Chronic stress in practice assistants: An analytic approach comparing four machine learning classifiers with a standard logistic regression model.

BackgroundOccupational stress is associated with adverse outcomes for medical professionals and patients. In our cross-sectional study with 136 general practices, 26.4% of 550 practice assistants showed high chronic stress. As machine learning strategies offer the opportunity to improve understanding of chronic stress by exploiting complex interactions between variables, we used data from our previous study to derive the best analytic model for chronic stress: four common machine learning (ML) approaches are compared to a classical statistical procedure.MethodsWe applied four machine learning classifiers (random forest, support vector machine, K-nearest neighbors', and artificial neural network) and logistic regression as standard approach to analyze factors contributing to chronic stress in practice assistants. Chronic stress had been measured by the standardized, self-administered TICS-SSCS questionnaire. The performance of these models was compared in terms of predictive accuracy based on the 'operating area under the curve' (AUC), sensitivity, and positive predictive value.FindingsCompared to the standard logistic regression model (AUC 0.636, 95% CI 0.490-0.674), all machine learning models improved prediction: random forest +20.8% (AUC 0.844, 95% CI 0.684-0.843), artificial neural network +12.4% (AUC 0.760, 95% CI 0.605-0.777), support vector machine +15.1% (AUC 0.787, 95% CI 0.634-0.802), and K-nearest neighbours +7.1% (AUC 0.707, 95% CI 0.556-0.735). As best prediction model, random forest showed a sensitivity of 99% and a positive predictive value of 79%. Using the variable frequencies at the decision nodes of the random forest model, the following five work characteristics influence chronic stress: too much work, high demand to concentrate, time pressure, complicated tasks, and insufficient support by practice leaders.ConclusionsRegarding chronic stress prediction, machine learning classifiers, especially random forest, provided more accurate prediction compared to classical logistic regression. Interventions to reduce chronic stress in practice personnel should primarily address the identified workplace characteristics

Prevalence of chronic stress in general practitioners and practice assistants: Personal, practice and regional characteristics - Fig 2

<p><b>2a. Practice characteristics</b>. Prevalence ratios for practices with low strain due to chronic stress (≤16.6) versus practices with high strain due to chronic stress (>16.6) by practice characteristics. <b>2b. Regional characteristics</b>. Prevalence ratios for practices with low strain due to chronic stress (≤16.6) versus practices with high strain due to chronic stress (>16.6) by regional characteristics.</p

Characteristics of study participants (n = 764).

<p>Characteristics of study participants (n = 764).</p

Vaccination Management and Vaccination Errors: A Representative Online-Survey among Primary Care Physicians

<div><p>Background</p><p>Effective immunizations require a thorough, multi-step process, yet few studies comprehensively addressed issues around vaccination management.</p><p>Objectives</p><p>To assess variations in vaccination management and vaccination errors in primary care.</p><p>Methods</p><p>A cross sectional, web-based questionnaire survey was performed among 1157 primary physicians from North Rhine-Westphalia, Germany: a representative 10% random sample of general practitioners (n = 946) and all teaching physicians from the University Duisburg-Essen (n = 211). Four quality aspects with three items each were included: patient-related quality (patient information, patient consent, strategies to increase immunization rates), vaccine-related quality (practice vaccine spectrum, vaccine pre-selection, vaccination documentation), personnel-related quality (recommendation of vaccinations, vaccine application, personnel qualification) and storage-related quality (storage device, temperature log, vaccine storage control). For each of the four quality aspects, “good quality” was reached if all three criteria per quality aspect were fulfilled. Good vaccination management was defined as fulfilling all twelve items. Additionally, physicians’ experiences with errors and nearby-errors in vaccination management were obtained.</p><p>Results</p><p>More than 20% of the physicians participated in the survey. Good vaccination management was reached by 19% of the practices. Patient-related quality was good in 69% of the practices, vaccine-related quality in 73%, personnel-related quality in 59% and storage-related quality in 41% of the practices. No predictors for error reporting and good vaccination management were identified.</p><p>Conclusions</p><p>We identified good results for vaccine- and patient-related quality but need to improve issues that revolve around vaccine storage.</p></div

Additional characteristics of vaccination management.

<p>*Multiple response.</p

Frequencies of errors and near-errors in vaccination management^*.

<p><b><i>*</i></b>The items offered were based on reports in a German primary care incidents reporting system.</p