Assessment and improvement of HIV screening rates in a Midwest primary care practice using an electronic clinical decision support system: a quality improvement study

BACKGROUND: Universal human immunodeficiency virus (HIV) screening remains low in many clinical practices despite published guidelines recommending screening for all patients between ages 13–65. Electronic clinical decision support tools have improved screening rates for many chronic diseases. We designed a quality improvement project to improve the rate of universal HIV screening of adult patients in a Midwest primary care practice using a clinical decision support tool. METHODS: We conducted this quality improvement project in Rochester, Minnesota from January 1, 2014 to December 31, 2014. Baseline primary care practice HIV screening data were acquired from January 1, 2014 to April 30, 2014. We surveyed providers and educated them about current CDC recommended screening guidelines. We then added an HIV screening alert to an existing electronic clinical decision support tool and post-intervention HIV screening rates were obtained from May 1, 2014 to December 31, 2014. The primary quality outcome being assessed was change in universal HIV screening rates. RESULTS: Twelve thousand five hundred ninety-six unique patients were eligible for HIV screening in 2014; 327 were screened for HIV. 6,070 and 6,526 patients were seen before and after the intervention, respectively. 1.80 % of eligible patients and 3.34 % of eligible patients were screened prior to and after the intervention, respectively (difference of −1.54 % [−2.1 %, −0.99 %], p < 0.0001); OR 1.89 (1.50, 2.38). Prior to the intervention, African Americans were more likely to have been screened for HIV (OR 3.86 (2.22, 6.71; p < 0.001) than Whites, but this effect decreased significantly after the intervention (OR 1.90 (1.12, 3.21; p = 0.03). CONCLUSIONS: These data showed that an electronic alert almost doubled the rates of universal HIV screening by primary care providers in a Midwestern practice and reduced racial disparities, but there is still substantial room for improvement in universal screening practices. Opportunities for universal HIV screening remain abundant, as many providers either do not understand the importance of screening average risk patients or do not remember to discuss it. Alerts to remind providers of current guidelines and help identify screening opportunities can be helpful. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12911-016-0320-5) contains supplementary material, which is available to authorized users

PM₁₀, PM_2.5, PM₁, number and surface of particles at the child’s seat when smoking a cigarette in a car

The exposure to particles was measured by a diffusion size classifier (10–300 nm) and an optical particle counter (300 nm–10 μm) at the child’s seat of a car during repeated drives on a fixed route from a suburban domestic area to a school and back. One single cigarette smoked in a car at the front seat during a 10 minute trip, lead to an increase of PM10 on the back seat by a factor of 10.5, for PM2.5 by a factor of 21.3 and for PM1 by a factor of 23.9. Concentrations dropped after opening the back door, but stayed elevated on the way back, compared to outdoor concentrations. Holding the cigarettes to the open window of the front seat did not reduce exposure on the back seat. When a second cigarette was smoked on the way back, PM10 concentrations rose again to 300 μg m−3. While background PM1 made up 19–39% of PM10, PM1 during smoking amounted to 78–89% of PM10. PM1 was highly correlated to particle number (mean 97,701 pt cm−3, SD 82,537) and lung deposited surface area (LDSA, mean 270 cm2 cm−3, SD 229). Positioning of the cigarette at the open window did not decrease the exposure to LDSA at the child’s seat. In conclusion, particles can reach exorbitant high levels at the back seat, when cigarettes are smoked in a small place like a car, even with a 2 inches open window next to the smoker at the front seat. Through smoking in cars parents can harm their or other’s children severely

Patient Satisfaction With Providers: Do Patient Surveys Give Enough Information to Help Providers Improve Specific Behaviors

Background: Patient satisfaction surveys ask patients specific questions about provider behavior such as whether they were satisfied with the provider’s instructions about medications or time spent with the patient. It’s unclear how responses to these surveys can help providers focus on specific behaviors to improve. Methods: In a primary care setting, we analyzed Press Ganey patient experience survey responses. We examined the 10 questions dealing with satisfaction specific to the care provider experience. We used the “Top Box” counts (counts of most favorable responses) and Top Box% (percentage of most favorable response) for categorical and continuous measures of patient satisfaction. Results: For 12 consecutive months, 652 providers of 1014 accumulated at least 300 total responses from patients for the 10 provider-related questions. Only 8 of the 652 providers had significant differences ( P &lt; .05) in Top Box% for the 10 questions. Correlation of responses between the questions were between 0.86 and 0.96. Analysis of variance showed that 87% of the total variation in the Top Box% of the 10 questions was between providers and only 13% within providers. Factor analysis found no independent factors within the 10 questions (ie, a one factor model was sufficient; P &lt; .0001). Conclusion: Patient survey questions appear to ask about specific provider behaviors that contribute to patient experience. However, the responses to 10 different questions are highly correlated and may not give providers or management enough statistically significant information to focus patient experience improvement efforts for individual providers. </jats:sec

Association of provider opioid prescribing practices and the Centers for Medicare and Medicaid Services hierarchical condition category score: A retrospective examination of correlation between the volume of provider-prescribed opioid medications and provider panel complexity

Objective: Opioids are being prescribed at increasing rates in primary care practices, and among individual providers there is significant variability in opioid prescribing. Primary care practices also vary significantly in complexity of their patients, ranging from healthy patients to those with multiple comorbidities. Our objective was to examine individual primary care providers for an association between their opioid prescribing and the complexity/risk of their panel of patients (a panel of patients is a group of patients whose medical care is the responsibility of a specific healthcare provider or care team). Methods: We retrospectively examined 12 months of opioid prescription data from a primary care practice. We obtained counts of opioids prescribed by providers in the Mayo Clinic, Rochester, Minnesota primary care practice. For patients paneled (assigned) to family medicine and internal medicine, we used the Centers for Medicare and Medicaid Services hierarchical condition category patient risk score as a measure of patient complexity. After adjusting the opioid counts for panel patient count (to get opioid counts per patient), we used linear regression analysis to determine the correlation between the hierarchical condition category risk and the amount of opioid prescribed by individual providers. Results: Among our combined 103 primary care providers, opioid unit counts prescribed per patient were highly correlated with the providers’ hierarchical condition category panel risk score (r 2  = 0.54). After excluding three outliers, r 2 was 0.74. With and without the outliers, the correlation was very significant (p  0.45 showed significant correlation with hierarchical condition category (r 2  = 0.26; p = 0.001). Conclusion: When examining differences in primary care providers’ opioid prescribing practices, the Centers for Medicare and Medicaid Services endorsed risk score (the hierarchical condition category score) can help adjust for population differences of a provider’s patients

Integration of e-consultations into the outpatient care process at a tertiary medical centre

An e-consultation is an asynchronous consultation performed by a specialist without a face-to-face patient visit. E-consultations have been available to primary care providers at the Mayo Clinic for several years. We reviewed e-consultations performed by specialists at the Mayo Clinic for the first six months of 2013. We included only “internal” e-consultations, originating from within the Rochester practice. During the study period a total of 3242 e-consultations were completed at the Mayo Clinic. After excluding those relating to patients who did not give research consent, 3008 e-consultations remained. We categorized our internal e-consultations into eight types. The most frequently used types were the first e-consultation processes to be implemented: the primary care to specialist e-consultation and the specialist to specialist e-consultation, accounting for 74% of the total. As these two types of e-consultation became widely used, the staff discovered that the e-consultation process could be adapted to meet specific practice needs and six more e-consultation types emerged. For example, intra-specialty e-consultations and surgical e-consultations accounted for 16% of the total. E-consultations appear to have improved access to specialists, and they are integrated into care processes when timely expert opinions are needed. As e-consultations evolve, it will be important to develop a standard, well-defined terminology to compare outcomes of these processes across practices. </jats:p