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Determinants of antibiotic prescribing for upper respiratory tract infections in an emergency department with good primary care access: a qualitative analysis.

ABSTRACT: Upper respiratory tract infections (URTIs) account for substantial attendances at emergency departments (EDs). There is a need to elucidate determinants of antibiotic prescribing in time-strapped EDs - popular choices for primary care despite highly accessible primary care clinics. Semi-structured in-depth interviews were conducted with purposively sampled physicians (n = 9) in an adult ED in Singapore. All interviews were analysed using thematic analysis and further interpreted using the Social Ecological Model to explain prescribing determinants. Themes included: (1) reliance on clinical knowledge and judgement, (2) patient-related factors, (3) patient-physician relationship factors, (4) perceived practice norms, (5) policies and treatment guidelines and (6) patient education and awareness. The physicians relied strongly on their clinical knowledge and judgement in managing URTI cases and seldom interfered with their peers' clinical decisions. Despite departmental norms of not prescribing antibiotics for URTIs, physicians would prescribe antibiotics when faced with uncertainty in patients' diagnoses, treating immunocompromised or older patients with comorbidities, and for patients demanding antibiotics, especially under time constraints. Participants had a preference for antibiotic prescribing guidelines based on local epidemiology, but viewed hospital policies on prescribing as a hindrance to clinical judgement. Participants highlighted the need for more public education and awareness on the appropriate use of antibiotics and management of URTIs. Organisational practice norms strongly influenced antibiotic prescribing decisions by physicians, who can be swayed by time pressures and patient demands. Clinical decision support tools, hospital guidelines and patient education targeting at individual, interpersonal and community levels could reduce unnecessary antibiotic use.

SUBMITTER: Chan YY

PROVIDER: S-EPMC6518493 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Determinants of antibiotic prescribing for upper respiratory tract infections in an emergency department with good primary care access: a qualitative analysis.

Chan Y Y YY Bin Ibrahim M A MA Wong C M CM Ooi C K CK Chow A A

Epidemiology and infection 20190101

Upper respiratory tract infections (URTIs) account for substantial attendances at emergency departments (EDs). There is a need to elucidate determinants of antibiotic prescribing in time-strapped EDs - popular choices for primary care despite highly accessible primary care clinics. Semi-structured in-depth interviews were conducted with purposively sampled physicians (n = 9) in an adult ED in Singapore. All interviews were analysed using thematic analysis and further interpreted using the Social ...[more]

PMID: 30868987

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Project description:IntroductionNational guidelines suggest that most skin abscesses do not require antibiotics, and that cellulitis antibiotics should target streptococci, not community-associated MRSA (CA-MRSA). The objective of this study is to describe antimicrobial treatment of skin infections in U.S. emergency departments (EDs) and analyze potential quality measures.MethodsThe National Hospital Ambulatory Medical Care Survey (NHAMCS) is a 4-stage probability sample of all non-federal U.S. ED visits. In 2007 NHAMCS started recording whether incision and drainage was performed at ED visits. We conducted a retrospective analysis, pooling 2007-2010 data, identified skin infections using diagnostic codes, and identified abscesses by performance of incision and drainage. We generated national estimates and 95% confidence intervals using weighted analyses; quantified frequencies and proportions; and evaluated antibiotic prescribing practices. We evaluated 4 parameters that might serve as quality measures of antibiotic stewardship, and present 2 of them as potentially robust enough for implementation.ResultsOf all ED visits, 3.2% (95% confidence interval 3.1-3.4%) were for skin infection, and 2.7% (2.6-2.9%) were first visits for skin infection, with no increase over time (p=0.80). However, anti-CA-MRSA antibiotic use increased, from 61% (56-66%) to 74% (71-78%) of antibiotic regimens (p<0.001). Twenty-two percent of visits were for abscess, with a non-significant increase (p=0.06). Potential quality measures: Among discharged abscess patients, 87% were prescribed antibiotics (84-90%, overuse). Among antibiotic regimens for abscess patients, 84% included anti-CA-MRSA agents (81-89%, underuse).ConclusionFrom 2007-2010, use of anti-CA-MRSA agents for skin infections increased significantly, despite stable visit frequencies. Antibiotics were over-used for discharged abscess cases, and CA-MRSA-active antibiotics were underused among regimens when antibiotics were used for abscess. [West J Emerg Med. 2014;15(3):282-289.].

Project description:ObjectivesInappropriate antibiotic use for respiratory tract infection (RTI) is an ongoing problem linked to the emergence of drug resistance and other adverse effects. Less is known about the prescribing practices of individual physicians or the impact of physician prescribing habits on patient outcomes. We studied the prescribing practices of providers for acute RTIs in an integrated health system, identified patient factors associated with receipt of an antibiotic and assessed the relation between providers' adjusted prescribing rates and a number of patient outcomes.MethodsThis was a retrospective analysis of adults with an RTI visit to any primary care providers across the Cleveland Clinic Health System in 2011-2012. Patients with a history of chronic obstructive pulmonary disease or immunocompromised status were excluded. Logistic regression was used to examine patient factors associated with receipt of an antibiotic.ResultsOf 31,416 patients with an RTI, 54.8% received an antibiotic. Patient factors associated with antibiotic prescribing included white race (odds ratio [OR] 1.35, P < 0.001), presence of fever (OR 1.66, P < 0.001), and a diagnosis of bronchitis (OR 10.98, P < 0.001) or sinusitis (OR 33.85, P < 0.001). Among 290 providers with ≥10 RTI visits, adjusted antibiotic prescribing rates ranged from 0% to 100% (mean 49%). Antibiotics were prescribed more often for sinusitis (OR 33.85, P < 0.001), bronchitis (OR 10.98, P < 0.001), or pharyngitis (OR 1.76, P < 0.001) compared with upper respiratory tract infection. Patients who were prescribed antibiotics at the index visit were more likely to return for RTI within 1 year (adjusted OR 1.26, P < 0.001). Emergency department visits for respiratory complications were rare and not associated with antibiotic receipt.ConclusionsAntibiotic prescribing for RTI varies widely among physicians and cannot be explained by patient factors. Patients prescribed antibiotics for RTI were more likely to return for RTI.

Project description:BackgroundUrinary tract infection (UTI) is a common emergency department (ED) diagnosis with reported high diagnostic error rates. Because a urine culture, part of the gold standard for diagnosis of UTI, is usually not available for 24-48 hours after an ED visit, diagnosis and treatment decisions are based on symptoms, physical findings, and other laboratory results, potentially leading to overutilization, antibiotic resistance, and delayed treatment. Previous research has demonstrated inadequate diagnostic performance for both individual laboratory tests and prediction tools.ObjectiveOur aim, was to train, validate, and compare machine-learning based predictive models for UTI in a large diverse set of ED patients.MethodsSingle-center, multi-site, retrospective cohort analysis of 80,387 adult ED visits with urine culture results and UTI symptoms. We developed models for UTI prediction with six machine learning algorithms using demographic information, vitals, laboratory results, medications, past medical history, chief complaint, and structured historical and physical exam findings. Models were developed with both the full set of 211 variables and a reduced set of 10 variables. UTI predictions were compared between models and to proxies of provider judgment (documentation of UTI diagnosis and antibiotic administration).ResultsThe machine learning models had an area under the curve ranging from 0.826-0.904, with extreme gradient boosting (XGBoost) the top performing algorithm for both full and reduced models. The XGBoost full and reduced models demonstrated greatly improved specificity when compared to the provider judgment proxy of UTI diagnosis OR antibiotic administration with specificity differences of 33.3 (31.3-34.3) and 29.6 (28.5-30.6), while also demonstrating superior sensitivity when compared to documentation of UTI diagnosis with sensitivity differences of 38.7 (38.1-39.4) and 33.2 (32.5-33.9). In the admission and discharge cohorts using the full XGboost model, approximately 1 in 4 patients (4109/15855) would be re-categorized from a false positive to a true negative and approximately 1 in 11 patients (1372/15855) would be re-categorized from a false negative to a true positive.ConclusionThe best performing machine learning algorithm, XGBoost, accurately diagnosed positive urine culture results, and outperformed previously developed models in the literature and several proxies for provider judgment. Future prospective validation is warranted.

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Determinants of antibiotic prescribing for upper respiratory tract infections in an emergency department with good primary care access: a qualitative analysis.

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Determinants of antibiotic prescribing for upper respiratory tract infections in an emergency department with good primary care access: a qualitative analysis.

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