A 40-Day Journey to Better Health: Utilizing the DanielFast to Improve Health Outcomes in Urban Church-Based Settings.

Background: As the costs associated with obesity increase, it is vital to evaluate the effectiveness of chronic disease prevention among underserved groups, particularly in urban settings. This research study evaluated Philadelphia area Keystone First members and church participants enrolled in a group health education program to determine the impact of the Daniel Fast on physical health and the adoption of healthy behaviors. Methods: Participants attended six-weekly health education sessions in two participating churches, and were provided with a digital healthy eating platform. Results: There was a statistically significant decrease from baseline to post assessment for weight, waist circumference and cholesterol. Participants reported a significant improvement in their overall well-being, social and physical functioning, vitality and mental health. Conclusion: Results of this study demonstrate that dietary recommendations and comprehensive group health education delivered in churches and reinforced on a digital platform can improve physical health, knowledge and psychosocial outcomes.

The geographic distribution of cardiovascular health in the stroke prevention in healthcare delivery environments (SPHERE) study.

BACKGROUND: Community-level factors have been clearly linked to health outcomes, but are challenging to incorporate into medical practice. Increasing use of electronic health records (EHRs) makes patient-level data available for researchers in a systematic and accessible way, but these data remain siloed from community-level data relevant to health. PURPOSE: This study sought to link community and EHR data from an older female patient cohort participating in an ongoing intervention at the Ohio State University Wexner Medical Center to associate community-level data with patient-level cardiovascular health (CVH) as well as to assess the utility of this EHR integration methodology. MATERIALS AND METHODS: CVH was characterized among patients using available EHR data collected May through July of 2013. EHR data for 153 patients were linked to United States census-tract level data to explore feasibility and insights gained from combining these disparate data sources. Analyses were conducted in 2014. RESULTS: Using the linked data, weekly per capita expenditure on fruits and vegetables was found to be significantly associated with CVH at the p<0.05 level and three other community-level attributes (median income, average household size, and unemployment rate) were associated with CVH at the p<0.10 level. CONCLUSIONS: This work paves the way for future integration of community and EHR-based data into patient care as a novel methodology to gain insight into multi-level factors that affect CVH and other health outcomes. Further, our findings demonstrate the specific architectural and functional challenges associated with integrating decision support technologies and geographic information to support tailored and patient-centered decision making therein.

EHR-based Visualization Tool: Adoption Rates, Satisfaction, and Patient Outcomes.

BACKGROUND: Electronic health records (EHRs) have the potential to enhance patient-provider communication and improve patient outcomes. However, in order to impact patient care, clinical decision support (CDS) and communication tools targeting such needs must be integrated into clinical workflow and be flexible with regard to the changing health care landscape. DESIGN: The Stroke Prevention in Healthcare Delivery Environments (SPHERE) team developed and implemented the SPHERE tool, an EHR-based CDS visualization, to enhance patient-provider communication around cardiovascular health (CVH) within an outpatient primary care setting of a large academic medical center. IMPLEMENTATION: We describe our successful CDS alert implementation strategy and report adoption rates. We also present results of a provider satisfaction survey showing that the SPHERE tool delivers appropriate content in a timely manner. Patient outcomes following implementation of the tool indicate one-year improvements in some CVH metrics, such as body mass index and diabetes. DISCUSSION: Clinical decision-making and practices change rapidly and in parallel to simultaneous changes in the health care landscape and EHR usage. Based on these observations and our preliminary results, we have found that an integrated, extensible, and workflow-aware CDS tool is critical to enhancing patient-provider communications and influencing patient outcomes.

Diagnosis-specific readmission risk prediction using electronic health data: a retrospective cohort study.

BACKGROUND: Readmissions after hospital discharge are a common occurrence and are costly for both hospitals and patients. Previous attempts to create universal risk prediction models for readmission have not met with success. In this study we leveraged a comprehensive electronic health record to create readmission-risk models that were institution- and patient- specific in an attempt to improve our ability to predict readmission. METHODS: This is a retrospective cohort study performed at a large midwestern tertiary care medical center. All patients with a primary discharge diagnosis of congestive heart failure, acute myocardial infarction or pneumonia over a two-year time period were included in the analysis.The main outcome was 30-day readmission. Demographic, comorbidity, laboratory, and medication data were collected on all patients from a comprehensive information warehouse. Using multivariable analysis with stepwise removal we created three risk disease-specific risk prediction models and a combined model. These models were then validated on separate cohorts. RESULTS: 3572 patients were included in the derivation cohort. Overall there was a 16.2% readmission rate. The acute myocardial infarction and pneumonia readmission-risk models performed well on a random sample validation cohort (AUC range 0.73 to 0.76) but less well on a historical validation cohort (AUC 0.66 for both). The congestive heart failure model performed poorly on both validation cohorts (AUC 0.63 and 0.64). CONCLUSIONS: The readmission-risk models for acute myocardial infarction and pneumonia validated well on a contemporary cohort, but not as well on a historical cohort, suggesting that models such as these need to be continuously trained and adjusted to respond to local trends. The poor performance of the congestive heart failure model may suggest that for chronic disease conditions social and behavioral variables are of greater importance and improved documentation of these variables within the electronic health record should be encouraged.

Community-level determinants of obesity: harnessing the power of electronic health records for retrospective data analysis.

BACKGROUND: Obesity and overweight are multifactorial diseases that affect two thirds of Americans, lead to numerous health conditions and deeply strain our healthcare system. With the increasing prevalence and dangers associated with higher body weight, there is great impetus to focus on public health strategies to prevent or curb the disease. Electronic health records (EHRs) are a powerful source for retrospective health data, but they lack important community-level information known to be associated with obesity. We explored linking EHR and community data to study factors associated with overweight and obesity in a systematic and rigorous way. METHODS: We augmented EHR-derived data on 62,701 patients with zip code-level socioeconomic and obesogenic data. Using a multinomial logistic regression model, we estimated odds ratios and 95% confidence intervals (OR, 95% CI) for community-level factors associated with overweight and obese body mass index (BMI), accounting for the clustering of patients within zip codes. RESULTS: 33, 31 and 35 percent of individuals had BMIs corresponding to normal, overweight and obese, respectively. Models adjusted for age, race and gender showed more farmers' markets/1,000 people (0.19, 0.10-0.36), more grocery stores/1,000 people (0.58, 0.36-0.93) and a 10% increase in percentage of college graduates (0.80, 0.77-0.84) were associated with lower odds of obesity. The same factors yielded odds ratios of smaller magnitudes for overweight. Our results also indicate that larger grocery stores may be inversely associated with obesity. CONCLUSIONS: Integrating community data into the EHR maximizes the potential of secondary use of EHR data to study and impact obesity prevention and other significant public health issues.

Assessment of Life's Simple 7 in the primary care setting: the Stroke Prevention in Healthcare Delivery EnviRonmEnts (SPHERE) study.

BACKGROUND: Adverse health behaviors and factors predict increased coronary heart disease and stroke risk, and effective use of health information technology (HIT) to automate assessment of and intervention on these factors is needed. A comprehensive, automated cardiovascular health (CVH) assessment deployed in the primary care setting offers the potential to enhance prevention, facilitate patient-provider communication, and ultimately reduce cardiovascular (CV) disease risk. We describe the methods for a study to develop and test an automated CVH application for stroke prevention in older women. METHODS AND RESULTS: The eligible study population for the Stroke Prevention in Healthcare Delivery EnviRonmEnts (SPHERE) study is approximately 1600 female patients aged 65 years and older and their primary care providers at The Ohio State University Wexner Medical Center. We will use an intervention design that will allow for a run-in period, comparison group data collection, a provider education period, and implementation of a best practice alert to prompt provider-patient interactions regarding CVH. Our primary outcome is a CVH score, comprising Life's Simple 7: smoking status, body mass index, blood pressure, cholesterol, fasting glucose, physical activity, and diet. The SPHERE application will generate visualizations of the CVH score within the electronic health record (EHR) during the patient-provider encounter. A key outcome of the study will be change in mean CVH score pre- and post-intervention. CONCLUSIONS: The SPHERE application leverages the EHR and may improve health outcomes through HIT designed to empower clinicians to discuss CVH with their patients and enhance primary prevention efforts.

EHR-based Clinical Trial Alert Effects on Recruitment to a Neurology Trial across Institutions: Interim Analysis of a Randomized Controlled Study.

EHR-based, point-of-care, clinical trial alerts (CTAs) have shown promise in prior studies to improve subject recruitment rates, but those studies have had limitations of generalizability. We report here an interim analysis of a cluster randomized controlled trial of the CTA approach applied to a neurology study at a second institution to test the efficacy of the approach across institutions with a different EHR. During the first phase (4 months) of our study, the CTA significantly improved physician-generated referrals among intervention physicians vs. control physicians (35 vs. 0). Additional trends and information about the usage of CTA features have also been gleaned. These findings add to the limited evidence for the utility and generalizability of the CTA approach.

Augmenting EHR-Derived Clinical Data with Geographic-Level Public Information to Develop Research Hypotheses for Population Obesity Rates.

Obesity adversely affects not just individuals but populations, and it is a major problem for our society. Environmental and socioeconomic factors influence obesity and are potentially important for research, yet these data are often not readily available in electronic health records (EHRs). We augmented an EHR-derived clinical data set with publicly available data on factors thought to influence obesity rates to examine associations between these factors and the prevalence of overweight and obesity. As revealed by our multinomial logistic model for overweight and obesity in a diverse region, this study demonstrates the potential value for augmenting the secondary use of EHR data with publicly available data.

Integrating population- and patient-level data for secondary use of electronic health records to study overweight and obesity.

We combined patient-level clinical data derived from the Electronic Health Record (EHR) with area-level environmental and socioeconomic data to study factors independently associated with overweight and obesity. Our multinomial logistic regression model showed that area-level factors such as farmers' markets, grocery stores and percent college-educated at the zip code level were significantly associated with the outcomes. However, mismatch in the granularity of community and clinical data limited us in creating a discriminatory model. While these results are promising, they reveal challenges that must be overcome in order to maximize secondary use of EHR data to further explore population health status.