BackgroundPatient adherence to medication regimens is critical in most chronic disease treatment plans. This study uses a patient-centered tablet app, “My Interventional Drug-Eluting Stent Educational App (MyIDEA).” This is an educational program designed to improve patient medication adherence.ObjectiveOur goal is to describe the design, methodology, limitations, and results of the MyIDEA tablet app. We created a mobile technology-based patient education app to improve dual antiplatelet therapy adherence in patients who underwent a percutaneous coronary intervention and received a drug-eluting stent.MethodsPatient advisers were involved in the development process of MyIDEA from the initial wireframe to the final launch of the product. The program was restructured and redesigned based on the patient advisers’ suggestions as well as those from multidisciplinary team members. To accommodate those with low health literacy, we modified the language and employed attractive color schemes to improve ease of use. We assumed that the target patient population may have little to no experience with electronic tablets, and therefore, we designed the interface to be as intuitive as possible.ResultsThe MyIDEA app has been successfully deployed to a low-health-literate elderly patient population in the hospital setting. A total of 6 patients have interacted with MyIDEA for an average of 17.6 minutes/session.ConclusionsIncluding patient advisers in the early phases of a mobile patient education development process is critical. A number of changes in text order, language, and color schemes occurred to improve ease of use. The MyIDEA program has been successfully deployed to a low-health-literate elderly patient population. Leveraging patient advisers throughout the development process helps to ensure implementation success.

Secondary data sources are widely used to measure the built asset environment, although their validity for this purpose is not well-established. Using community-engaged research methodology, this study conducted a census of publicfacing, built assets via direct observation and then tested the performance of these data against widely used secondary datasets. After engaging community organizations, a community education campaign was implemented. Using web-enabled cell phones and a web-based application prepopulated with the secondary data, census workers verified, modified, and/or added assets using street-level observation, supplementing data with web searches and telephone calls. Data were uploaded to http://www.SouthSideHealth.org. Using direct observation as the criterion standard, the sensitivity of secondary datasets was calculated. Of 5,773 assets on the prepopulated list, direct observation of publicfacing assets verified 1,612 as operating; another 653 operating assets were newly identified. Sensitivity of the commercial list for nonresidential, operating assets was 61 %. Using the asset census as the criterion standard, secondary datasets were incomplete and inaccurate. Comprehensive, accurate built asset data are needed to advance urban health research, inform policy, and improve individuals' access to assets.

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Background: Preferences regarding end-of-life (EOL) care in patients (pts) with HF may be influenced by personal, cultural, and health system factors. We examined characteristics associated with cardiopulmonary resuscitation preferences among pts hospitalized for HF and explored whether rates of Do Not Resuscitate (DNR) status have changed over time. Methods: Using the California State Inpatient Databases from the Healthcare Cost and Utilization Project and the American Hospital Association Annual Survey Databases, we identified 297,156 pts aged ≥ 60 years hospitalized for HF between 2004 and 2011. The first eligible hospitalization for each patient was selected. DNR status documented within the first 24 hours of admission was assessed. Multivariable logistic regression was used to evaluate associations of DNR status with demographic factors, year of admission, median household income quartile, and hospital teaching status. Results: 39,658 (13.4%) pts had a DNR order. Higher proportions with DNR were found among women than men (15.4% vs. 11.0%, p<.0001) and white pts than non-white pts (16.7% vs. 7.3%). Proportions of pts with DNR increased over the study period for both men and women (from 10.0% to 12.3% and 13.7% to 17.2%, respectively, p<.0001 for trend with time) and in both white pts (15.0% to 19.2%) and non-white pts (6.5% to 7.9%), but more rapidly for white pts (p=.02). In multivariable analysis adjusting for age, gender, race, and year of admission, odds ratios (ORs) for DNR were 1.5 per 5-year increase in age, 1.2 for women vs. men, and 2.0 for white vs. non-white pts. After adding income and hospital teaching status to the model, the respective ORs associated with each incremental increase in income quartile and receiving treatment in a teaching hospital were 1.1 and 1.2 (p<.0001 for both). ORs for age, gender, race, income, and hospital teaching status were essentially unchanged after adjustment for comorbidities. Conclusion: DNR status among pts hospitalized with HF is influenced by age, gender, race, and income in addition to hospital teaching status. The proportions of pts with DNR status have increased, but unequally among demographic subgroups. Continued efforts to understand the factors that influence preferences around resuscitation and EOL care are needed to provide truly patient-centered care.

Some clinical algorithms incorporate a person's race, ethnicity, or both as an input variable or predictor in determining diagnoses, prognoses, treatment plans, or risk assessments. Inappropriate use of race and ethnicity in clinical algorithms at the point of care may exacerbate health disparities and promote harmful practices of race-based medicine. This article describes a comprehensive search of online resources, the scientific literature, and the FDA Drug Label Information that uncovered 39 race-based risk calculators, six laboratory test results with race-based reference ranges, one race-based therapy recommendation, and 15 medications with race-based recommendations. These clinical algorithms based on race are freely accessible through an online database. This resource aims to raise awareness about the use of race-based clinical algorithms and track the progress made toward eradicating the inappropriate use of race. The database will be actively updated to include clinical algorithms based on race that were previously omitted, along with additional characteristics of these algorithms.