BackgroundReadmissions 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.MethodsThis 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.Results3572 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).ConclusionsThe 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.

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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.

BackgroundObesity 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.MethodsWe 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.Results33, 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.ConclusionsIntegrating 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.

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.