OBJECTIVE: To determine if patient gender and race affect decisions about pain management. DESIGN, SETTING, AND PARTICIPANTS:Experimental design using medical vignettes to evaluate treatment decisions. A convenience sample of 111 primary care physicians (61 men, 50 women) in the Northeast was asked to treat 3 hypothetical patients with pain (kidney stone, back pain) or a control condition (sinusitis). Symptom presentation and severity were held constant, but patient gender and race were varied. MEASUREMENTS AND MAIN RESULTS:The maximum permitted doses of narcotic analgesics (hydrocodone) prescribed at initial and return visits were calculated by multiplying mg per pill         number of pills per day         number of days         number of refills. No overall differences with respect to patient gender or race were found in decisions to treat or in the maximum permitted doses. However, for renal colic, male physicians prescribed higher doses of hydrocodone to white versus black patients (426 mg vs 238 mg), while female physicians prescribed higher doses to blacks (335 mg vs 161 mg; F 1,85 = 9.65, P = .003). This pattern was repeated for persistent kidney stone pain. For persistent back pain, male physicians prescribed higher doses of hydrocodone to males versus females (406 mg vs 201 mg), but female physicians prescribed higher doses to females (327 mg vs 163 mg; F 1,28 = 5.50, P = .03).CONCLUSION: When treating pain, gender and racial differences were evident only when the role of physician gender was examined, suggesting that male and female physicians may react differently to gender and/or racial cues.

Health plan administrative and claims data can be used to accurately identify pre-gestational and gestational diabetes and ultrasounds. Obesity is not consistently coded.

Among newly homebound elders, those with significant depressive symptoms are more likely to experience deterioration in function and quality of life over 6 months. However, those with more support showed significant improvement in the SF-36 mental component scale at 6 months.

Among members of five US health plans, MCV4 vaccination was not associated with increased GBS risk.

BackgroundData from randomized controlled trials and observational studies on older adults who take statins for primary prevention of atherosclerotic cardiovascular disease are limited. To determine the incidence of statin use in older adults with and without cardiovascular disease (CVD) and/or diabetes (DM), we conducted a descriptive observational study.MethodsThe cohort consisted of health plan members in the NIH Collaboratory Distributed Research Network aged >75 years who had continuous drug and medical benefits for ≥183 days during the study period, January 1, 2008- March 31, 2018. We defined DM and CVD using diagnosis codes, and identified statins using dispensing data. Statin use was considered incident if a member had no evidence of statin exposure in the claims during the previous 183 days, and the use was considered long-term if statins were supplied for ≥180 days. Incidence rates were reported among members with and without CVD and/or diabetes, and stratified by year, sex, and age group.ResultsAmong 757,569 eligible members, 109,306 older adults initiated statins and 54,624 became long-term users. Health plan members with CVD had the highest incidence of statin use (143.9 initiators per 1,000 member-years for CVD & DM; 114.5 initiators per 1,000 member-years for CVD & No DM). Among health plan members without CVD, those with DM had rates of statin use that were over two times higher than members without DM (76.1 versus 34.5 initiators per 1,000 member-years, respectively). Statin initiation remained steady throughout 2008–2016, was slightly higher in males, and declined with increasing age.ConclusionIncidence of statin use varied by CVD and DM comorbidity, and was lowest among those without CVD. These results highlight the potential clinical equipoise to conduct large pragmatic clinical trials to generate evidence that could be used to inform future blood cholesterol guidelines.

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Abstracts have an enormous impact on the people, processes, and technology throughout KP and other Health Care Organizations. ICD-9 is running out f codes. Hundreds of new diagnosis codes are submitted annually. ICD-10 will allow not only for more codes, but also for greater specificity and thus better epidemiological tracking. How will this change impact data? Where do analysts find the new codes and what process should they follow to get ready for this conversion. What Clarity tables and columns will carry the new codes and how should the mapping be done? This presentation will provide tools for the programmers and guide them to make this conversion less painful. Keywords: High-Level Overview of ICD-10; Difference between ICD-9-CM and ICD-10-CM; Health Methods: A prototype patient SDM tool developed for point of care use is presented to the patient as a companion piece that is congruent with a physician clinical decision support tool called CV Wizard. The patient tool was designed to convey clear, succinct and personalized information about blood pressure, lipids, blood sugar, weight, smoking, and aspirin use. Reversible CV risk associated with each of these risk factors is conveyed using a combination of symbols and text accommodating a range of patient educational and literacy levels. The patient tool was presented to the HealthPartners Patient Council (HPC), the patient education specialist and a number of physician and leadership groups for feedback on content and design. Results: The HPC found the initial version confusing. They wanted more specific information on the values of their current CV risk factors and preferred the more complex tool like the CV Wizard physician tool because of its quantitative detail on reversible CV risk and pharmacologic recommendations. However, they did acknowledge that not every patient would understand that level of detail. They noted that dialogue between the patient and the physician in conjunction with the tool was more important than the tool itself. Others thought the tool was a good start with minor modifications suggested. Conclusion: The HPC preferred more specific CV risk factor values and recommendations than were included on the low literacy, or simple tool we presented. Tools that are tailored or able to accommodate a wide range of educational and literacy levels may be desirable to facilitate provider-patient shared decision making discussions. The version of the patient tool discussed here will be implemented in summer of 2012.

Abstracts biobank total twelve million dollars, representing a several-fold return on investment. Conclusions: The seamless integration of the automated MyCode process with existing clinical infrastructure maximizes the efficiency of research sample collection and minimizes costs. The process allows resources to be focused on interaction with the patients during consent. The opt-in consent allows samples to be linked to EMR data. Conclusions:We designed an ePhenotyping algorithm to identify AAA cases and controls from the EMR with high PPV and sensitivity necessary for research purposes. The VDW provides an excellent opportunity to broaden the study population characteristics and replicate the findings.