Edward F. Ewen, M.D

Director, Clinical Data and Analytics, Center for Strategic Information Management

Publications

Antibiotic prescribing by telephone in primary care
Antibiotic prescribing by telephone in primary care
Ewen E, Willey VJ, Kolm P, McGhan WF, Drees M

Pharmacoepidemiol Drug Saf 2015 Feb;24(2):113-20

PMID: 25079292

Abstract

OBJECTIVES: Little is known about the contribution of telephone-based prescribing on overall antibiotic utilization. The objective of this study was to determine the extent and characteristics of telephone-based antibiotic prescribing in teaching and non-teaching primary care practices.
METHODS: This retrospective cohort study included all patients (n = 114 610) cared for by teaching and non-teaching internal medicine, pediatrics, family practice, and obstetrics/gynecology practices (n = 19) affiliated with a large US healthcare system during 2006-2010 and using a common electronic medical record. Rates and types of antibiotics prescribed by teaching and non-teaching practices via telephone contact and office visit were compared among the overall cohort. All telephone-related prescriptions during 2008 underwent chart review to determine indications for antibiotic prescribing.
RESULTS: Overall, 28.9 antibiotic prescriptions were issued per 100 patient-years, with 63 418 total antibiotic prescriptions and 7876 (12.4%) generated after telephone contact. Telephone-based prescribing increased steadily from 2.2 to 4.2 per 100 patient-years during the study period. Both telephone-based and office-based antibiotic prescribing were higher in non-teaching practices. Of 1790 antibiotics prescribed by telephone during 2008, the majority were for urinary tract infection (28.3%), sinusitis (20.1%), and unspecified upper respiratory infection (URI, 15.0%).
CONCLUSIONS: Overall, one in every eight antibiotics was prescribed via telephone encounter. These data highlight the need to include the impact of this practice in analysis of outcomes associated with outpatient antibiotic prescribing and to incorporate telephonic prescribing into guidelines facilitating appropriate antibiotic use.
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Using the AUDIT-PC to predict alcohol withdrawal in hospitalized patients
Using the AUDIT-PC to predict alcohol withdrawal in hospitalized patients
Pecoraro A, Ewen E, Horton T, Mooney R, Kolm P, McGraw P, Woody G

J Gen Intern Med 2014 Jan;29(1):34-40

PMID: 23959745

Abstract

BACKGROUND: Alcohol withdrawal syndrome (AWS) occurs when alcohol-dependent individuals abruptly reduce or stop drinking. Hospitalized alcohol-dependent patients are at risk. Hospitals need a validated screening tool to assess withdrawal risk, but no validated tools are currently available.
OBJECTIVE: To examine the admission Alcohol Use Disorders Identification Test-(Piccinelli) Consumption (AUDIT-PC) ability to predict the subsequent development of AWS among hospitalized medical-surgical patients admitted to a non-intensive care setting.
DESIGN: Retrospective case–control study of patients discharged from the hospital with a diagnosis of AWS. All patients with AWS were classified as presenting with AWS or developing AWS later during admission. Patients admitted to an intensive care setting and those missing AUDIT-PC scores were excluded from analysis. A hierarchical (by hospital unit) logistic regression was performed and receiver-operating characteristics were examined on those developing AWS after admission and randomly selected controls. Because those diagnosing AWS were not blinded to the AUDIT-PC scores, a sensitivity analysis was performed.
PARTICIPANTS: The study cohort included all patients age ≥18 years admitted to any medical or surgical units in a single health care system from 6 October 2009 to 7 October 2010.
KEY RESULTS: After exclusions, 414 patients were identified with AWS. The 223 (53.9 %) who developed AWS after admission were compared to 466 randomly selected controls without AWS. An AUDIT-PC score ≥4 at admission provides 91.0 % sensitivity and 89.7 % specificity (AUC=0.95; 95 % CI, 0.94–0.97) for AWS, and maximizes the correct classification while resulting in 17 false positives for every true positive identified. Performance remained excellent on sensitivity analysis (AUC=0.92; 95 % CI, 0.90–0.93). Increasing AUDIT-PC scores were associated with an increased risk of AWS (OR=1.68, 95 % CI 1.55–1.82, p<0.001).
CONCLUSIONS: The admission AUDIT-PC score is an excellent discriminator of AWS and could be an important component of future clinical prediction rules. Calibration and further validation on a large prospectivecohort is indicated.
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