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BACKGROUND Electronic health records (EHR) have become commonplace in medicine. A disconnect between developers and users while creating the interface often fails to create a product that captures clinical workflow, and issues become apparent with implementation. Optimization allows collaboration of clinicians and informaticists after implementation, but documentation of success has only been at the institutional level. METHODS A 4-month, department-wide EHR optimization was conducted with information technology (IT). Optimizations were developed from an intensive quality improvement process involving all levels of clinicians and clinical staff. The optimizations were then categorized as accommodations (department adjusted workflow to EHR), creations (IT developed new workflows within EHR), discoveries (department found workflows within EHR), and modifications (IT changed workflows within EHR). Departmental productivity, defined as number of visits, charges, and payments, was standardized to ratios prior to the COVID-19 pandemic and evaluated by Taylor's change point analysis. Significant improvements were defined as shifts (change points), trends (5 or more consecutive values above/below the mean), and values outside 95% CIs. RESULTS The 124 optimizations were categorized as 43 accommodations, 13 creations, 54 discoveries, and 14 modifications. Productivity ratios of monthly charges (0.74 to 1.28) and payments (0.83 to 1.58) significantly improved with the optimization efforts. Monthly visit ratios increased (0.65 to 0.98) but did not change significantly. CONCLUSION Departmental collaboration with organizational IT for EHR optimization focused on detailed analysis of how workflows can impact productivity. Discovery optimization predominance indicates many solutions to EHR usability problems were already in the system. A large proportion of accommodation optimizations reinforced the need for better developer-user collaboration before implementation. Key words: electronic health records; health information management; organizational efficiency; quality improvement, INTRODUCTION In 2009, $27 billion in incentives were generated by the Health Information Technology for Economic and Clinical Health Act for development and implementation of electronic health records (EHRs). (1-8) [...]

Background To decrease hospital readmission rates, clinical practices create a transition of care (TOC) process to assess patients and coordinate care postdischarge. As current evidence suggests lack of universal benefit, this study's objectives are to determine what patient and process factors associate with hospital readmissions, as well as construct a model to decrease 30-day readmissions. Methods Three months of retrospective discharged patient data (n = 123) were analysed for readmission influences including: patient-specific comorbidities, admission-specific diagnoses, and TOC components. A structured intervention of weekly contact, the Care Coordination Cocoon (CCC), was created for multiply readmitted patients (MRPs), defined as ≥2 readmissions. Three months of postintervention data (n = 141) were analysed. Overall readmission rates and patient- and process-specific characteristics were analysed for associations with hospital readmission. Results Standard TOC lacked significance. Patient-specific comorbidities of cancer (odds ratio [OR] 6.27; 95% confidence interval [CI] 1.73–22.75) and coronary artery disease (OR 6.71; 95% CI 1.84–24.46), and admission-specific diagnoses within pulmonary system admissions (OR 7.20; 95% CI 1.96–26.41) were associated with readmissions. Post-CCC data demonstrated a 48-h call (OR 0.21; 95% CI 0.09–0.50), answered calls (OR 0.16; CI 0.07–0.38), 14-day scheduled visit (OR 0.20; 95% CI 0.07–0.54), and visit arrival (OR 0.39; 95% CI 0.17–0.91) independently associated with decreased readmission rate. Patient-specific (hypertension—OR 3.65; CI 1.03–12.87) and admission-specific (nephrologic system—OR 3.22; CI 1.02–10.14) factors associated with readmissions which differed from the initial analysis. Conclusions Targeting a practice's MRPs with CCC resources improves the association of TOC components with readmissions and rates decreased. This is a more efficient use of TOC resources. [ABSTRACT FROM AUTHOR]

Background and Aims Design Setting Participants Measurements Findings Conclusions Disposable e‐cigarette use has increased among United States (US) high school students in recent years. However, there is limited research on the profile of these users, how often they use these products, and whether they displace cigarette smoking. This study aimed to measure how disposable e‐cigarette use among US youth varies according to demographic characteristics and whether there is any association between e‐cigarette use and reduced use of traditional cigarettes.We used cross‐sectional data from the 2022 National Youth Tobacco Survey and conducted a multinomial logistic regression to examine factors associated with the types of e‐cigarette devices used in the prior 30 days, adjusting for sex, sexual identity, grade level and race/ethnicity. We also used a finite mixture model to account for unobserved differences among users and identify e‐cigarette use patterns in different subgroups of users.United States.High school students in grades 9–12 (n = 14 389).Survey participants self‐reported the type of e‐cigarette device used, the frequency of e‐cigarettes used and cigarettes smoked over the past 30 days.Disposable e‐cigarettes were the most popular e‐cigarette type. Sex, sexual orientation, grade level and race/ethnicity were associated with disposable e‐cigarette use. The odds of disposable e‐cigarette use were lower in male students than in female students (odds ratio [OR] = 0.78, 95% confidence interval [CI] = [0.64–0.96]), and higher in students who identified as gay or lesbian (OR = 1.70, 95% CI = [1.11–2.61]) or bisexual (OR = 1.52, 95% CI = [1.16–1.99]) than in heterosexual students. The odds of disposable use were higher among students in higher grades (10th, 11th and 12th) than in 9th graders (OR = 1.71, 2.24 and 2.52, respectively). Disposable e‐cigarette users had a lower frequency of traditional cigarette use than other e‐cigarette users, both in the low‐frequency class (incidence rate ratio [IRR] = 0.33, 95% CI = [0.12–0.92]) and the high‐frequency class (IRR = 0.27, 95% CI = [0.08–0.92]).Disposable e‐cigarette use appears to be higher among United States high school students who are female, older and/or identify as gay, lesbian or bisexual. Disposable e‐cigarettes appear to be associated with reduced traditional cigarette use. [ABSTRACT FROM AUTHOR]