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Using machine learning to examine drivers of inappropriate outpatient antibiotic prescribing in acute respiratory illnesses.
King, Laura M; Kusnetsov, Michael; Filippoupolitis, Avgoustinos; Arik, Deniz; Bartoces, Monina; Roberts, Rebecca M; Tsay, Sharon V; Kabbani, Sarah; Bizune, Destani; Rathore, Anirudh Singh; Valkova, Silvia; Eleftherohorinou, Hariklia; Hicks, Lauri A.
Afiliación
  • King LM; Division of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, United States.
  • Kusnetsov M; IQVIA, London, United Kingdom.
  • Filippoupolitis A; IQVIA, London, United Kingdom.
  • Arik D; IQVIA, London, United Kingdom.
  • Bartoces M; Division of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, United States.
  • Roberts RM; Division of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, United States.
  • Tsay SV; Division of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, United States.
  • Kabbani S; Division of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, United States.
  • Bizune D; Division of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, United States.
  • Rathore AS; IQVIA, London, United Kingdom.
  • Valkova S; IQVIA, Plymouth Meeting, Pennsylvania, United States.
  • Eleftherohorinou H; IQVIA, London, United Kingdom.
  • Hicks LA; Division of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, United States.
Infect Control Hosp Epidemiol ; 44(5): 786-790, 2023 05.
Article en En | MEDLINE | ID: mdl-35001867
ABSTRACT
Using a machine-learning model, we examined drivers of antibiotic prescribing for antibiotic-inappropriate acute respiratory illnesses in a large US claims data set. Antibiotics were prescribed in 11% of the 42 million visits in our sample. The model identified outpatient setting type, patient age mix, and state as top drivers of prescribing.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Infecciones del Sistema Respiratorio / Antibacterianos Límite: Humans Idioma: En Revista: Infect Control Hosp Epidemiol Asunto de la revista: DOENCAS TRANSMISSIVEIS / ENFERMAGEM / EPIDEMIOLOGIA / HOSPITAIS Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Infecciones del Sistema Respiratorio / Antibacterianos Límite: Humans Idioma: En Revista: Infect Control Hosp Epidemiol Asunto de la revista: DOENCAS TRANSMISSIVEIS / ENFERMAGEM / EPIDEMIOLOGIA / HOSPITAIS Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos