Electronic Medical Records and Their Use in Health Promotion and Population Research of Cardiovascular Disease

Kite, Bobbie; Tangasi, Wilkister; Kelley, Marjorie M.; Bower, Julie K.; Foraker, Randi E.

doi:10.1007/s12170-014-0422-5

Cited by 7 publications

(6 citation statements)

References 22 publications

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“…Passive use includes activities such as secondary data analysis or identification of patients that meet specific phenotypic requirements. Active use includes engaging patients via the use of platforms such as the EHR and PHR for recruitment and outcomes data collection 1,9,10. The overall goal of this study is to describe the use, strengths, and limitations of the EHR for identifying potential study participants and the PHR for recruiting and collecting contextual data for an observational research study.…”

Section: Introductionmentioning

confidence: 99%

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Active Use of Electronic Health Records (EHRs) and Personal Health Records (PHRs) for Epidemiologic Research: Sample Representativeness and Nonresponse Bias in a Study of Women During Pregnancy

Bower

Bollinger

Foraker

et al. 2017

eGEMs

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Introduction:With the growing use of electronic medical records, electronic health records (EHRs), and personal health records (PHRs) for health care delivery, new opportunities have arisen for population health researchers. Our objective was to characterize PHR users and examine sample representativeness and nonresponse bias in a study of pregnant women recruited via the PHR.Design:Demographic characteristics were examined for PHR users and nonusers. Enrolled study participants (responders, n=187) were then compared with nonresponders and a representative sample of the target population.Results:PHR patient portal users (34 percent of eligible persons) were older and more likely to be White, have private health insurance, and develop gestational diabetes than nonusers. Of eligible persons (all PHR users), 11 percent (187/1,713) completed a self-administered PHR based questionnaire. Participants in the research study were more likely to be non-Hispanic White (90 percent versus 79 percent) and married (85 percent versus 77 percent), and were less likely to be Non-Hispanic Black (3 percent versus 12 percent) or Hispanic (3 percent versus 6 percent). Responders and nonresponders were similar regarding age distribution, employment status, and health insurance status. Demographic characteristics were similar between responders and nonresponders.Discussion:Demographic characteristics of the study population differed from the general population, consistent with patterns seen in traditional population-based studies. The PHR may be an efficient method for recruiting and conducting observational research with additional benefits of efficiency and cost-cost-effectiveness.

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Section: Introductionmentioning

confidence: 99%

“…With the growing use of EHRs and PHRs for health care delivery and quality improvement, new opportunities also have arisen for rapidly identifying, recruiting, and collecting contextual data from patients for population health research. 1 …”

Section: Introductionmentioning

confidence: 99%

Active Use of Electronic Health Records (EHRs) and Personal Health Records (PHRs) for Epidemiologic Research: Sample Representativeness and Nonresponse Bias in a Study of Women During Pregnancy

Bower

Bollinger

Foraker

et al. 2017

eGEMs

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“…As a result, the EHR becomes less of a passive data capture system as is typically the case, and instead, serves as the basis for timely and efficient shared decision-making. Shared decision-making allows for patients' values, needs, and preferences to guide evidence-based care ( Barry and Edgman-Levitan, 2012 ), and our EHR systems should help improve shared decision-making around prevention of chronic disease ( Kite et al, 2014 ). A Cochrane review of interventions for improving the adoption of shared decision making concluded that interventions targeting the provider and patient were more effective than those targeting the provider or patient alone ( Legare et al, 2010 ).…”

Section: Discussionmentioning

confidence: 99%

“…Unlike existing research, our study was done in a primary care setting and targeted all patients meeting age and clinic criteria, regardless of presence of comorbid conditions. Also distinct from other point-of-care interventions, the SPHERE tool is designed to be platform-independent and scalable to other patient populations and healthcare settings, not to mention EHR platforms ( Foraker et al, 2014 , Kite et al, 2014 ).…”

Section: Discussionmentioning

confidence: 99%

Electronic health record-based assessment of cardiovascular health: The stroke prevention in healthcare delivery environments (SPHERE) study

Foraker

Shoben

Kelley

et al. 2016

Preventive Medicine Reports

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< 3% of Americans have ideal cardiovascular health (CVH). The primary care encounter provides a setting in which to conduct patient-provider discussions of CVH. We implemented a CVH risk assessment, visualization, and decision-making tool that automatically populates with electronic health record (EHR) data during the encounter in order to encourage patient-centered CVH discussions among at-risk, yet under-treated, populations. We quantified five of the seven CVH behaviors and factors that were available in The Ohio State University Wexner Medical Center's EHR at baseline (May–July 2013) and compared values to those ascertained at one-year (May–July 2014) among intervention (n = 109) and control (n = 42) patients. The CVH of women in the intervention clinic improved relative to the metrics of body mass index (16% to 21% ideal) and diabetes (62% to 68% ideal), but not for smoking, total cholesterol, or blood pressure. Meanwhile, the CVH of women in the control clinic either held constant or worsened slightly as measured using those same metrics. Providers need easy-to-use tools at the point-of-care to help patients improve CVH. We demonstrated that the EHR could deliver such a tool using an existing American Heart Association framework, and we noted small improvements in CVH in our patient population. Future work is needed to assess how to best harness the potential of such tools in order to have the greatest impact on the CVH of a larger patient population.

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“…Additionally, EHR data can be leveraged as an existing data source to conduct rapid and more efficient investigations into the population burden of CVD and its risk factors. 2 Although easier to obtain, EHR data present unique challenges to epidemiological research. These data are primarily collected for clinical purposes, and repurposing these data for surveillance research creates the potential for bias (defined as a systematic deviation of observed results or inferences from the “truth”) 3 .…”

Section: Introductionmentioning

confidence: 99%

Addressing Bias in Electronic Health Record-based Surveillance of Cardiovascular Disease Risk: Finding the Signal Through the Noise

et al. 2017

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PURPOSE OF REVIEW: Use of the electronic health record (EHR) for CVD surveillance is increasingly common. However, these data can introduce systematic error that influences the internal and external validity of study findings. We reviewed recent literature on EHR-based studies of CVD risk to summarize the most common types of bias that arise. Subsequently, we recommend strategies informed by work from others as well as our own to reduce the impact of these biases in future research. RECENT FINDINGS: Systematic error, or bias, is a concern in all observational research including EHR-based studies of CVD risk surveillance. Patients captured in an EHR system may not be representative of the general population, due to issues such as informed presence bias, perceptions about the healthcare system that influence entry, and access to health services. Further, the EHR may contain inaccurate information or be missing key data points of interest due to loss to follow-up or over-diagnosis bias. Several strategies, including implementation of unique patient identifiers, adoption of standardized rules for inclusion/exclusion criteria, statistical procedures for data harmonization and analysis, and incorporation of patient-reported data have been used to reduce the impact of these biases. SUMMARY: EHR data provide an opportunity to monitor and characterize CVD risk in populations. However, understanding the biases that arise from EHR datasets is instrumental in planning epidemiological studies and interpreting study findings. Strategies to reduce the impact of bias in the context of EHR data can increase the quality and utility of these data.

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Electronic Medical Records and Their Use in Health Promotion and Population Research of Cardiovascular Disease

Cited by 7 publications

References 22 publications

Active Use of Electronic Health Records (EHRs) and Personal Health Records (PHRs) for Epidemiologic Research: Sample Representativeness and Nonresponse Bias in a Study of Women During Pregnancy

Active Use of Electronic Health Records (EHRs) and Personal Health Records (PHRs) for Epidemiologic Research: Sample Representativeness and Nonresponse Bias in a Study of Women During Pregnancy

Electronic health record-based assessment of cardiovascular health: The stroke prevention in healthcare delivery environments (SPHERE) study

Addressing Bias in Electronic Health Record-based Surveillance of Cardiovascular Disease Risk: Finding the Signal Through the Noise

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