementation. Program Development. An iterative process of program design was built upon a pilot study and engaged multiple institutional stakeholders. Patients identified having a "human face" to the pilot program as essential. Stakeholders recognized the need to integrate the program with primary and specialty care but voiced concerns about competing demands on clinician time. Program Description. Nurse administrators at a university-affiliated health plan use automated text messaging to provide personalized self-management support for member patients with diabetes and facilitate care coordination with the primary care team. For example, when a patient texts a request to meet with a dietitian, a nurse-administrator coordinates with the primary care team to provide a referral. Conclusion. Our innovative program enables the existing health system to support a de novo care management program by leveraging mobile technology. The program supports self-management and team-based care in a way that we believe engages patients yet meets the limited availability of providers and needs of health plan administrators. [ABSTRACT FROM AUTHOR]

We used data from the National Practitioner Data Bank (NPDB) to study the growth of physician malpractice payments. Judgments at trial account for 4 percent of all malpractice payments; settlements account for the remaining 96 percent. The average payment grew 52 percent between 1991 and 2003 (4 percent per year) and now exceeds $12 per capita each year. These increases are consistent with increases in the cost of health care. A preoccupation with data on judgments, extreme awards, or specific specialties results in an incomplete understanding of the growth of physician malpractice payments. [ABSTRACT FROM AUTHOR]

This Viewpoint discusses the use of artificial intelligence in health care and its potential effect on how patients access care and how physicians and patients make decisions. [ABSTRACT FROM AUTHOR]