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ObjectiveWe present the design and feasibility testing for the "Digital Drag and Drop Pillbox" (D-3 Pillbox), a skill-based educational approach that engages patients and providers, measures performance, and generates reports of medication management skills.MethodsA single-cohort convenience sample of patients hospitalized with heart failure was taught pill management skills using a tablet-based D-3 Pillbox. Medication reconciliation was conducted, and aptitude, performance (% completed), accuracy (% correct), and feasibility were measured.ResultsThe mean age of the sample (n = 25) was 59 (36-89) years, 50% were women, 62% were black, 46% were uninsured, 46% had seventh-grade education or lower, and 31% scored very low for health literacy. However, most reported that the D-3 Pillbox was easy to read (78%), easy to repeat-demonstrate (78%), and comfortable to use (tablet weight) (75%). Accurate medication recognition was achieved by discharge in 98%, but only 25% reported having a "good understanding of my responsibilities."ConclusionsThe D-3 Pillbox is a feasible approach for teaching medication management skills and can be used across clinical settings to reinforce skills and medication list accuracy.

Poor education-related discharge preparedness for patients with heart failure is believed to be a major cause of avoidable rehospitalizations. Technology-based applications offer innovative educational approaches that may improve educational readiness for patients in both inpatient and outpatient settings; however, a number of challenges exist when implementing electronic devices in the clinical setting. Implementation challenges include processes for "on-boarding" staff, mediating risks of cross-contamination with patients' device use, and selling the value to staff and health system leaders to secure the investment in software, hardware, and system support infrastructure. Strategies to address these challenges are poorly described in the literature. The purpose of this article is to present a staff development program designed to overcome challenges in implementing an electronic, tablet-based education program for patients with heart failure.

In clinical practice, nurses are expected to understand and implement the science that supports patient care, yet they fall short of goals to implement evidence in practice. One reason is difficulty in interpreting research results. Interpretation requires an ability to read and speak a language that many nurses have never mastered-the language of data. This article presents a skill-based solution for use in nursing professional development to improve nurses' understanding of statistics as a language.

BackgroundThe use of health information technology (HIT) may improve medication adherence, but challenges for implementation remain. ObjectiveThe aim of this paper is to review the current state of HIT as it relates to medication adherence programs, acknowledge the potential barriers in light of current legislation, and provide recommendations to improve ongoing medication adherence strategies through the use of HIT. MethodsWe describe four potential HIT barriers that may impact interoperability and subsequent medication adherence. Legislation in the United States has incentivized the use of HIT to facilitate and enhance medication adherence. The Health Information Technology for Economic and Clinical Health (HITECH) was recently adopted and establishes federal standards for the so-called "meaningful use" of certified electronic health record (EHR) technology that can directly impact medication adherence. ResultsThe four persistent HIT barriers to medication adherence include (1) underdevelopment of data reciprocity across clinical, community, and home settings, limiting the capture of data necessary for clinical care; (2) inconsistent data definitions and lack of harmonization of patient-focused data standards, making existing data difficult to use for patient-centered outcomes research; (3) inability to effectively use the national drug code information from the various electronic health record and claims datasets for adherence purposes; and (4) lack of data capture for medication management interventions, such as medication management therapy (MTM) in the EHR. Potential recommendations to address these issues are discussed. ConclusionTo make meaningful, high quality data accessible, and subsequently improve medication adherence, these challenges will need to be addressed to fully reach the potential of HIT in impacting one of our largest public health issues.

Background: Cardiovascular disease (CVD) is the leading cause of hospitalization in the United States, and patients with CVD are at a high risk of readmission after discharge. We examined whether patients' perceived risk of readmission at discharge was associated with actual 30-day readmissions in patients hospitalized with CVD.Methods: We recruited 730 patients from the Duke Heart Center who were admitted for treatment of CVD between January 1, 2015, and August 31, 2017. A standardized survey was linked with electronic health records to ascertain patients' perceived risk of readmission, and other sociodemographic, psychosocial, behavioral, and clinical data before discharge. All-cause readmission within 30 days after discharge was examined.Results: Nearly 1-in-3 patients perceived a high risk of readmission at index admission and those who perceived a high risk had significantly more readmissions within 30 days than patients who perceived low risks of readmission (23.6% versus 15.8%, P=0.016). Among those who perceived a high risk of readmission, non-White patients (odds ratio [OR], 2.07 [95% CI, 1.28-3.36]), those with poor self-rated health (OR, 2.30 [95% CI, 1.38-3.85]), difficulty accessing care (OR, 2.72 [95% CI, 1.24-6.00]), and prior hospitalizations in the past year (OR, 2.13 [95% CI, 1.21-3.74]) were more likely to be readmitted. Among those who perceived a low risk of readmission, patients who were widowed (OR, 2.69 [95% CI, 1.60-4.51]) and reported difficulty accessing care (OR, 1.89 [95% CI, 1.07-3.33]) were more likely to be readmitted.Conclusions: Patients who perceived a high risk of readmission had a higher rate of 30-day readmission than patients who perceived a low risk. These findings have important implications for identifying CVD patients at a high risk of 30-day readmission and targeting the factors associated with perceived and actual risks of readmission. [ABSTRACT FROM AUTHOR]