OBJECTIVES: Emergency physicians unaware of patients' insurance status may prescribe expensive proprietary antibiotics for patients who cannot afford them. The objective of this study was to develop a clinical decision support system to display patient insurance status before prescription writing for outpatient conditions. METHODS: This was a 26-week "before-and-after" trial at an urban emergency department (ED) with 78,000 visits/year treating a medically underserved population. Sixty-one prescribers, including attending physicians, residents, and physician assistants, participated. All patients older than 18 years of age discharged from the ED receiving antibiotic prescriptions were eligible. The electronic ED chart is linked to prescription-writing software, which includes a menu of 74 antibiotics. The system was programmed so that when an emergency physician accessed the prescription menu, a prompt appeared displaying insurance status. Prescribers also received educational interventions. The main outcome measure was the percentage of prescribers who reduced their prescription writing of proprietary antibiotics to self-pay patients. Data were analyzed with cluster techniques using SPSS 10.0 (SPSS Inc., Chicago, IL). RESULTS: Of 594 prescriptions written for self-pay patients before prompt insertion, 158 (26.6%) were for proprietary antibiotics. After the intervention, self-pay patients received 564 antibiotic prescriptions, of which 117 (20.7%) were for proprietary drugs. Analyzed by prescriber, the reduction in the prescription rate for proprietary antibiotics was statistically significant (p = 0.03, chi(2) test). Patients with respiratory or urinary infections also had a statistically significant reduction in proprietary antibiotic prescription (p = 0.03). CONCLUSIONS: A clinical decision support system, integrated into a prescription-writing program, can decrease the prescription of proprietary antibiotics for self-pay patients in the ED.
OBJECTIVES: Emergency physicians unaware of patients' insurance status may prescribe expensive proprietary antibiotics for patients who cannot afford them. The objective of this study was to develop a clinical decision support system to display patient insurance status before prescription writing for outpatient conditions. METHODS: This was a 26-week "before-and-after" trial at an urban emergency department (ED) with 78,000 visits/year treating a medically underserved population. Sixty-one prescribers, including attending physicians, residents, and physician assistants, participated. All patients older than 18 years of age discharged from the ED receiving antibiotic prescriptions were eligible. The electronic ED chart is linked to prescription-writing software, which includes a menu of 74 antibiotics. The system was programmed so that when an emergency physician accessed the prescription menu, a prompt appeared displaying insurance status. Prescribers also received educational interventions. The main outcome measure was the percentage of prescribers who reduced their prescription writing of proprietary antibiotics to self-pay patients. Data were analyzed with cluster techniques using SPSS 10.0 (SPSS Inc., Chicago, IL). RESULTS: Of 594 prescriptions written for self-pay patients before prompt insertion, 158 (26.6%) were for proprietary antibiotics. After the intervention, self-pay patients received 564 antibiotic prescriptions, of which 117 (20.7%) were for proprietary drugs. Analyzed by prescriber, the reduction in the prescription rate for proprietary antibiotics was statistically significant (p = 0.03, chi(2) test). Patients with respiratory or urinary infections also had a statistically significant reduction in proprietary antibiotic prescription (p = 0.03). CONCLUSIONS: A clinical decision support system, integrated into a prescription-writing program, can decrease the prescription of proprietary antibiotics for self-pay patients in the ED.
Authors: Brian W Patterson; Michael S Pulia; Shashank Ravi; Peter L T Hoonakker; Ann Schoofs Hundt; Douglas Wiegmann; Emily J Wirkus; Stephen Johnson; Pascale Carayon Journal: Ann Emerg Med Date: 2019-01-03 Impact factor: 5.721
Authors: Jonathan Austrian; Felicia Mendoza; Adam Szerencsy; Lucille Fenelon; Leora I Horwitz; Simon Jones; Masha Kuznetsova; Devin M Mann Journal: J Med Internet Res Date: 2021-04-09 Impact factor: 5.428
Authors: Mika Lehto; Kaisu Pitkälä; Ossi Rahkonen; Merja K Laine; Marko Raina; Timo Kauppila Journal: Scand J Prim Health Care Date: 2021-04-14 Impact factor: 2.581