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Electronic health records (EHRs) offer the potential to study large numbers of patients but are designed for clinical practice, not research. Despite the increasing availability of EHR data, their use in research comes with its own set of challenges. In this article, we describe some important considerations and potential solutions for commonly encountered problems when working with large-scale, EHR-derived data for health services and community-relevant health research. Specifically, using EHR data requires the researcher to define the relevant patient subpopulation, reliably identify the primary care provider, recognize the EHR as containing episodic (i.e., unstructured longitudinal) data, account for changes in health system composition and treatment options over time, understand that the EHR is not always well-organized and accurate, design methods to identify the same patient across multiple health systems, account for the enormous size of the EHR, and consider barriers to data access. Associations found in the EHR may be nonrepresentative of associations in the general population, but a clear understanding of the EHR-based associations can be enormously valuable to the process of improving outcomes for patients in learning health care systems. In the context of building 2 large-scale EHR-derived data sets for health services research, we describe the potential pitfalls of EHR data and propose some solutions for those planning to use EHR data in their research. As ever greater amounts of clinical data are amassed in the EHR, use of these data for research will become increasingly common and important. Attention to the intricacies of EHR data will allow for more informed analysis and interpretation of results from EHR-based data sets. [ABSTRACT FROM AUTHOR]

Objective Adults with serious mental illness are at increased risk for diabetes mellitus and diabetes-related complications. This article classifies subgroups among people with serious mental illness and comorbid diabetes with respect to functional status and examines differences among those groups. Methods This analysis used a baseline sample of 157 adults with serious mental illness and diabetes mellitus enrolled in a National Institute of Health-funded research study. Latent profile analysis was used to distinguish health status profiles and investigate how these subgroups differed across assessment domains. Results Participants with depression, schizophrenia, and bipolar disorder (n = 157) were included in the study. Mean age was 52.9 years (standard deviation = 9.8), and 62 (40%) were African American. From the latent profile analysis, a three-class model appeared to provide the best fit. Class 1 (34.9%) had a very low functional health status approximately two standard deviations below the general population mean. Class 2 (43.7%) had a low functional status approximately one standard deviation below the general mean. Class 3 (21.4%) had moderate functional status with scores near population mean. Groups differed on measures of personal characteristics, clinical status and symptom severity, self-care behaviors, and environmental characteristics. Conclusions Although individuals with schizophrenia generally have poor prognosis once they develop diabetes, latent profile analysis identified distinct health status subgroups. Although all three groups demonstrated illness burden, the pattern of differences between these groups across measures may suggest the need for different interventions for highly diverse adults who received care within safety-net primary care. [ABSTRACT FROM AUTHOR]

Objective Serious mental illness and type II diabetes mellitus have a high comorbidity, and both have a higher prevalence of anxiety disorders compared to the general population. Targeted Training in Illness Management is a group-based self-management training approach which targets serious mental illness and type II diabetes mellitus concurrently. This analysis examines data from a randomized controlled trial of Targeted Training in Illness Management intervention to examine the impact of comorbid anxiety on baseline psychiatric symptomatology and diabetic control, and on longitudinal treatment outcomes. Methods We conducted secondary analyses on data from a prospective, 60-week, randomized controlled trial testing Targeted Training in Illness Management versus treatment as usual in 200 individuals with serious mental illness and diabetes. Primary outcomes included measures related to serious mental illness symptoms, functional status, general health status, and diabetes control. Measures were compared between those participants with anxiety disorders versus those without anxiety at baseline as well as over time using linear mixed effects analyses. Results Forty seven percent of the participants had one or more anxiety disorders. At baseline, those with an anxiety diagnosis had higher illness severity, depressive, and other psychiatric symptomatology and disability. Diabetic control (HbA1c) was not significantly different at baseline. In the longitudinal analyses, no significant mean slope differences over time (group-by-time interaction effect) between those with anxiety diagnoses and those without in treatment as usual group were found for primary outcomes. Within the Targeted Training in Illness Management arm, those with anxiety disorders had significantly greater improvement in mental health functioning. Those with anxiety comorbidity in the Targeted Training in Illness Management group demonstrated significantly lower HbA1c levels compared to no anxiety comorbidity and also demonstrated a greater improvement in HbA1c over the first 30 weeks compared to those without anxiety comorbidity. Conclusion Comorbid anxiety in serious mental illness and type II diabetes mellitus population is associated with increased psychiatric symptomatology and greater disability. Individuals from this population appear to experience greater improvement in functioning from baseline with the Targeted Training in Illness Management intervention. Anxiety comorbidity in the serious mental illness and type II diabetes mellitus population does not appear to have a negative impact on diabetic control. These complex relationships need further study. Clinical Trials Registration ClinicalTrials.gov: Improving outcomes for individuals with serious mental illness and diabetes (NCT01410357). [ABSTRACT FROM AUTHOR]

Randomized trials provide strong evidence regarding efficacy of interventions but are limited in their capacity to address potential heterogeneity in effectiveness within broad clinical populations. For example, a treatment that on average is superior may be distinctly worse in certain patients. We propose a technique for using large electronic health registries to develop and validate decision models that measure—for distinct combinations of covariate values—the difference in predicted outcomes among 2 alternative treatments. We demonstrate the methodology in a prototype analysis of in-hospital mortality under alternative revascularization treatments. First, we developed prediction models for a binary outcome of interest for each treatment. Decision criteria were then defined based on the treatment-specific model predictions. Patients were then classified as receiving concordant or discordant care (in relation to the model recommendation), and the association between discordance and outcomes was evaluated. We then present alternative decision criteria and validation methodologies, as well as sensitivity analyses that investigate 1) the imbalance between treatments on observed covariates and 2) the aggregate impact of unobserved covariates. Our methodology supplements population-average clinical trial results by modeling heterogeneity in outcomes according to specific covariate values. It thus allows for assessment of current practice, from which cogent hypotheses for improved care can be derived. Newly emerging large population registries will allow for accurate predictions of outcome risk under competing treatments, as complex functions of predictor variables. Whether or not the models might be used to inform decision making depends on the extent to which important predictors are available. Further work is needed to understand the strengths and limitations of this approach, particularly in relation to those based on randomized trials. [ABSTRACT FROM AUTHOR]

Recruitment and retention of individuals with serious mental illness (SMI) and comorbid diabetes mellitus (DM) in research studies can be challenging with major impediments being difficulties reaching participants via telephone contact, logistic difficulties due to lack of transportation, ongoing psychiatric symptoms, and significant medical complications. Research staff directly involved in recruitment and retention processes of this study reviewed their experiences. The largest barriers at the macro, mediator, and micro levels identified in this study were inclement weather, transportation difficulties, and intermittent and inaccessible telephone contact. Barrier work-around practices included using the health system's EHR to obtain current phone numbers, providing transportation assistance (bus passes or parking reimbursement), and flexible scheduling of appointments. Suggestions are intended to assist in planning for recruitment and retention strategies.