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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]

Introduction. Recommendations for colorectal cancer screening encourage patients to choose among various screening methods based on individual preferences for benefits, risks, screening frequency, and discomfort. We devised a model to illustrate how individuals with varying tolerance for screening complications risk might decide on their preferred screening strategy. Methods. We developed a discrete-time Markov mathematical model that allowed hypothetical individuals to maximize expected lifetime utility by selecting screening method, start age, stop age, and frequency. Individuals could choose from stool-based testing every 1 to 3 years, flexible sigmoidoscopy every 1 to 20 years with annual stool-based testing, colonoscopy every 1 to 20 years, or no screening. We compared the life expectancy gained from the chosen strategy with the life expectancy available from a benchmark strategy of decennial colonoscopy. Results. For an individual at average risk of colorectal cancer who was risk neutral with respect to screening complications (and therefore was willing to undergo screening if it would actuarially increase life expectancy), the model predicted that he or she would choose colonoscopy every 10 years, from age 53 to 73 years, consistent with national guidelines. For a similar individual who was moderately averse to screening complications risk (and therefore required a greater increase in life expectancy to accept potential risks of colonoscopy), the model predicted that he or she would prefer flexible sigmoidoscopy every 12 years with annual stool-based testing, with 93% of the life expectancy benefit of decennial colonoscopy. For an individual with higher risk aversion, the model predicted that he or she would prefer 2 lifetime flexible sigmoidoscopies, 20 years apart, with 70% of the life expectancy benefit of decennial colonoscopy. Conclusion. Mathematical models may formalize how individuals with different risk attitudes choose between various guideline-recommended colorectal cancer screening strategies. [ABSTRACT FROM AUTHOR]

Background: Understanding resource utilization patterns among high-cost patients may inform cost reduction strategies.Objective: To identify patterns of high-cost healthcare utilization and associated clinical diagnoses and to quantify the significance of hot-spotters among high-cost users.Design: Retrospective analysis of high-cost patients in 2012 using data from electronic medical records, internal cost accounting, and the Centers for Medicare and Medicaid Services. K-medoids cluster analysis was performed on utilization measures of the highest-cost decile of patients. Clusters were compared using clinical diagnoses. We defined "hot-spotters" as those in the highest-cost decile with ≥4 hospitalizations or ED visits during the study period.Participants and Exposure: A total of 14,855 Medicare Fee-for-service beneficiaries identified by the Medicare Quality Resource and Use Report as having received 100 % of inpatient care and ≥90 % of primary care services at Cleveland Clinic Health System (CCHS) in Northeast Ohio. The highest-cost decile was selected from this population.Main Measures: Healthcare utilization and diagnoses.Key Results: The highest-cost decile of patients (n = 1486) accounted for 60 % of total costs. We identified five patient clusters: "Ambulatory," with 0 admissions; "Surgical," with a median of 2 surgeries; "Critically Ill," with a median of 4 ICU days; "Frequent Care," with a median of 2 admissions, 3 ED visits, and 29 outpatient visits; and "Mixed Utilization," with 1 median admission and 1 ED visit. Cancer diagnoses were prevalent in the Ambulatory group, care complications in the Surgical group, cardiac diseases in the Critically Ill group, and psychiatric disorders in the Frequent Care group. Most hot-spotters (55 %) were in the "frequent care" cluster. Overall, hot-spotters represented 9 % of the high-cost population and accounted for 19 % of their overall costs.Conclusions: High-cost patients are heterogeneous; most are not so-called "hot-spotters" with frequent admissions. Effective interventions to reduce costs will require a more multi-faceted approach to the high-cost population. [ABSTRACT FROM AUTHOR]

Background. Older adults have the highest influenza-related morbidity and mortality risk, but the influenza vaccine is less effective in the elderly. It is unknown whether influenza vaccination of nonelderly adults confers additional disease protection on the elderly population. Methods. We examined the association between county-wide influenza vaccination coverage among 520 229 younger adults (aged 18-64 years) in the Behavioral Risk Factors Surveillance System Survey and illnesses related to influenza in 3 317 709 elderly Medicare beneficiaries aged ≥65 years, between 2002 and 2010 (13 267 786 person-years). Results were stratified by documented receipt of a seasonal influenza vaccine in each Medicare beneficiary. Results. Increases in county-wide vaccine coverage among younger adults were associated with lower adjusted odds of illnesses related to influenza in the elderly. Compared with elderly residents of counties with ≤15% of younger adults vaccinated, the adjusted odds ratio for a principal diagnosis of influenza among elderly residents was 0.91 (95% confidence interval, .88-.94) for counties with 16%-20% of younger adults vaccinated, 0.87 (.84-.90) for counties with 21%-25% vaccinated, 0.80 (.77-.83) for counties with 26%-30% vaccinated, and 0.79 (.76-.83) for counties with ≥31% vaccinated (P for trend <.001). Stronger associations were observed among vaccinated elderly adults, in peak months of influenza season, in more severe influenza seasons, in influenza seasons with greater antigenic match to influenza vaccine, and for more specific definitions of influenza-related illness. Conclusions. In a large, nationwide sample of Medicare beneficiaries, influenza vaccination among adults aged 18-64 years was inversely associated with illnesses related to influenza in the elderly. [ABSTRACT FROM AUTHOR]

Background: Understanding resource utilization patterns among high-cost patients may inform cost reduction strategies., Objective: To identify patterns of high-cost healthcare utilization and associated clinical diagnoses and to quantify the significance of hot-spotters among high-cost users., Design: Retrospective analysis of high-cost patients in 2012 using data from electronic medical records, internal cost accounting, and the Centers for Medicare and Medicaid Services. K-medoids cluster analysis was performed on utilization measures of the highest-cost decile of patients. Clusters were compared using clinical diagnoses. We defined "hot-spotters" as those in the highest-cost decile with ≥4 hospitalizations or ED visits during the study period., Participants and Exposure: A total of 14,855 Medicare Fee-for-service beneficiaries identified by the Medicare Quality Resource and Use Report as having received 100 % of inpatient care and ≥90 % of primary care services at Cleveland Clinic Health System (CCHS) in Northeast Ohio. The highest-cost decile was selected from this population., Main Measures: Healthcare utilization and diagnoses., Key Results: The highest-cost decile of patients (n = 1486) accounted for 60 % of total costs. We identified five patient clusters: "Ambulatory," with 0 admissions; "Surgical," with a median of 2 surgeries; "Critically Ill," with a median of 4 ICU days; "Frequent Care," with a median of 2 admissions, 3 ED visits, and 29 outpatient visits; and "Mixed Utilization," with 1 median admission and 1 ED visit. Cancer diagnoses were prevalent in the Ambulatory group, care complications in the Surgical group, cardiac diseases in the Critically Ill group, and psychiatric disorders in the Frequent Care group. Most hot-spotters (55 %) were in the "frequent care" cluster. Overall, hot-spotters represented 9 % of the high-cost population and accounted for 19 % of their overall costs., Conclusions: High-cost patients are heterogeneous; most are not so-called "hot-spotters" with frequent admissions. Effective interventions to reduce costs will require a more multi-faceted approach to the high-cost population., Competing Interests: Compliance with Ethical Standards Funders This study was conducted without funding. Prior Presentations Presented at the 38th Annual Society of General Internal Medicine National Meeting, Toronto, Canada, April 22, 2015. Conflicts of Interest There are no conflicts of interest to disclose.

Objective: Despite influenza vaccination being an integral part of prenatal care, vaccination rates remain low. To evaluate the impact of pre-visit video education on patients' vaccination health beliefs and vaccination rate., Study Design: From November 2013-January 2014 unvaccinated patients seen for routine prenatal carewere randomized into 2 study groups: pre-visit vaccination video education or control. Pre- and post-video health beliefs were assessed on a 5-point scale, and unvaccinated participants were subsequently interviewed by phone., Results: In 105 randomized participants, intervention positively influenced health beliefs, as demonstrated by differences in mean pre- versus post-video scores for intervention versus control: vaccination may harm mother (difference = -0.05, p = 0.009) and baby (difference = -0.44, p = 0.015), and vaccination can protect mother (difference = 0.49, p = 0.003) and baby (difference = 0.59, p = 0.001). Vaccination rates were 28% intervention and 25% control (p = 0.70). Provider recommendation was associated with vaccination (47% if recommended vs. 12% if not, p < 0.001). Phone interviews revealed susceptibility, to influenza and vaccine safety as primary reasons for remaining unvaccinated., Conclusion: Video education positively influenced vaccination health beliefs without impacting vaccination rates. Physician's recommendation was strongly associated with participant's decision to become vaccinated and may be most effective when emphasizing influenza vaccination's protective impact on the newborn,.

Objective: Black and Hispanic individuals synthesize less vitamin D per unit of sun exposure than white individuals. The relationship between UV radiation and vitamin D insufficiency in minorities has not been well explored., Design: Prospective cohort study., Setting: Using the National Health and Nutrition Examination Survey, we obtained serum vitamin D levels for non-Hispanic Whites, Hispanics and non-Hispanic Blacks aged ≥18 years from 2000-2006. We linked these data with the average monthly solar UV index by census tract and data on sun exposure, vitamin D supplementation, health and demographics. We used multivariable regression analyses to assess vitamin D deficiency (<15 ng/ml) and insufficiency (<20 ng/ml) in January (when the UV index was lowest) by race/ethnicity and geography., Subjects: Adults (n 14,319) aged ≥18 years., Results: A 1-point increase in the UV index was associated with a 0·51 ng/ml increase in vitamin D (95% CI 0·35, 0·67 ng/ml; P<0·001). Non-Hispanic Black race and Hispanic ethnicity were associated with a 7·47 and 3·41 ng/ml decrease in vitamin D, respectively (both P<0·001). In January, an estimated 65·4% of non-Hispanic Blacks were deficient in vitamin D, compared with 28·9% of Hispanics and 14·0% of non-Hispanic Whites. An estimated 84·2% of non-Hispanic Blacks were insufficient in vitamin D v. 56·3% of Hispanics and 34·8% of non-Hispanic Whites. More non-Hispanic Blacks were estimated to be deficient in vitamin D in January in the highest UV index quartile than were non-Hispanic Whites in the lowest UV index quartile (60·2% v. 25·7%)., Conclusions: Wintertime vitamin D insufficiency is pervasive among minority populations, and not uncommon among non-Hispanic Whites.