RESUMO
To facilitate healthcare quality improvement initiatives, we previously developed an algorithm to identify diabetes mellitus (DM) patients using only electronically available administrative data. In this study, we have validated our prediction model, screening over 28,000 admissions and determining factors associated with false positive assignment. These factors will be incorporated into a revised algorithm.
Assuntos
Algoritmos , Diabetes Mellitus , Adulto , Hospitalização , Humanos , Computação Matemática , Admissão do Paciente , Estudos ProspectivosRESUMO
Using a previously described computer algorithm to prospectively identify diabetics, we observed the adherence to published guidelines for treatment of dyslipidemia in diabetics. Despite national guidelines, dyslipidemia remains widely under-treated. Further interventions are needed to improve lipid-lowering treatment in appropriate candidates.
Assuntos
Complicações do Diabetes/tratamento farmacológico , Dislipidemias/tratamento farmacológico , Fidelidade a Diretrizes , Hipolipemiantes/uso terapêutico , Algoritmos , Diabetes Mellitus/diagnóstico , Feminino , Hospitalização , Humanos , Masculino , Pessoa de Meia-Idade , Guias de Prática Clínica como AssuntoRESUMO
Commercial rule bases can be implemented to identify medication orders that fall outside recommended dosage ranges, but they are likely to produce an excessive number of nuisance and clinically insignificant alerts. Strategies for customizing commercial dosing rules can be implemented to minimize this problem. This paper describes specific strategies implemented in a dose checking application necessary for achieving a clinically acceptable alert rate.
Assuntos
Quimioterapia Assistida por Computador , Sistemas de Medicação no Hospital , Preparações Farmacêuticas/administração & dosagem , Sistemas de Alerta , Sistemas de Informação em Farmácia Clínica , Creatinina/metabolismo , Sistemas de Apoio a Decisões Clínicas , Hospitais Comunitários , Hospitais Universitários , Humanos , Erros de Medicação/prevenção & controle , Estudos RetrospectivosRESUMO
Using an automated method to prospectively identify diabetic patients, we measured the impact of an administrative policy to perform LDL-cholesterol (LDL-c) testing on all diabetics not having the test performed within a specified time period. Automatic testing resulted in significant increases in LDL-c testing rate, and identified a greater proportion of patients who were candidates for statins. Further interventions are needed to increase prescriptions for lipid-lowering therapy.
