Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting.
J Am Med Inform Assoc
; 26(12): 1560-1565, 2019 12 01.
Article
en En
| MEDLINE
| ID: mdl-31390471
ABSTRACT
BACKGROUND:
Drug prescription errors are made, worldwide, on a daily basis, resulting in a high burden of morbidity and mortality. Existing rule-based systems for prevention of such errors are unsuccessful and associated with substantial burden of false alerts.OBJECTIVE:
In this prospective study, we evaluated the accuracy, validity, and clinical usefulness of medication error alerts generated by a novel system using outlier detection screening algorithms, used on top of a legacy standard system, in a real-life inpatient setting. MATERIALS ANDMETHODS:
We integrated a novel outlier system into an existing electronic medical record system, in a single medical ward in a tertiary medical center. The system monitored all drug prescriptions written during 16 months. The department's staff assessed all alerts for accuracy, clinical validity, and usefulness. We recorded all physician's real-time responses to alerts generated.RESULTS:
The alert burden generated by the system was low, with alerts generated for 0.4% of all medication orders. Sixty percent of the alerts were flagged after the medication was already dispensed following changes in patients' status which necessitated medication changes (eg, changes in vital signs). Eighty-five percent of the alerts were confirmed clinically valid, and 80% were considered clinically useful. Forty-three percent of the alerts caused changes in subsequent medical orders.CONCLUSION:
A clinical decision support system that used a probabilistic, machine-learning approach based on statistically derived outliers to detect medication errors generated clinically useful alerts. The system had high accuracy, low alert burden and low false-positive rate, and led to changes in subsequent orders.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Sistemas de Apoyo a Decisiones Clínicas
/
Efectos Colaterales y Reacciones Adversas Relacionados con Medicamentos
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Sistemas de Entrada de Órdenes Médicas
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Aprendizaje Automático
/
Errores de Medicación
Tipo de estudio:
Evaluation_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
País/Región como asunto:
Asia
Idioma:
En
Revista:
J Am Med Inform Assoc
Asunto de la revista:
INFORMATICA MEDICA
Año:
2019
Tipo del documento:
Article
País de afiliación:
Israel